molbook_leaderboad.csv

paper_id,year,title,authors,primary_area,decision,venue,scores,avg_score,presentations,avg_presentation,soundnesses,avg_soundness,contributions,avg_contribution,confidences,avg_confidence,keywords,citations_serper,frontend_paper_id,submission_date,normalized_citations
iclr_zuuhtmK1Ub,2025,Differentiable Implicit Solver on Graph Neural Networks for Forward and Inverse Problems,"Nikolay Yavich, Alexander Ryabov, Evgeny Burnaev, Vladimir Vanovskiy","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[3, 1, 3, 1]",2.0,"[1, 1, 1, 1]",1.0,"[1, 2, 1, 2]",1.5,"[2, 1, 2, 1]",1.5,"[3, 3, 4, 3]",3.25,"[""Graph Neural Networks"", ""Differentiable solvers"", ""Implicit schemes"", ""Numerical modelling"", ""Inverse problems""]",0,0acd2b4e-bfb3-4dff-9237-8ff525d4dacb,2024-09-27,0.0
iclr_zuKrRYM3Tg,2025,Quantized Approximately Orthogonal Recurrent Neural Networks,"Armand Foucault, Francois Malgouyres, Franck Mamalet","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[3, 3, 5, 1]",3.0,"[3, 2, 2, 4]",2.75,"[2, 2, 2, 3]",2.25,"[1, 2, 2, 1]",1.5,"[4, 3, 3, 5]",3.75,"[""ecurrent neural networks"", ""neural network quantization"", ""orthogonal recurrent neural networks"", ""quantization bitwidth""]",2,bb734b19-ae84-4867-a59f-04800d183221,2024-09-25,0.1053
iclr_znGnmAM44K,2025,The other you in black mirror: first steps from chatbots to personalized LLM clones,"Mingzhong Sun, Mengmi Zhang, Gabriel Kreiman","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[3, 5, 6, 6]",5.0,"[2, 3, 2, 3]",2.5,"[2, 2, 2, 3]",2.25,"[2, 3, 2, 2]",2.25,"[4, 4, 4, 4]",4.0,"[""Large Language Models (LLMs)"", ""Personalized AI"", ""Turing Test"", ""AI Safety""]",1,9db7bd97-3c34-4334-94ac-3099e846e34b,2024-09-27,0.0526
iclr_zkNCWtw2fd,2025,"Synergistic Approach for Simultaneous Optimization of Monolingual, Cross-lingual, and Multilingual Information Retrieval","Adel Elmahdy, Sheng-Chieh Lin, Amin Ahmad","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[3, 3, 3]",3.0,"[3, 2, 2]",2.33,"[2, 2, 3]",2.33,"[2, 2, 1]",1.67,"[4, 3, 4]",3.67,"[""Information Retrieval"", ""Multilingualism and Cross-Lingual NLP"", ""Question Answering""]",2,4d6738dc-6b30-488d-b5cc-901ae8a88289,2024-09-28,0.1053
iclr_zi8YBcmXqA,2025,PokeChamp: an Expert-level Minimax Language Agent for Competitive Pokemon,"Seth Karten, Andy Luu Nguyen, Chi Jin","foundation or frontier models, including LLMs",reject,Rejected,"[6, 6, 5, 3]",5.0,"[3, 3, 2, 2]",2.5,"[3, 3, 2, 2]",2.5,"[3, 3, 2, 1]",2.25,"[4, 3, 3, 4]",3.5,"[""multiagent"", ""LLM agents"", ""competitive games"", ""game theory"", ""reinforcement learning""]",1,daa70490-7c53-4951-a317-85555ab3bba4,2024-09-28,0.0526
iclr_zeBhcfP8tN,2025,Trust but Verify: Programmatic VLM Evaluation in the Wild,"Viraj Uday Prabhu, Senthil Purushwalkam, An Yan, Caiming Xiong, Ran Xu",datasets and benchmarks,reject,Rejected,"[5, 5, 5, 5]",5.0,"[2, 2, 3, 2]",2.25,"[2, 2, 3, 2]",2.25,"[2, 3, 2, 2]",2.25,"[4, 4, 4, 4]",4.0,"[""vision-language models"", ""evaluation"", ""hallucinations""]",5,aa756086-db5f-4bba-9a10-eb996596ae85,2024-09-13,0.2632
iclr_zPxlHOLxmh,2025,From Counseling Transcript to Mind Map: Leveraging LLMs for Effective Summarization in Mental Health Counseling,"Julian Hao Yong, Mei Kuan Lim, Chun Yong Chong","foundation or frontier models, including LLMs",reject,Rejected,"[3, 1, 1, 3]",2.0,"[3, 2, 3, 2]",2.5,"[1, 2, 2, 2]",1.75,"[2, 1, 1, 2]",1.5,"[4, 4, 5, 4]",4.25,"[""Large Language Models"", ""Visual-based Summarization"", ""Mental Health Counseling""]",0,a91f38d2-f622-4e05-a8f8-3728297759eb,2024-09-27,0.0
iclr_zG2vcC1l1f,2025,KEA: Keeping Exploration Alive by Proactively Coordinating Exploration Strategies in Curiosity-driven Exploration,"Shih-Min Yang, Martin Magnusson, Johannes A. Stork, Todor Stoyanov",reinforcement learning,reject,Rejected,"[5, 5, 5, 5]",5.0,"[3, 3, 2, 2]",2.5,"[2, 2, 2, 3]",2.25,"[2, 2, 1, 2]",1.75,"[4, 4, 4, 4]",4.0,"[""Reinforcement Learning"", ""Curiosity-based Exploration"", ""Sparse Reward"", ""Soft Actor-Critic""]",1,dce9a866-019a-42be-a186-089db8dec313,2024-09-27,0.0526
iclr_z9UABOHCZc,2025,GeoTimeCLIP: Unveiling the When and Where of Images,"David G Shatwell, Ishan Rajendrakumar Dave, Swetha Sirnam, Mubarak Shah","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[3, 5, 6, 6]",5.0,"[3, 3, 3, 4]",3.25,"[3, 3, 3, 4]",3.25,"[1, 3, 3, 4]",2.75,"[4, 5, 4, 5]",4.5,"[""time prediction"", ""geolocalization"", ""contrastive learning"", ""metric learning""]",0,bef8cada-bfcc-491a-a7fd-09d440a142a4,2024-09-24,0.0
iclr_z2VBHpRT14,2025,SpaceSet: A Large-scale Realistic Space-based Image Dataset for Space Situational Awareness,"Rangya Zhang, Jiaping Xiao, Yuhang Zhang, Lu Bai, Qianlei Jia, Mir Feroskhan",datasets and benchmarks,reject,Rejected,"[6, 5, 5, 10]",6.5,"[3, 2, 3, 4]",3.0,"[3, 2, 3, 4]",3.0,"[3, 2, 2, 4]",2.75,"[2, 4, 3, 1]",2.5,"[""space situational awareness"", ""object detection and tracking"", ""space image dataset"", ""high resolution image""]",0,450a5faf-8362-4717-a952-71c96b50f173,2024-09-27,0.0
iclr_z2QdVmhtAP,2025,Efficient Multi Subject Visual Reconstruction from fMRI Using Aligned Representations,"Christos Zangos, Danish Ebadulla, Thomas Christopher Sprague, Ambuj Singh",applications to neuroscience & cognitive science,reject,Rejected,"[3, 3, 3]",3.0,"[2, 2, 3]",2.33,"[1, 1, 3]",1.67,"[2, 1, 3]",2.0,"[4, 5, 4]",4.33,"[""fMRI"", ""Computational Neuroscience"", ""Neuroimaging"", ""Diffusion"", ""CLIP"", ""alignment"", ""neuroAI""]",0,b5a3352d-893a-47b2-b7cf-d99a67b23f68,2024-09-28,0.0
iclr_yuymgwkjj1,2025,Correcting the Bias of  Normalizing Flows by Synthetic Outliers for Improving Out-of-Distribution Detection,"Yuzhong Zhao, Qiaoqiao Ding, Xiaoqun Zhang","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[5, 5, 5, 5]",5.0,"[2, 2, 3, 3]",2.5,"[2, 3, 3, 3]",2.75,"[2, 2, 2, 2]",2.0,"[4, 3, 4, 5]",4.0,"[""OOD Detection"", ""Normalizing Flow""]",0,12764945-2e96-4500-89ee-8e86ad5ef5d3,2024-09-18,0.0
iclr_yt7nxONs3J,2025,Prioritize Alignment in Dataset Distillation,"Zekai Li, Ziyao Guo, Wangbo Zhao, Tianle Zhang, Samir Khaki, Ahmad Sajedi, Zhi-Qi Cheng, Kaipeng Zhang, Konstantinos N Plataniotis, Kai Wang, Yang You","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[3, 6, 5, 6]",5.0,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 3]",3.0,"[2, 2, 2, 3]",2.25,"[4, 5, 4, 4]",4.25,"[""dataset distillation""]",9,0bcba59f-5bad-4f15-9035-7982023a4cdf,2024-09-25,0.4737
iclr_ys16t9FcLN,2025,Distribution-Dependent Rates for Multi-Distribution Learning,"Rafael Hanashiro, Patrick Jaillet",learning theory,reject,Rejected,"[3, 6, 6, 5]",5.0,"[2, 2, 3, 4]",2.75,"[2, 3, 3, 3]",2.75,"[3, 3, 3, 2]",2.75,"[3, 2, 2, 4]",2.75,"[""multi-distribution learning"", ""distributionally robust optimization"", ""pure exploration multi-armed bandits""]",2,9099632f-215c-41e0-b439-4d005d3a6f97,2024-09-27,0.1053
iclr_ySRsm6HDy5,2025,Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning,"Laixi Shi, Jingchu Gai, Eric Mazumdar, Yuejie Chi, Adam Wierman",learning theory,reject,Rejected,"[5, 5, 5, 5]",5.0,"[3, 3, 2, 2]",2.5,"[2, 3, 3, 2]",2.5,"[2, 2, 2, 2]",2.0,"[3, 4, 4, 3]",3.5,"[""Multi-agent reinforcement learning"", ""Robust Markov games"", ""Game theory"", ""Distribution shift""]",4,5594979c-b152-4300-a867-8f6367ffabb5,2024-09-25,0.2105
iclr_yIRtu2FJvY,2025,A Matrix Variational Auto-Encoder for Variant Effect Prediction in Pharmacogenes,"Antoine Honore, Borja Rodríguez Gálvez, Yitian Zhou, Yoomi Park, Volker M. Lauschke, MingX","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[3, 3, 3, 3]",3.0,"[4, 1, 1, 2]",2.0,"[3, 1, 2, 3]",2.25,"[2, 1, 1, 1]",1.25,"[4, 3, 4, 4]",3.75,"[""variant effect prediction"", ""variational auto-encoder"", ""transformer"", ""deep learning""]",0,95196d96-632b-4e4e-b858-6501888eeb0d,2024-09-20,0.0
iclr_yIN4yDCcmo,2025,INS-MMBench: A Comprehensive Benchmark for Evaluating LVLMs' Performance in Insurance,"Chenwei Lin, Hanjia Lyu, Xian Xu, Jiebo Luo",datasets and benchmarks,reject,Rejected,"[5, 3, 6, 6]",5.0,"[3, 2, 3, 3]",2.75,"[3, 1, 3, 3]",2.5,"[3, 1, 2, 3]",2.25,"[5, 4, 3, 3]",3.75,"[""large vision-language model"", ""insurance"", ""multimodal""]",5,e6b4bc7e-0eca-4c6c-9078-32820b605a0f,2024-09-24,0.2632
iclr_yDy9fZXNJV,2025,The Graph's Apprentice: Teaching an LLM Low-Level Knowledge for Circuit Quality Estimation,"Reza Moravej, Saurabh Bodhe, Zhanguang Zhang, Didier Chételat, Dimitrios Tsaras, Yingxue Zhang, Hui-Ling Zhen, Jianye HAO, Mingxuan Yuan","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[3, 6, 6, 5]",5.0,"[2, 3, 3, 4]",3.0,"[2, 3, 3, 3]",2.75,"[2, 3, 3, 3]",2.75,"[4, 2, 4, 5]",3.75,"[""LLM"", ""Knowledge Distillation"", ""Verilog"", ""Graph Neural Network""]",5,4ba5ca69-d5af-4fc0-a201-7f71bd8ceee6,2024-09-27,0.2632
iclr_yAN2oPHs7y,2025,Neuro-Symbolic Rule Lists,"Sascha Xu, Nils Philipp Walter, Jilles Vreeken",interpretability and explainable AI,reject,Rejected,"[6, 5, 3, 6]",5.0,"[3, 4, 3, 3]",3.25,"[3, 3, 2, 3]",2.75,"[2, 3, 2, 3]",2.5,"[4, 4, 5, 4]",4.25,"[""Neuro-Symbolic;Rule Induction; Intepretability""]",2,86bc2264-cbc5-4d3c-8452-71fb8d6d22aa,2024-09-27,0.1053
iclr_y9e1tcWlme,2025,Tackling Decision Processes with Non-Cumulative Objectives using Reinforcement Learning,"Maximilian Nägele, Jan Olle, Thomas Fösel, Remmy Zen, Florian Marquardt",reinforcement learning,reject,Rejected,"[5, 3, 6, 6]",5.0,"[3, 2, 3, 4]",3.0,"[3, 2, 2, 2]",2.25,"[2, 2, 2, 3]",2.25,"[4, 3, 4, 3]",3.5,"[""reinforcement learning"", ""markov decision processes"", ""discrete optimization""]",2,b6a6dd33-33b2-4cdb-be47-4da7a04f8622,2024-09-26,0.1053
iclr_y2ch7iQSJu,2025,Budget-constrained Active Learning to De-censor Survival Data,"Ali Parsaee, Bei Jiang, Russell Greiner",learning theory,reject,Rejected,"[1, 1, 3, 3]",2.0,"[2, 1, 3, 2]",2.0,"[2, 2, 2, 1]",1.75,"[2, 2, 4, 2]",2.5,"[4, 4, 2, 4]",3.5,"[""Active Learning"", ""Survival Analysis"", ""Budgeted Constraints"", ""Bayesian Model"", ""Mutual Information"", ""De-censoring Data""]",0,358620cd-943a-41e8-824e-f6c12297bcfb,2024-09-28,0.0
iclr_xcHIiZr3DT,2025,Vision-Based Pseudo-Tactile Information Extraction and Localization for Dexterous Grasping,"Teng Yan, Cai Yaobang, Tian Xia, Jianhao, Wenxian Li","applications to robotics, autonomy, planning",reject,Rejected,"[3, 1, 3, 3]",2.5,"[2, 1, 2, 2]",1.75,"[1, 1, 2, 2]",1.5,"[1, 1, 1, 1]",1.0,"[4, 4, 3, 3]",3.5,"[""Pseudo-Tactile Information"", ""Dexterous Grasping"", ""Vision-Based Perception"", ""Robotic Localization""]",0,3f82e83d-5033-4706-bbc1-b9d49d908ec5,2024-09-28,0.0
iclr_xajif1l65R,2025,Rethinking Dataset Quantization: Efficient Core Set Selection via Semantically-Aware Data Augmentation,"Yangze Liu, Hong Liu","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[5, 5, 5, 5]",5.0,"[3, 2, 2, 3]",2.5,"[2, 2, 2, 3]",2.25,"[2, 2, 3, 2]",2.25,"[4, 4, 5, 4]",4.25,"[""Coreset Selection"", ""Dataset Quantization"", ""Data Augmentation"", ""Efficient Deep Learning"", ""Semantically-Aware Augmentation""]",0,8137cf3a-c593-4fa7-8c7a-c391323c19d1,2024-09-28,0.0
iclr_xW4J2QlqRx,2025,Context Matters: Leveraging Contextual Features for Time Series Forecasting,"Sameep Chattopadhyay, Pulkit Paliwal, Sai Shankar Narasimhan, Shubhankar Agarwal, Sandeep P. Chinchali",learning on time series and dynamical systems,reject,Rejected,"[5, 5, 5, 5]",5.0,"[2, 3, 3, 2]",2.5,"[3, 3, 3, 2]",2.75,"[1, 1, 2, 2]",1.5,"[4, 5, 3, 4]",4.0,"[""Time series forecasting"", ""Contextual features"", ""Predictive modeling""]",18,8667a569-0cd4-4903-a832-fc4ebe8ee6b1,2024-09-26,0.9474
iclr_xVw8YNEtH3,2025,Reset Method based on the Theory of Manifold Optimization on Real Manifolds,"Weiping Liu, Jiajun Wang, He Li, Youfa Liu, Jingui Zou",optimization,reject,Rejected,"[1, 5, 3]",3.0,"[1, 2, 1]",1.33,"[1, 3, 2]",2.0,"[1, 2, 2]",1.67,"[4, 3, 5]",4.0,"[""Manifold Optimization"", ""Real Manifolds"", ""Method"", ""Deep Learning.""]",0,c003da6c-0042-4bc9-a0c5-fe51da2122b5,2024-09-16,0.0
iclr_xTrAA3UKPa,2025,SWGA: A Distributed Hyperparameter Search Method for Time Series Prediction Models,"Weijian Li, Haozheng Luo, Chenwei Xu, Han Liu",learning on time series and dynamical systems,reject,Rejected,"[3, 3, 1, 1]",2.0,"[2, 2, 1, 2]",1.75,"[2, 2, 1, 1]",1.5,"[1, 1, 1, 1]",1.0,"[4, 4, 4, 3]",3.75,"[""Machine Learning"", ""Deep Learning"", ""Time Series Prediction"", ""Hyperparameter Search"", ""Genetic Algorithms""]",0,aa855cff-86d1-4275-99a7-2a1574c185ba,2024-09-27,0.0
iclr_xS4XOS4NQ5,2025,General Preference Modeling with Preference Representations for Aligning Language Models,"Yifan Zhang, Ge Zhang, Yue Wu, Kangping Xu, Quanquan Gu","foundation or frontier models, including LLMs",reject,Rejected,"[5, 3, 6, 6]",5.0,"[3, 3, 2, 3]",2.75,"[2, 2, 2, 3]",2.25,"[2, 2, 3, 3]",2.5,"[3, 4, 3, 3]",3.25,"[""preference modeling"", ""preference optimization"", ""reinforcement learning from human feedback""]",14,b92fc163-928d-494d-bccf-444f04daf86a,2024-09-27,0.7368
iclr_xLPakPOKDX,2025,Causally Motivated Diffusion Sampling Frameworks for Harnessing Contextual Bias,"Wonwoong Cho, Raymond A. Yeh, David I. Inouye",generative models,reject,Rejected,"[6, 6, 3, 5]",5.0,"[3, 2, 2, 2]",2.25,"[3, 3, 2, 3]",2.75,"[3, 2, 2, 2]",2.25,"[3, 4, 5, 4]",4.0,"[""Causal Inference"", ""Diffusion Models"", ""Contextual bias"", ""Spurious Correlations"", ""Object Cooccurrence"", ""StableDiffusion""]",0,08cfe6f9-ff39-4303-bc60-109fa713d621,2024-09-18,0.0
iclr_xH53mFbwK8,2025,Future Events as Backdoor Triggers: Investigating Temporal Vulnerability in LLMs,"Sara Price, Arjun Panickssery, Samuel R. Bowman, Asa Cooper Stickland","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[3, 8, 6, 3]",5.0,"[3, 2, 3, 3]",2.75,"[3, 3, 3, 2]",2.75,"[2, 4, 3, 2]",2.75,"[3, 3, 3, 4]",3.25,"[""Alignment"", ""Fairness"", ""Safety"", ""and Privacy"", ""Generative Models"", ""Interpretation of learned representations""]",16,1701517b-86cb-43df-93d5-9151288de701,2024-09-23,0.8421
iclr_x07rHuChwF,2025,Euclid: Supercharging Multimodal LLMs with Synthetic High-Fidelity Visual Descriptions,"Jiarui Zhang, Ollie Liu, Tianyu Yu, Jinyi Hu, Willie Neiswanger","foundation or frontier models, including LLMs",reject,Rejected,"[6, 6, 5, 3]",5.0,"[3, 3, 1, 3]",2.5,"[2, 3, 2, 3]",2.5,"[3, 2, 2, 3]",2.5,"[4, 4, 3, 3]",3.5,"[""Multimodal LLMs"", ""Geometric Perception"", ""Low-level Visual Perception""]",11,d211573a-37f0-4f3f-ad29-f179047c48be,2024-09-19,0.5789
iclr_wl4c9jvcyY,2025,AutoGUI: Scaling GUI Grounding with Automatic Functionality Annotations from LLMs,"Hongxin Li, Jingran Su, Jingfan CHEN, Yuntao Chen, Qing Li, Zhaoxiang Zhang",datasets and benchmarks,reject,Rejected,"[6, 8, 3, 3]",5.0,"[3, 3, 3, 3]",3.0,"[3, 3, 1, 2]",2.25,"[3, 3, 2, 2]",2.5,"[3, 4, 5, 4]",4.0,"[""Vision language model"", ""Large language model"", ""Embodied AI"", ""GUI understanding"", ""Web agent""]",4,3b8b07cc-b3c5-43f8-918e-c511b1b7cc01,2024-09-13,0.2105
iclr_wTm4W39GdD,2025,Emergence of Hierarchical Emotion Representations in Large Language Models,"Bo Zhao, Maya Okawa, Eric J Bigelow, Rose Yu, Tomer Ullman, Hidenori Tanaka","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[6, 5, 3, 6]",5.0,"[4, 2, 3, 3]",3.0,"[3, 2, 2, 4]",2.75,"[2, 2, 3, 4]",2.75,"[3, 2, 4, 3]",3.0,"[""LLM"", ""emotion""]",7,cff2ca2e-dd80-45d6-806f-f5caf07f348f,2024-09-27,0.3684
iclr_wT1aFmsXOc,2025,Understanding and Mitigating Memorization in Diffusion Models for Tabular Data,"Zhengyu Fang, Zhimeng Jiang, Huiyuan Chen, Xiao Li, Jing Li",generative models,reject,Rejected,"[6, 6, 3, 5]",5.0,"[3, 2, 3, 4]",3.0,"[3, 2, 1, 3]",2.25,"[3, 2, 1, 2]",2.0,"[3, 3, 5, 4]",3.75,"[""Memorization"", ""Tabular Data"", ""Diffusion Models""]",6,e4fc67f1-10ca-4f76-8921-3e3bf2875ae9,2024-09-17,0.3158
iclr_wQk6yaRGOi,2025,Improving Discrete Diffusion with Schedule-Conditioning,"Alan Nawzad Amin, Nate Gruver, Andrew Gordon Wilson",generative models,reject,Rejected,"[8, 6, 6, 6]",6.5,"[3, 2, 2, 2]",2.25,"[3, 3, 3, 2]",2.75,"[3, 3, 2, 2]",2.5,"[4, 2, 1, 4]",2.75,"[""discrete diffusion"", ""image generation"", ""language model""]",0,133c860d-0c5c-43e2-90bd-65a8f2749e05,2024-09-27,0.0
iclr_wJVZkUOUjh,2025,EXAGREE: Towards Explanation Agreement in Explainable Machine Learning,"Sichao Li, Quanling Deng, Amanda S Barnard",interpretability and explainable AI,reject,Rejected,"[3, 1, 3, 1]",2.0,"[1, 2, 1, 1]",1.25,"[2, 1, 3, 1]",1.75,"[2, 1, 3, 1]",1.75,"[3, 4, 4, 4]",3.75,"[""Explainable Machine Learning"", ""Explainable Artificial Intelligence"", ""Rashomon Sets""]",2,ec159f4d-0377-40f9-810f-e737ace5ed28,2024-09-15,0.1053
iclr_wE5xp3zBaQ,2025,"The Good, the Bad and the Ugly: Watermarks, Transferable Attacks and Adversarial Defenses","Grzegorz Gluch, Berkant Turan, Sai Ganesh Nagarajan, Sebastian Pokutta","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[3, 5, 6, 6]",5.0,"[2, 3, 2, 3]",2.5,"[1, 3, 2, 3]",2.25,"[2, 2, 3, 3]",2.5,"[3, 2, 4, 4]",3.25,"[""Watermarks"", ""Adversarial Defenses"", ""Transferable Attacks"", ""Interactive Proof Systems"", ""Cryptography"", ""Backdooring"", ""Game Theory"", ""Learning Theory""]",2,7e6d350d-ee49-4c3f-8002-17245c18c44a,2024-09-28,0.1053
iclr_wCwz1F8qY8,2025,Prediction of Protein-protein Contacts with Structure-aware Single-sequence Protein Language Models,"Derek Huang, Peicong Lin, Sheng-You Huang","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[3, 3, 8, 6]",5.0,"[2, 2, 2, 3]",2.25,"[2, 2, 3, 3]",2.5,"[2, 1, 3, 3]",2.25,"[3, 5, 4, 3]",3.75,"[""Protein bioinformatics"", ""Protein language models"", ""Protein-protein contact prediction"", ""Protein representations"", ""Deep neural networks""]",1,3d9fa6b9-57d8-4e08-9432-f72567f14b9b,2024-09-27,0.0526
iclr_w7vn6ah0Qg,2025,KokerNet: Koopman Kernel Network for Time Series Forecasting,"Yanfang Xue, Hui Xue, Jinyue Tian, Shipeng Zhu, Pengfei Fang, Meimei Yang","other topics in machine learning (i.e., none of the above)",reject,Rejected,"[5, 6, 3, 6]",5.0,"[2, 2, 3, 4]",2.75,"[2, 3, 3, 3]",2.75,"[2, 3, 2, 3]",2.5,"[4, 4, 4, 4]",4.0,"[""Spectral kernel"", ""Koopman operator"", ""Time series""]",0,1dda6cae-080d-4825-a12f-1e7e9b7fae44,2024-09-26,0.0
iclr_w5pErXbwQl,2025,Noise-Robust Preference Losses for Deep Regression Models,"Ziyi Chen, Jeremy Karp, Aditya Rajagopal, Lavanya Marla","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[3, 3, 3]",3.0,"[3, 2, 3]",2.67,"[3, 2, 2]",2.33,"[2, 2, 1]",1.67,"[4, 3, 4]",3.67,"[""Regression"", ""Robustness"", ""Alignment""]",0,7af13e27-f3e4-465d-9be4-57537acbf4a5,2024-09-26,0.0
iclr_w5h443GIGo,2025,On the Convergence of Symbolic Pattern Forests and Silhouette Coefficients for Robust Time Series Clustering,Nishat Raihan,"unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[3, 1, 3]",2.33,"[1, 2, 2]",1.67,"[2, 1, 2]",1.67,"[2, 1, 2]",1.67,"[4, 4, 3]",3.67,"[""Data Mining"", ""Time Series"", ""Clustering""]",0,4e65a38e-bacc-4488-ac97-0ea681ed0a33,2024-09-14,0.0
iclr_w2HL7yuWE2,2025,Uncertainty-aware Guided Diffusion for Missing Data in Sequential Recommendation,"Wenyu Mao, Zhengyi Yang, Jiancan Wu, Haozhe Liu, Yancheng Yuan, Xiang Wang, Xiangnan He",generative models,reject,Rejected,"[6, 5, 10, 5]",6.5,"[3, 2, 4, 3]",3.0,"[3, 2, 4, 3]",3.0,"[2, 2, 4, 2]",2.5,"[2, 4, 1, 3]",2.5,"[""Diffusion Models"", ""Recommender Systems"", ""Missing Data""]",0,22a2fbd9-287d-40ad-92ac-2db15a01334a,2024-09-26,0.0
iclr_w2C7gJqaai,2025,Integrated Multi-system Prediction via Equilibrium State Evaluation,"Beinan Xu, Andy Song, Jiti Gao",learning on time series and dynamical systems,reject,Rejected,"[1, 5, 1]",2.33,"[1, 3, 1]",1.67,"[1, 2, 1]",1.33,"[1, 2, 1]",1.33,"[1, 4, 5]",3.33,"[""Multi-system"", ""Equilibrium"", ""Prediction""]",0,1dd073a3-f852-4d81-8fe6-e81b28214efb,2024-09-27,0.0
iclr_vxWDoD8oz7,2025,Distortion-free and GPU-compatible Tree Embeddings in Hyperbolic Space,"Max van Spengler, Pascal Mettes",learning on graphs and other geometries & topologies,reject,Rejected,"[8, 6, 8, 6, 6]",6.8,"[3, 2, 3, 2, 3]",2.6,"[4, 2, 3, 3, 3]",3.0,"[3, 2, 3, 3, 4]",3.0,"[2, 5, 5, 1, 3]",3.2,"[""Hyperbolic Geometry"", ""Hyperbolic Tree Embeddings"", ""Representation Learning"", ""Hierarchical Learning""]",1,18378f84-11da-41cd-98eb-aa0bd7cdec98,2024-09-25,0.0526
iclr_vw0NurJ7UX,2025,PrefixQuant: Static Quantization Beats Dynamic through Prefixed Outliers in LLMs,"Mengzhao Chen, Yi Liu, Jiahao Wang, Yi Bin, Wenqi Shao, Ping Luo","foundation or frontier models, including LLMs",reject,Rejected,"[3, 3, 3]",3.0,"[2, 3, 1]",2.0,"[1, 3, 2]",2.0,"[1, 1, 3]",1.67,"[4, 4, 3]",3.67,"[""Large language model; Token-wise outliers; Static quantization;""]",19,6f3bdb96-1647-4dc0-9df9-f8353d30bbe9,2024-09-19,1.0
iclr_voYshhbWeJ,2025,EndoAssistant: A Large-scale Vision-Language Dataset for Endoscopic Surgery Understanding from Open-Source Videos,"Xuan Gong, Balu Harshavardan Koduru, Yuanhao Zhai, Shun Liu, Nan Xi, Xi Tang, Yuan Zhang, Tenzin Lhakpa, Yunjie Tian, Yuxuan Sun, Tianyu Luan, Ziqing Xue, Junsong Yuan, David Doermann",datasets and benchmarks,reject,Rejected,"[6, 3, 5, 6]",5.0,"[2, 3, 3, 4]",3.0,"[2, 3, 2, 3]",2.5,"[2, 2, 3, 3]",2.5,"[5, 5, 4, 4]",4.5,"[""Medical image"", ""endoscopy"", ""vision-language model""]",0,f97e5474-392f-4801-8c9e-f7fdf9b0c6fe,2024-09-26,0.0
iclr_vgvnfUho7X,2025,Beyond accuracy: understanding the performance of LLMs on exams designed for humans,"Pedro Calais, Gabriel Franco, Themistoklis Nikas, Zilu Tang, Mark Crovella, Wagner Meira Jr., Evimaria Terzi",datasets and benchmarks,reject,Rejected,"[3, 3, 3]",3.0,"[3, 3, 2]",2.67,"[1, 2, 2]",1.67,"[1, 1, 2]",1.33,"[5, 4, 4]",4.33,"[""large language models"", ""model evaluation"", ""psychometrics""]",1,9116f64d-3bb7-43bb-bbc6-44668497a8d3,2024-09-28,0.0526
iclr_vgV4y086FY,2025,Differentially Private Bilevel Optimization,Guy Kornowski,optimization,reject,Rejected,"[6, 5, 8, 8]",6.75,"[3, 3, 4, 4]",3.5,"[3, 3, 4, 3]",3.25,"[3, 2, 3, 3]",2.75,"[2, 3, 4, 3]",3.0,"[""Bilevel optimization"", ""differential privacy"", ""nonconvex optimization"", ""first-order methods""]",0,710e56b2-9ece-492b-a4ea-aac21f82f027,2024-09-14,0.0
iclr_vgMAtJONKX,2025,Towards Accurate Validation in Deep Clustering through Unified Embedding Learning,"Zeya Wang, Chenglong Ye","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[5, 5, 5, 5]",5.0,"[2, 3, 3, 3]",2.75,"[2, 3, 2, 3]",2.5,"[2, 3, 3, 2]",2.5,"[4, 4, 5, 4]",4.25,"[""Internal validation measures"", ""Deep clustering"", ""Clustering evaluation"", ""Unified embedding learning""]",0,4539f8c3-69ba-4dda-a396-0207b83347ad,2024-09-23,0.0
iclr_veiSkPqIXm,2025,OpenPL: Realistic Evaluation of Prompt Learning for VLM in Open Environments,"Zi-Kang Wang, Song-Lin Lv, Hao-Zhe Tan, Zhi Zhou, Yu-Feng Li, Lan-Zhe Guo",datasets and benchmarks,reject,Rejected,"[5, 6, 3, 6]",5.0,"[3, 3, 3, 3]",3.0,"[2, 3, 1, 3]",2.25,"[2, 3, 1, 3]",2.25,"[4, 5, 5, 4]",4.5,"[""VLM; Prompt Learning; Open environments""]",0,84bc8a17-cd8e-4147-9d75-bf08ae81d6ab,2024-09-28,0.0
iclr_vc1i3a4O99,2025,Interpreting and Steering LLM Representations with Mutual Information-based Explanations on Sparse Autoencoders,"Xuansheng Wu, Jiayi Yuan, Wenlin Yao, Xiaoming Zhai, Ninghao Liu",interpretability and explainable AI,reject,Rejected,"[6, 6, 3, 5]",5.0,"[4, 3, 3, 3]",3.25,"[3, 3, 1, 2]",2.25,"[3, 3, 2, 2]",2.5,"[4, 4, 4, 4]",4.0,"[""large language models"", ""sparse autoencoders"", ""usable xai"", ""explanations"", ""interpretability""]",1,9ee90312-0c5a-4306-a0f4-1bef38c30d86,2024-09-20,0.0526
iclr_vQ1y086Kn2,2025,UnrealCV Zoo: Enriching Photo-realistic Virtual Worlds for Embodied AI Agents,"Fangwei Zhong, Kui Wu, Churan Wang, Hao Chen, Hai Ci, Zhoujun Li, Yizhou Wang",datasets and benchmarks,reject,Rejected,"[6, 5, 3, 6]",5.0,"[3, 3, 2, 3]",2.75,"[3, 3, 2, 2]",2.5,"[3, 2, 3, 2]",2.5,"[4, 4, 4, 2]",3.5,"[""Virtual worlds; Embodied AI; Embodied Tracking and Navigation; Visual RL;""]",0,5e7847b2-3cdb-46a9-921e-fad4ed8f4e0b,2024-09-26,0.0
iclr_vKL1i2p5Xr,2025,Text as Any-Modality for Zero-shot Classification by Consistent Prompt Tuning,"Xiangyu Wu, Feng Yu, Yang Yang, Jianfeng Lu","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[5, 5, 5, 5]",5.0,"[2, 2, 2, 3]",2.25,"[2, 3, 1, 3]",2.25,"[2, 3, 2, 3]",2.5,"[3, 4, 3, 4]",3.5,"[""Multimodal Learning ; Prompt Learning; Zero-shot Classification;""]",3,244291e8-54b9-4657-b230-310bf59b1fad,2024-09-27,0.1579
iclr_vKJ8YH0iNp,2025,MGD$^3$: Mode-Guided Dataset Distillation using Diffusion Models,"Jeffrey A Chan Santiago, praveen tirupattur, Gaurav Kumar Nayak, Gaowen Liu, Mubarak Shah",generative models,reject,Rejected,"[3, 3, 6, 8]",5.0,"[2, 2, 3, 3]",2.5,"[2, 2, 2, 3]",2.25,"[1, 2, 3, 3]",2.25,"[4, 4, 4, 4]",4.0,"[""Dataset Distillation; Dataset Condensation; Diffusion;""]",15,600b46d1-7ee5-4263-b05b-f1f0f02d134d,2024-09-26,0.7895
iclr_vHO9mU87dc,2025,ShadowKV: KV Cache in Shadows for High-Throughput Long-Context LLM Inference,"Hanshi Sun, Li-Wen Chang, Wenlei Bao, Size Zheng, Ningxin Zheng, Harry Dong, Yuejie Chi, Beidi Chen","foundation or frontier models, including LLMs",reject,Rejected,"[8, 6, 5, 8]",6.75,"[3, 3, 1, 3]",2.5,"[3, 2, 2, 3]",2.5,"[3, 3, 3, 3]",3.0,"[4, 3, 4, 3]",3.5,"[""Long-Context LLM Inference"", ""KV Cache Optimization""]",28,e4fe2128-21db-44a4-b873-18e7dd351857,2024-09-21,1.4737
iclr_vG9dVXwXQV,2025,Pre-Trained Vision-Language Model Selection and Reuse for Downstream Tasks,"Hao-Zhe Tan, Zhi Zhou, Lan-Zhe Guo, Yu-Feng Li","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[5, 6, 8]",6.33,"[2, 3, 3]",2.67,"[1, 3, 3]",2.33,"[2, 3, 3]",2.67,"[4, 5, 4]",4.33,"[""Vision-Langage Model; Model Selection; Model Reuse""]",1,19c3962b-2c47-4995-ba5f-f744ab2653f5,2024-09-28,0.0526
iclr_v6NNopExN4,2025,POST: A Framework for Privacy of Soft-prompt Transfer,"Xun Wang, Jing Xu, Franziska Boenisch, Michael Backes, Christopher A. Choquette-Choo, Adam Dziedzic","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[6, 5, 6, 3]",5.0,"[3, 2, 3, 3]",2.75,"[2, 2, 3, 2]",2.25,"[2, 3, 3, 2]",2.5,"[4, 4, 3, 4]",3.75,"[""prompt transfer"", ""soft prompt"", ""privacy"", ""distillation"", ""confidentiality""]",1,bf751d36-fdab-402c-914c-20b3e98fcf26,2024-09-25,0.0526
iclr_v3XabZsB7j,2025,CNN Variational autoencoders' reconstruction ability of long ECG signals,"Ahsan Habib, Brenton Adey, Chandan Karmakar",interpretability and explainable AI,reject,Rejected,"[1, 3, 1, 3]",2.0,"[1, 2, 2, 2]",1.75,"[1, 2, 2, 2]",1.75,"[1, 1, 2, 2]",1.5,"[4, 4, 5, 4]",4.25,"[""VAE"", ""CNN"", ""electrocardiogram"", ""reconstruction"", ""compression"", ""interpretability""]",0,47aa2330-7b7a-41ac-8da5-b79f8340f075,2024-09-27,0.0
iclr_v3DwQlyGbv,2025,Paramanu-Ganita: An Efficient Pre-trained Generative Mathematics Language Model with Chain-of-Thought Instruction Fine-Tuning,"Mitodru Niyogi, Arnab Bhattacharya","foundation or frontier models, including LLMs",reject,Rejected,"[3, 1, 3]",2.33,"[2, 1, 2]",1.67,"[3, 1, 2]",2.0,"[2, 1, 1]",1.33,"[4, 5, 5]",4.67,"[""reasoning"", ""language models"", ""pretraining"", ""CoT fine-tuning"", ""AI4Math""]",0,ec337fc9-ae8c-45b4-ba0c-e32398229e11,2024-09-26,0.0
iclr_v2nEL42Pvb,2025,SSGNN: Simple Yet Effective Spectral Graph Neural Network,"Ram Samarth B B, Rishabh Sabharwal, Sundeep Prabhakar Chepuri, Punit Rathore","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[5, 5, 5, 5]",5.0,"[2, 2, 2, 2]",2.0,"[3, 2, 2, 3]",2.5,"[3, 2, 2, 3]",2.5,"[4, 4, 4, 4]",4.0,"[""Spectral Graph Neural Networks"", ""Graph Representation Learning""]",0,b428f5a2-1b77-43e7-9174-4bca5e89bd5f,2024-09-28,0.0
iclr_uy9oR0nYCW,2025,Toward Robust Real-World Audio Deepfake Detection: Closing the Explainability Gap,"Georgia Channing, Juil Sock, Ronald Clark, Philip Torr, Christian Schroeder de Witt",interpretability and explainable AI,reject,Rejected,"[1, 5, 3, 1]",2.5,"[1, 3, 2, 3]",2.25,"[2, 3, 2, 1]",2.0,"[1, 2, 1, 1]",1.25,"[4, 4, 4, 5]",4.25,"[""self-supervised learning"", ""explainability"", ""deepfake audio"", ""generalizability""]",13,771d2c2d-949e-4892-b7c1-8d1bef275672,2024-09-27,0.6842
iclr_uuCcK4cmlH,2025,IDS-Agent: An LLM Agent for Explainable Intrusion Detection in IoT Networks,"Yanjie Li, Zhen Xiang, Nathaniel D. Bastian, Dawn Song, Bo Li","foundation or frontier models, including LLMs",reject,Rejected,"[3, 3, 3]",3.0,"[3, 3, 2]",2.67,"[2, 3, 2]",2.33,"[2, 2, 2]",2.0,"[4, 4, 4]",4.0,"[""intrusion detection"", ""LLM agent"", ""internet of things"", ""LLM""]",60,fdf586c5-3c6a-4e16-b8d1-7b13edfb4629,2024-09-27,3.1579
iclr_urQi0TgXFY,2025,Hidden in Plain Text: Emergence & Mitigation of Steganographic Collusion in LLMs,"Yohan Mathew, Robert McCarthy, Joan Velja, Ollie Matthews, Christian Schroeder de Witt, Dylan Cope, Nandi Schoots","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[6, 5, 3, 6]",5.0,"[3, 2, 3, 4]",3.0,"[3, 2, 3, 3]",2.75,"[3, 2, 1, 3]",2.25,"[3, 4, 5, 4]",4.0,"[""Large Language Models"", ""Steganography"", ""Collusion"", ""Reinforcement Learning"", ""In-Context Learning"", ""Multi-agent Systems""]",36,031e141a-9f77-4eca-b5b1-b89ada844010,2024-09-27,1.8947
iclr_ubuGgIPVD0,2025,TSTTC: A Large-Scale Dataset for Time-to-Contact Estimation in Driving Scenarios,"Yuheng Shi, Zehao Huang, Yan Yan, Naiyan Wang, Xiaojie Guo",datasets and benchmarks,reject,Rejected,"[5, 5, 5, 5]",5.0,"[3, 2, 2, 3]",2.5,"[3, 2, 2, 3]",2.5,"[2, 3, 2, 3]",2.5,"[4, 4, 5, 3]",4.0,"[""Time-to-Contact Estimation"", ""Dataset""]",2,58403b24-d2e3-4b63-b917-8705ce9751d9,2024-09-27,0.1053
iclr_uMxiGoczX1,2025,Data-Driven Creativity: Amplifying Imagination in LLM Writing,"Jialian Li, Ludan ZHANG, YipinZhang, Jian Xie, Dong Yan","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[3, 1, 3, 3]",2.5,"[2, 1, 2, 1]",1.5,"[1, 1, 3, 1]",1.5,"[2, 1, 2, 2]",1.75,"[4, 5, 4, 4]",4.25,"[""LLM"", ""RLHF""]",0,3438ed40-1e93-4ac7-8f38-fee18e8d4e72,2024-09-27,0.0
iclr_uMLeOlzlZ2,2025,LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval,"Yuan Chiang, Elvis Hsieh, Chia-Hong Chou, Janosh Riebesell","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[3, 8, 1, 8]",5.0,"[3, 3, 2, 3]",2.75,"[3, 3, 1, 3]",2.5,"[1, 3, 2, 3]",2.25,"[3, 4, 4, 4]",3.75,"[""information retrieval"", ""language model"", ""database"", ""materials informatics""]",52,e59ea430-59c1-4435-9f70-524c487d7400,2024-09-28,2.7368
iclr_u6Y0GdTEYp,2025,Constrained Multi-Objective Optimization,"Dongsheng Li, Xinghan Gong, Xiaowen Gong, Shiwen Mao",optimization,reject,Rejected,"[3, 3, 3, 1]",2.5,"[1, 2, 2, 2]",1.75,"[1, 3, 2, 2]",2.0,"[2, 2, 1, 1]",1.5,"[3, 3, 4, 4]",3.5,"[""constrained multi-objective optimization"", ""multi-gradient descent algorithms""]",219,8aac8bdc-c72c-4253-96bc-1dea51def27b,2024-09-28,11.5263
iclr_u14Y236LwX,2025,OpenWaves: A Large-Scale Anatomically Realistic Ultrasound-CT Dataset for Benchmarking Neural Wave Equation Solvers,"Zhijun Zeng, Youjia Zheng, Hao Hu, Zeyuan Dong, Yihang Zheng, Xinliang Liu, Jinzhuo Wang, Zuoqiang Shi, Linfeng Zhang, Yubing Li, He Sun",datasets and benchmarks,reject,Rejected,"[5, 6, 3, 6]",5.0,"[2, 4, 3, 2]",2.75,"[3, 3, 2, 2]",2.5,"[3, 3, 1, 2]",2.25,"[3, 3, 4, 4]",3.5,"[""Computational imaging"", ""Inverse problem"", ""Neural operators"", ""Ultrasound Computed Tomography"", ""Full Waveform Inversion""]",3,b9a3a51e-7cce-4272-94ab-1543f7aa168a,2024-09-26,0.1579
iclr_to4PdiiILF,2025,Honesty to Subterfuge: In-Context Reinforcement Learning Can Make Honest Models Reward Hack,"Leo McKee-Reid, Christoph Sträter, Maria Angelica Martinez, Joe Needham, Mikita Balesni","foundation or frontier models, including LLMs",reject,Rejected,"[3, 3, 3]",3.0,"[3, 2, 3]",2.67,"[3, 2, 2]",2.33,"[2, 2, 2]",2.0,"[3, 4, 5]",4.0,"[""Large Language Model"", ""Deception"", ""specification gaming"", ""Reward Hacking"", ""Evaluations"", ""in-context reinforcement learning"", ""in-context learning"", ""iterative reflection"", ""gpt-4o-mini"", ""gpt-4o"", ""o1-mini"", ""o1-preview""]",10,440a53d0-ee8c-4d79-a819-b9fdf8a70c9e,2024-09-28,0.5263
iclr_tccML2tDd4,2025,Perceptual Piercing: Human Visual Cue-Based Object Detection in Low Visibility Conditions,Ashutosh Kumar,"applications to computer vision, audio, language, and other modalities",reject,Rejected,"[3, 1, 3]",2.33,"[2, 1, 3]",2.0,"[2, 1, 1]",1.33,"[1, 1, 1]",1.0,"[3, 5, 4]",4.0,"[""deep learning"", ""computer vision"", ""dehazing"", ""bio-inspired networks"", ""human visual perception""]",1,b2c5489a-8e76-4a4f-ba19-211d7b589e43,2024-09-27,0.0526
iclr_tKnPtyDt6H,2025,Active Evaluation Acquisition for Efficient LLM Benchmarking,"Yang Li, Jie Ma, Miguel Ballesteros, Yassine Benajiba, Graham Horwood","foundation or frontier models, including LLMs",reject,Rejected,"[3, 3, 8, 6]",5.0,"[2, 3, 3, 3]",2.75,"[2, 2, 3, 2]",2.25,"[2, 2, 3, 2]",2.25,"[3, 4, 4, 3]",3.5,"[""Efficient LLM Evaluation"", ""Active Learning"", ""Subset Selection""]",10,7398e0a7-1588-4a71-8197-86e19c5d87db,2024-09-27,0.5263
iclr_tKFZ53nerQ,2025,Topic and Description Reasoning Generation based on User-Contributed Comments,"Tong-Ru Wu, Jheng-Long Wu, PAN HONG-RUI","transfer learning, meta learning, and lifelong learning",reject,Rejected,"[1, 3, 1, 3]",2.0,"[1, 3, 1, 2]",1.75,"[1, 2, 1, 2]",1.5,"[1, 2, 1, 2]",1.5,"[4, 4, 5, 2]",3.75,"[""topic modeling"", ""topic reasoning"", ""large language models""]",0,3eaddfe6-aa3a-406f-aece-004a416f07ed,2024-09-27,0.0
iclr_tJE9WeqHEI,2025,Beyond Scaling Laws: Understanding Transformer Performance with Associative Memory,"Xueyan Niu, Bo Bai, Lei Deng, Wei Han","neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)",reject,Rejected,"[5, 8, 6, 8]",6.75,"[2, 4, 4, 3]",3.25,"[2, 4, 4, 4]",3.5,"[2, 3, 3, 3]",2.75,"[4, 3, 3, 4]",3.5,"[""Transformer; Associative Memory; Energy-Based Model""]",19,effebbed-3f2c-4592-b32f-7bb65a5798a6,2024-09-26,1.0
iclr_tC1b9DBWww,2025,Person Detection Through the Lens of Algorithmic Bias,"Kiana Alikhademi, Emma Drobina, Jean D Louis","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[1, 3, 3, 3]",2.5,"[1, 3, 3, 3]",2.5,"[1, 2, 3, 1]",1.75,"[1, 2, 3, 2]",2.0,"[5, 3, 5, 4]",4.25,"[""object detection"", ""autonomous vehicles"", ""algorithmic bias"", ""algorithmic fairness"", ""fairness in ML""]",0,9f2c3a8e-df57-492b-ab25-f35068f2c1f1,2024-09-27,0.0
iclr_t8ctvylFn7,2025,Linearly Controlled Language Generation with Performative Guarantees,"Emily Cheng, Marco Baroni, Carmen Amo Alonso","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[6, 5, 6, 3]",5.0,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 3]",3.0,"[3, 2, 3, 2]",2.5,"[3, 3, 4, 4]",3.5,"[""control theory"", ""representation engineering"", ""large language models""]",16,91dd1677-80c3-423d-9972-5ed316856efb,2024-09-26,0.8421
iclr_t73rC2GJQJ,2025,DMM: Building a Versatile Image Generation Model via Distillation-Based Model Merging,"Tianhui Song, Weixin Feng, Shuai Wang, Xubin Li, Tiezheng Ge, Bo Zheng, Limin Wang",generative models,reject,Rejected,"[5, 5, 5, 5]",5.0,"[2, 3, 3, 3]",2.75,"[2, 4, 3, 3]",3.0,"[2, 2, 2, 2]",2.0,"[3, 5, 4, 4]",4.0,"[""Diffusion Models"", ""Generative Models"", ""Model Merging""]",5,f36350d6-33ec-48c3-bb95-019762b82f5c,2024-09-23,0.2632
iclr_t2yD3IaIMc,2025,Hypernetwork-Based Equivariant CNNs,"Chengyu Jiao, Yu Zhang","other topics in machine learning (i.e., none of the above)",reject,Rejected,"[5, 5, 5, 5]",5.0,"[3, 2, 2, 3]",2.5,"[2, 2, 2, 3]",2.25,"[1, 2, 2, 2]",1.75,"[4, 4, 3, 4]",3.75,"[""Equivariant Neural Networks"", ""Geometric Deep Learning""]",0,1b3e0664-b7fe-4f20-aa92-b35f83132766,2024-09-26,0.0
iclr_swqZKDoMJA,2025,Decoupled SGDA for Games with Intermittent Strategy Communication,"Ali Zindari, Parham Yazdkhasti, Anton Rodomanov, Tatjana Chavdarova, Sebastian U Stich",optimization,reject,Rejected,"[6, 6, 8, 6]",6.5,"[3, 3, 4, 3]",3.25,"[3, 3, 3, 3]",3.0,"[2, 3, 3, 2]",2.5,"[2, 3, 4, 3]",3.0,"[""optimization"", ""minimax optimization"", ""distributed games"", ""distributed optimization""]",2,6bdbc7a4-c317-4d63-afac-b1c2b65d2aca,2024-09-27,0.1053
iclr_sruGNQHd7t,2025,Privacy-Preserving of Deep Learning Queries by Domain Shifting,"Xiang Zhang, Aidong Adam Ding, Yunsi Fei","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[3, 5, 1]",3.0,"[1, 3, 3]",2.33,"[2, 3, 1]",2.0,"[1, 2, 1]",1.33,"[4, 4, 5]",4.33,"[""Privacy-Preserving"", ""Domain Shifting"", ""Input Obfuscation""]",0,5a32237f-39e4-4db0-bf7c-301f4c562e9e,2024-09-26,0.0
iclr_sgke1JuVlc,2025,Temporal Misinformation and Conversion through  Probabilistic Spiking Neurons,"Velibor Bojkovic, Xiaofeng Wu, Bin Gu",learning theory,reject,Rejected,"[8, 6, 3, 3]",5.0,"[1, 2, 2, 1]",1.5,"[3, 2, 2, 2]",2.25,"[3, 3, 3, 2]",2.75,"[4, 4, 5, 5]",4.5,"[""spiking neural networks"", ""probabilistic spiking"", ""ANN-SNN conversion""]",0,16af23d2-ba76-46a6-9b5a-425715891b81,2024-09-27,0.0
iclr_sTQC4TeYo1,2025,The GECo algorithm for Graph Neural Networks Explanation,"Salvatore Calderaro, Domenico Amato, Giosue' Lo Bosco, Riccardo Rizzo, Filippo Vella",interpretability and explainable AI,reject,Rejected,"[1, 3, 1, 3]",2.0,"[2, 3, 1, 2]",2.0,"[1, 2, 2, 2]",1.75,"[1, 2, 1, 1]",1.25,"[5, 4, 5, 5]",4.75,"[""Graph Neural Networks"", ""Interpretability"", ""Explainability""]",1,478a27ca-10a6-4875-a75d-78c5bea79c8f,2024-09-27,0.0526
iclr_sOOrTkYgb6,2025,DSEG-LIME: Improving Image Explanation by Hierarchical Data-Driven Segmentation,"Patrick Knab, Sascha Marton, Christian Bartelt",interpretability and explainable AI,reject,Rejected,"[3, 8, 3, 6]",5.0,"[2, 2, 2, 3]",2.25,"[2, 2, 2, 2]",2.0,"[2, 2, 2, 3]",2.25,"[4, 3, 4, 3]",3.5,"[""XAI"", ""LIME"", ""Segmentation""]",10,ba319bee-b8ec-4c47-a46b-801d4a01cd3d,2024-09-13,0.5263
iclr_sFGMkoBjUe,2025,Lookahead Shielding for Regular Safety Properties in Reinforcement Learning,"Alex Goodall, Francesco Belardinelli",reinforcement learning,reject,Rejected,"[5, 3, 6, 6]",5.0,"[3, 2, 4, 3]",3.0,"[3, 2, 3, 3]",2.75,"[2, 1, 3, 3]",2.25,"[4, 4, 4, 4]",4.0,"[""Safe Reinforcement Learning"", ""Model Checking"", ""Shielding""]",0,bb7b7181-f044-4a21-a5e8-ea9642b787ce,2024-09-26,0.0
iclr_s9SVlWOcLt,2025,Proto Successor Measure: Representing the space of all possible solutions of Reinforcement Learning,"Siddhant Agarwal, Harshit Sikchi, Peter Stone, Amy Zhang",reinforcement learning,reject,Rejected,"[8, 6, 8, 5]",6.75,"[2, 3, 3, 3]",2.75,"[3, 3, 2, 3]",2.75,"[2, 3, 3, 2]",2.5,"[3, 3, 4, 3]",3.25,"[""Zero-Shot Reinforcement Learning"", ""Representation Learning"", ""Unsupervised RL""]",6,a7250726-3d6a-4785-9706-2ce1cf86be34,2024-09-27,0.3158
iclr_s5N7p5UjgR,2025,Markovian Transformers for Informative Language Modeling,"Scott Viteri, Max Lamparth, Peter Chatain, Clark Barrett",reinforcement learning,reject,Rejected,"[5, 6, 6, 10]",6.75,"[2, 2, 2, 4]",2.5,"[3, 2, 3, 4]",3.0,"[2, 2, 3, 4]",2.75,"[2, 4, 4, 4]",3.5,"[""Chain of Thought Reasoning"", ""Reinforcement Learning"", ""Scalable Oversight"", ""Language Modeling"", ""Proximal Policy Optimization""]",2,b4b0f0ca-c328-453a-a6e9-88d36593f6f6,2024-09-27,0.1053
iclr_rynb4Vn8rb,2025,DEQuify your force field: Towards efficient simulations using deep equilibrium models,"Andreas Burger, Luca Thiede, Alan Aspuru-Guzik, Nandita Vijaykumar","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[5, 6, 6, 3]",5.0,"[3, 3, 3, 2]",2.75,"[3, 3, 3, 2]",2.75,"[3, 3, 3, 2]",2.75,"[3, 4, 4, 5]",4.0,"[""Machine Learning Force Fields"", ""Deep Equilibrium Models""]",0,24724897-b66e-4ba4-b277-5f15e61e2ab6,2024-09-20,0.0
iclr_rpbzBXdo4x,2025,Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse,"Ryan Liu, Jiayi Geng, Addison J. Wu, Ilia Sucholutsky, Tania Lombrozo, Thomas L. Griffiths",applications to neuroscience & cognitive science,reject,Rejected,"[6, 3, 5, 6]",5.0,"[1, 4, 2, 3]",2.5,"[3, 2, 2, 3]",2.5,"[3, 3, 2, 3]",2.75,"[3, 4, 4, 4]",3.75,"[""chain of thought"", ""psychology"", ""overthinking""]",108,e60ec583-7034-4cde-bd39-f3f1ef31a43f,2024-09-28,5.6842
iclr_rcmhydaEJp,2025,Flow-based imputation of small data,"Bryan Edward Kaiser, Kyle S. Hickmann","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[3, 3, 3]",3.0,"[1, 3, 1]",1.67,"[2, 2, 1]",1.67,"[2, 2, 1]",1.67,"[2, 3, 3]",2.67,"[""Normalizing flows"", ""imputation"", ""diffeomorphism"", ""out of distribution detection""]",0,b65efb5c-fa4b-475b-9cc5-ff720765710f,2024-09-27,0.0
iclr_rYU6xsZkfk,2025,Derivatives Are All You Need For Learning Physical Models,"Alessandro Trenta, Andrea Cossu, Davide Bacciu","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[3, 3, 3, 1]",2.5,"[2, 2, 2, 2]",2.0,"[3, 2, 2, 1]",2.0,"[1, 2, 2, 1]",1.5,"[4, 4, 4, 3]",3.75,"[""Physics-informed neural networks"", ""Physics-inspired neural networks"", ""Dynamical systems"", ""Learning physics"", ""Physical systems""]",0,45be5da2-8df7-431e-bf48-bc0d0c34d89b,2024-09-27,0.0
iclr_rVD4lasVp4,2025,A Lazy Hessian Evaluation Framework for Accelerating Stochastic Bilevel Optimization,"Peiwen Qiu, Prashant Khanduri, Haibo Yang, Chaosheng Dong, Jia Liu, Mingyi Hong",optimization,reject,Rejected,"[5, 5, 5, 5]",5.0,"[3, 2, 2, 3]",2.5,"[2, 2, 2, 2]",2.0,"[2, 2, 3, 2]",2.25,"[5, 4, 2, 4]",3.75,"[""bilevel optimization"", ""stochastic optimization"", ""lazy Hessian evaluation""]",0,fa27b398-1f03-48eb-833b-b94453342378,2024-09-27,0.0
iclr_rSAPrQzoQa,2025,Subject Clustering by an Improved IF-PCA Algorithm,"Yinan Guo, Jiashun Jin","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[3, 5, 6, 6]",5.0,"[2, 2, 2, 3]",2.25,"[2, 3, 3, 3]",2.75,"[1, 3, 2, 2]",2.0,"[5, 2, 3, 3]",3.25,"[""gene microarray"", ""scRNA-seq"", ""feature selection"", ""manifold fitting"", ""nonlinearity"", ""PCA"", ""sparsity"", ""subject clustering""]",0,87d9f2f9-7a4a-4b6e-bb64-f909b54d0b5e,2024-09-27,0.0
iclr_rQV33MVNWs,2025,FreeGaussian: Guidance-free Controllable 3D Gaussian Splats with Flow Derivatives,"Qizhi Chen, Delin Qu, Yiwen Tang, Haoming Song, Yiting Zhang, Bin Zhao, Dong Wang, Xuelong Li","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[3, 6, 5, 6]",5.0,"[2, 3, 3, 2]",2.5,"[2, 3, 3, 2]",2.5,"[2, 3, 3, 2]",2.5,"[3, 4, 4, 4]",3.75,"[""3D Gaussian Splatting"", ""Controllable View Synthesis""]",3,61dcda1b-9a3f-45c9-9411-80352bc6d388,2024-09-13,0.1579
iclr_rKMz6cDE7W,2025,One Pass Streaming Algorithm for Super Long Token Attention Approximation in Sublinear Space,"Raghavendra Addanki, Chenyang Li, Zhao Song, Chiwun Yang","other topics in machine learning (i.e., none of the above)",reject,Rejected,"[3, 1, 3]",2.33,"[3, 2, 1]",2.0,"[3, 2, 1]",2.0,"[1, 1, 1]",1.0,"[3, 5, 3]",3.67,"[""streaming algorithm"", ""efficient attention"", ""super-long context""]",9,9c41cab4-ced5-448b-8bc1-ec2ed5685e33,2024-09-26,0.4737
iclr_rDb9oY6Ww7,2025,Robust Consensus Anchor Learning for Efficient Multi-view Subspace Clustering,"Yalan Qin, Nan Pu, Nicu Sebe, Guorui Feng","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[3, 8, 8, 8]",6.75,"[3, 3, 4, 3]",3.25,"[3, 3, 3, 3]",3.0,"[2, 3, 3, 3]",2.75,"[5, 5, 5, 5]",5.0,"[""Multi-view clustering"", ""consensus anchor learning"", ""effectiveness and efficiency""]",3,8fa31fad-fad4-419d-a9d7-ec1f5bbd77dd,2024-09-24,0.1579
iclr_rCvdAVQpAe,2025,Physics-Informed Deep B-Spline Networks,"Zhuoyuan Wang, Raffaele Romagnoli, Jasmine Ratchford, Yorie Nakahira","neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)",reject,Rejected,"[5, 6, 6, 8]",6.25,"[3, 2, 3, 4]",3.0,"[2, 3, 2, 4]",2.75,"[2, 2, 2, 3]",2.25,"[3, 3, 4, 3]",3.25,"[""Physics-informed machine learning"", ""B-splines"", ""Partial differential equations (PDEs)""]",0,320510e3-eaa0-4009-81d7-5b331effa968,2024-09-27,0.0
iclr_rBAnJed1iY,2025,A Provably Robust Algorithm for Differentially Private Clustered Federated Learning,"Saber Malekmohammadi, Afaf Taik, Golnoosh Farnadi","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[3, 8, 3, 6]",5.0,"[2, 3, 2, 2]",2.25,"[1, 3, 1, 2]",1.75,"[2, 3, 2, 2]",2.25,"[4, 3, 4, 3]",3.5,"[""Federated Learning"", ""Clustered Federated Learning"", ""Differential Privacy"", ""Clustering""]",0,d04dd0e8-2c6c-4746-ad65-b2964682f9d4,2024-09-27,0.0
iclr_r4Q86nBQka,2025,A second-order-like optimizer with adaptive gradient scaling for deep learning,"Jerome Bolte, Ryan Boustany, Edouard Pauwels, Andrei Purica",optimization,reject,Rejected,"[3, 6, 5, 6]",5.0,"[2, 3, 3, 3]",2.75,"[2, 4, 2, 2]",2.5,"[2, 2, 3, 3]",2.5,"[3, 3, 3, 3]",3.0,"[""deep learning"", ""second-order methods"", ""stochastic optimization"", ""dynamical systems""]",4,9e3b91c8-1121-497e-8f8e-1e6d1ee8b824,2024-09-27,0.2105
iclr_qrTrnrEi9d,2025,Translation and Fusion Improves Zero-shot Cross-lingual Information Extraction,"Yang Chen, Vedaant Shah, Alan Ritter","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[3, 6, 6, 5]",5.0,"[4, 2, 3, 3]",3.0,"[3, 3, 3, 3]",3.0,"[2, 3, 2, 2]",2.25,"[4, 4, 4, 4]",4.0,"[""large language model"", ""multilingual"", ""information extraction"", ""low-resource language""]",7,e658959c-614b-4eaa-a270-2d332e483c16,2024-09-26,0.3684
iclr_qq0zZMC4SM,2025,Synthetic Datasets for Machine Learning on Spatio-Temporal Graphs using PDEs,"Jost Arndt, Utku Isil, Michael Detzel, Wojciech Samek, Jackie Ma",datasets and benchmarks,reject,Rejected,"[8, 3, 3, 6]",5.0,"[3, 3, 1, 3]",2.5,"[3, 2, 1, 3]",2.25,"[4, 3, 1, 3]",2.75,"[2, 4, 5, 4]",3.75,"[""Data"", ""Dataset"", ""PDE"", ""Graph"", ""Spatio-Temporal"", ""Epidemiology"", ""Benchmarking""]",0,84b6b242-f1c7-4252-b485-fc8b8bc4e608,2024-09-26,0.0
iclr_qlzxeNESWI,2025,Bandits with Anytime Knapsacks,"Eray Can Elumar, Cem Tekin, Osman Yagan","probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)",reject,Rejected,"[5, 5, 8, 8]",6.5,"[3, 3, 3, 2]",2.75,"[3, 3, 3, 3]",3.0,"[2, 1, 3, 3]",2.25,"[4, 4, 4, 3]",3.75,"[""multi-armed bandits"", ""knapsack problem"", ""online learning""]",1,b254e7d6-6537-4da4-ad27-525d574842b9,2024-09-26,0.0526
iclr_qit4pa6PpY,2025,Evaluating the Instruction-following Abilities of Language Models using Knowledge Tasks,"Rudra Murthy, Prince Kumar, Praveen Venkateswaran, Danish Contractor",datasets and benchmarks,reject,Rejected,"[3, 3, 3]",3.0,"[2, 1, 2]",1.67,"[2, 3, 1]",2.0,"[2, 2, 1]",1.67,"[4, 4, 4]",4.0,"[""Large Language Models"", ""Instruction Following"", ""Evaluation Benchmark""]",6,2525a6d5-b3c2-463e-945a-a6665c730b89,2024-09-27,0.3158
iclr_qi5dkmEE91,2025,Uncovering BioLOGICAL Motifs and Syntax via Sufficient and Necessary Explanations,"Beepul Bharti, Gabriele Scalia, Alex M Tseng","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[5, 3, 1]",3.0,"[2, 2, 2]",2.0,"[2, 2, 1]",1.67,"[2, 2, 1]",1.67,"[4, 3, 5]",4.0,"[""interpretability"", ""attributions"", ""computational biology""]",0,cacd178b-f041-4824-aa54-bf0036bc4c13,2024-09-28,0.0
iclr_qfU5S4cddQ,2025,Physics-Informed Weakly Supervised Learning for Interatomic Potentials,"Makoto Takamoto, Viktor Zaverkin, Mathias Niepert","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[3, 3, 8, 6]",5.0,"[2, 4, 3, 3]",3.0,"[2, 2, 3, 4]",2.75,"[2, 2, 3, 3]",2.5,"[4, 5, 3, 5]",4.25,"[""machine learning interatomic potential"", ""weakly supervised learning"", ""machine learning for science""]",4,46137304-309a-49e7-b955-8b613f48d109,2024-09-26,0.2105
iclr_qZz7PKt4bE,2025,AutoTune for Time Series Transformers using Low Rank Adaptation and Limited Discrepancy Search,"Shivani Tomar, Elizabeth M. Daly, Ivana Dusparic, Seshu Tirupathi, Radu Marinescu","foundation or frontier models, including LLMs",reject,Rejected,"[5, 3, 1]",3.0,"[2, 2, 1]",1.67,"[2, 2, 2]",2.0,"[2, 2, 1]",1.67,"[3, 3, 5]",3.67,"[""Time Series Transformers"", ""LoRA"", ""Time Series Forecasting""]",0,62aa706f-97ca-477d-a373-12329c41ff42,2024-09-27,0.0
iclr_qU1GtrDDst,2025,Representation learning for financial time series forecasting,"Antony Krymski, Paul Alexander Bilokon, Tom Davison",learning on time series and dynamical systems,reject,Rejected,"[3, 1, 1, 1, 3]",1.8,"[2, 1, 1, 1, 1]",1.2,"[1, 2, 1, 1, 1]",1.2,"[1, 1, 1, 1, 2]",1.2,"[4, 4, 5, 4, 4]",4.2,"[""representation learning"", ""contrastive predictive coding"", ""cpc""]",0,0c802063-696e-43a4-bbd6-a8082867047d,2024-09-23,0.0
iclr_qTWDpbF47t,2025,Compositional Video Generation as Flow Equalization,"Xingyi Yang, Xinchao Wang",generative models,reject,Rejected,"[8, 8, 6, 5]",6.75,"[3, 2, 2, 2]",2.25,"[3, 3, 3, 3]",3.0,"[3, 3, 2, 2]",2.5,"[3, 4, 5, 4]",4.0,"[""Video Generation; Compositionality""]",13,860dc3b4-8edb-4066-bbfe-b1585900a59c,2024-09-17,0.6842
iclr_qODJnX99hi,2025,Hierarchical Self-Attention: Generalizing Neural Attention Mechanics to Hierarchy,"Saeed Amizadeh, Sara Abdali, Yinheng Li, Kazuhito Koishida",learning on graphs and other geometries & topologies,reject,Rejected,"[5, 8, 8, 5]",6.5,"[3, 4, 4, 2]",3.25,"[3, 3, 3, 2]",2.75,"[2, 3, 3, 2]",2.5,"[3, 2, 3, 3]",2.75,"[""Attention Mechanism"", ""Transformers"", ""Hierarchical Data"", ""Multi-modal Data"", ""Geometric Deep Learning""]",0,c6f92c61-6e94-4b53-afc6-1c2f0c1522a0,2024-09-27,0.0
iclr_q9T51gF0fr,2025,Understanding Adversarially Robust Generalization via Weight-Curvature Index,"Yuelin Xu, Xiao Zhang","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[3, 6, 5, 6]",5.0,"[2, 3, 2, 3]",2.5,"[1, 2, 1, 3]",1.75,"[1, 3, 2, 3]",2.25,"[4, 4, 4, 2]",3.5,"[""Adversarially Robust Generalization; Weight-Curvature Index; Robust overfitting; PAC-Bayesian framework""]",1,a9982c43-fe2f-420b-b10a-60085fe638b2,2024-09-13,0.0526
iclr_pzmbxkCBiq,2025,Understanding Likelihood Over-optimisation in Direct Alignment Algorithms,"Zhengyan Shi, Sander Land, Acyr Locatelli, Matthieu Geist, Max Bartolo","foundation or frontier models, including LLMs",reject,Rejected,"[6, 3, 8, 3]",5.0,"[4, 3, 2, 1]",2.5,"[3, 2, 3, 2]",2.5,"[2, 2, 3, 2]",2.25,"[4, 3, 3, 4]",3.5,"[""Preference Learning"", ""Large Language Model"", ""Direct Alignment Algorithm""]",6,90913c4d-5c2a-47b1-b20f-31b5d90f9484,2024-09-26,0.3158
iclr_pxy5wDMnzv,2025,InvestAlign: Align LLMs with Investor Decision-Making under Herd Behavior,"Huisheng Wang, Zhuoshi Pan, Hangjing Zhang, Mingxiao Liu, H. Vicky Zhao","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[6, 3, 5, 6]",5.0,"[4, 2, 3, 2]",2.75,"[3, 1, 2, 2]",2.0,"[3, 1, 2, 2]",2.0,"[4, 3, 3, 2]",3.0,"[""Alignment"", ""Investment decision"", ""Large language model"", ""Supervised fine-tuning""]",4,7100e2c0-4108-43ae-82c2-de38e4cd51a5,2024-09-27,0.2105
iclr_puGvShnqeA,2025,Interpreting Adversarial Attacks and Defenses using Architectures with Enhanced Interpretability,"Akshay G Rao, Chandra Shekar Lakshminarayanan, Arun Rajkumar",interpretability and explainable AI,reject,Rejected,"[3, 3, 3]",3.0,"[1, 2, 1]",1.33,"[3, 2, 2]",2.33,"[2, 1, 2]",1.67,"[4, 4, 3]",3.67,"[""adversarial attacks"", ""adversarial defenses"", ""computer vision"", ""deep learning"", ""Interpretability""]",2,ba0fa870-c986-4516-b63e-95c33463d956,2024-09-25,0.1053
iclr_ptTt8mhS7n,2025,In-Context Transfer Learning: Demonstration Synthesis by Transferring Similar Tasks,"Dingzirui Wang, Xuanliang Zhang, Qiguang Chen, Longxu Dou, Xiao Xu, Rongyu Cao, YINGWEI MA, Qingfu Zhu, Wanxiang Che, Binhua Li, Fei Huang, Yongbin Li","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[6, 6, 3, 5]",5.0,"[3, 3, 1, 3]",2.5,"[2, 3, 1, 3]",2.25,"[3, 2, 2, 2]",2.25,"[3, 3, 4, 4]",3.5,"[""In-Context Learning"", ""Transfer Learning"", ""Demonstration Synthesis""]",2,de0fa285-7f19-4088-b532-2cafbfb4dfd0,2024-09-26,0.1053
iclr_plAiJUFNja,2025,Graph-Enhanced Learning for Predicting Optimal Drug Combinations Using Contrastive Embedding,"Zhenghan chen, youhuan yang, Lang Zheng, Ruxue Xing, Han Quan","transfer learning, meta learning, and lifelong learning",reject,Rejected,"[1, 3, 3, 3]",2.5,"[1, 1, 1, 2]",1.25,"[1, 2, 2, 2]",1.75,"[1, 2, 3, 2]",2.0,"[3, 2, 2, 4]",2.75,"[""Graph Learning"", ""Contrastive Embedding"", ""DDI""]",0,305efff6-ca06-48fe-a63b-09e8c8af4d25,2024-09-28,0.0
iclr_pPWAPiFf3z,2025,Generative Editing via Convolutional Obscuring (GECO): A Generative Adversarial Network for MRI de-artifacting,"Bryce Allen Bagley, Sergei Petrov, Ge Cheng, Matei Armănașu, Nancy J. Fischbein MD, Bin Jiang, Michael Iv, Eric Tranvinh, Michael Zeineh, Olivier Gevaert",applications to neuroscience & cognitive science,reject,Rejected,"[3, 3, 3]",3.0,"[2, 3, 2]",2.33,"[3, 2, 2]",2.33,"[2, 2, 3]",2.33,"[5, 5, 4]",4.67,"[""Deep convolutional neural networks"", ""computer vision"", ""medical machine learning"", ""image analysis"", ""generative adversarial networks"", ""artifact removal"", ""machine learning model generalization""]",3,febed04e-996a-4a10-a4cf-512a71a98482,2024-09-27,0.1579
iclr_pNxD5dpu1M,2025,Learning Cooperative Mean Field Games on Sparse Chung-Lu Graphs,"Christian Fabian, Kai Cui, Heinz Koeppl",learning on graphs and other geometries & topologies,reject,Rejected,"[5, 5, 5, 5]",5.0,"[3, 3, 2, 2]",2.5,"[3, 2, 2, 2]",2.25,"[2, 2, 3, 2]",2.25,"[4, 3, 3, 4]",3.5,"[""Cooperative Mean Field Games"", ""Large Networks"", ""Sparse Graphs"", ""Multi Agent Reinforcement Learning""]",0,f8a6479f-f28d-4d10-8315-6f0cce636cac,2024-09-26,0.0
iclr_pL8ws91RW2,2025,Hierarchical Self-Supervised Graph Contrastive Learning: Capturing Multi-Scale Structural Information,Ashish Dubey,"unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[3, 3, 1, 3, 3]",2.6,"[3, 2, 2, 2, 2]",2.2,"[2, 1, 2, 2, 2]",1.8,"[1, 1, 1, 1, 2]",1.2,"[5, 5, 4, 4, 4]",4.4,"[""Graph Neural Networks"", ""Self-supervised Learning"", ""Contrastive Learning"", ""Hierarchical Representation"", ""Node Classification""]",0,1a1639b6-da79-4eb9-b816-9dd5bd059f96,2024-09-28,0.0
iclr_pKMpmbuKnd,2025,Constrained Posterior Sampling: Time Series Generation with Hard Constraints,"Sai Shankar Narasimhan, Shubhankar Agarwal, Litu Rout, Sanjay Shakkottai, Sandeep P. Chinchali",generative models,reject,Rejected,"[5, 6, 8, 8]",6.75,"[4, 4, 4, 3]",3.75,"[3, 2, 4, 4]",3.25,"[2, 2, 4, 3]",2.75,"[5, 4, 3, 4]",4.0,"[""Time Series Generation"", ""Posterior Sampling"", ""Diffusion Models"", ""Controlled Generation""]",5,f25585ed-d0ae-4ec2-b601-38e81b00f3ca,2024-09-26,0.2632
iclr_pIVOSU7TFQ,2025,Detecting Discrepancies Between Generated and Natural Images Using Uncertainty,"Jun Nie, Yonggang Zhang, Tongliang Liu, Yiu-ming Cheung, Bo Han, Xinmei Tian","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[6, 5, 6, 3]",5.0,"[4, 4, 3, 1]",3.0,"[3, 3, 3, 1]",2.5,"[2, 3, 3, 2]",2.5,"[3, 4, 3, 5]",3.75,"[""AI-generated image detection""]",5,720a0a12-8972-4531-8f68-88db691032f6,2024-09-28,0.2632
iclr_p30YulvDbj,2025,OPTIMIZED SINGLE EEG CHANNEL SELECTION FOR DETECTING MAJOR DEPRESSIVE DISORDER,"Shruthi Narayanan Vaniya, Ahsan Habib, Sheik Mohammed Shariful Islam, Maia Angelova, Chandan Karmakar",learning on time series and dynamical systems,reject,Rejected,"[3, 3, 1, 1]",2.0,"[3, 2, 2, 1]",2.0,"[3, 2, 1, 1]",1.75,"[1, 2, 1, 1]",1.25,"[1, 4, 5, 4]",3.5,"[""Major depressive disorder"", ""deep learning"", ""electroencephalogram"", ""EEG"", ""single-channel""]",0,358111d2-75c7-4118-bf6e-8a6142bdaec6,2024-09-27,0.0
iclr_p1HeFnn2AA,2025,Deep Learning for Two-Sided Matching,"Sai Srivatsa Ravindranath, Zhe Feng, Shira Li, Jonathan Ma, Scott Kominers, David C. Parkes","other topics in machine learning (i.e., none of the above)",reject,Rejected,"[8, 8, 6]",7.33,"[3, 3, 3]",3.0,"[3, 3, 4]",3.33,"[3, 3, 3]",3.0,"[4, 3, 4]",3.67,"[""Mechanism Design"", ""Matching Markets"", ""Game Theory"", ""Differentiable Economics"", ""Two-Sided Matching""]",36,71e1d904-959d-410f-aac3-af8dba0da24e,2024-09-27,1.8947
iclr_ouRX6A8RQJ,2025,Understanding Chain-of-Thought in LLMs Through Information Theory,"Jean-Francois Ton, Muhammad Faaiz Taufiq, Yang Liu","foundation or frontier models, including LLMs",reject,Rejected,"[6, 5, 8, 5, 8]",6.4,"[3, 2, 3, 3, 3]",2.8,"[3, 2, 3, 3, 3]",2.8,"[3, 2, 3, 3, 2]",2.6,"[4, 4, 4, 4, 2]",3.6,"[""Large Language models"", ""Chain-of-thought""]",42,9c37aac9-7ef0-480e-9df9-5b77a78f8ad7,2024-09-27,2.2105
iclr_ob7UrZOJve,2025,Inheritune: Training Smaller Yet More Attentive Language Models,"Sunny Sanyal, Ravid Shwartz-Ziv, Sujay Sanghavi, Alex Dimakis","foundation or frontier models, including LLMs",reject,Rejected,"[3, 6, 6, 5]",5.0,"[2, 3, 3, 3]",2.75,"[1, 3, 2, 2]",2.0,"[1, 2, 2, 2]",1.75,"[5, 4, 4, 3]",4.0,"[""Large Language Models"", ""Small Language Models"", ""Attention degeneration"", ""Efficient training"", ""Model Initialization""]",3,ce0f421c-2610-49f9-ad16-6635d77c7926,2024-09-28,0.1579
iclr_oaRaaG1WB1,2025,Unlocking Trilevel Learning with Level-Wise Zeroth Order Constraints: Distributed Algorithms and Provable Non-Asymptotic Convergence,"Yang Jiao, Kai Yang, Chengtao Jian",optimization,reject,Rejected,"[6, 5, 6, 3]",5.0,"[3, 2, 2, 1]",2.0,"[2, 2, 3, 2]",2.25,"[2, 2, 3, 2]",2.25,"[2, 4, 3, 5]",3.5,"[""Trilevel Optimization"", ""Distributed Optimization"", ""Zeroth Order Optimization""]",3,e82b8875-9f21-4cab-8d96-18e5045486fc,2024-09-28,0.1579
iclr_oZdaEiDBpF,2025,On Characterizing and Mitigating Imbalances in Multi-Instance Partial Label Learning,"Kaifu Wang, Efthymia Tsamoura, Dan Roth","other topics in machine learning (i.e., none of the above)",reject,Rejected,"[6, 5, 3, 6]",5.0,"[2, 2, 1, 2]",1.75,"[3, 2, 2, 3]",2.5,"[3, 3, 2, 2]",2.5,"[2, 3, 4, 4]",3.25,"[""muliti-instance partial label learning"", ""weakly-supervised learning"", ""neurosymbolic learning"", ""learning theory"", ""long-tailed learning"", ""learning imbalances"", ""class-specific error bounds""]",2,04bcd98d-11bb-4edc-ab7f-ec50d7bdd90c,2024-09-26,0.1053
iclr_oIvjUpuZLC,2025,Invariant Convolutional Layers for Time Series,"Thibaut Germain, Chrysoula Kosma, Laurent Oudre",learning on time series and dynamical systems,reject,Rejected,"[5, 3, 6, 6]",5.0,"[3, 1, 3, 3]",2.5,"[2, 2, 3, 3]",2.5,"[2, 2, 2, 3]",2.25,"[3, 4, 4, 2]",3.25,"[""Time Series"", ""Convolution"", ""Invariances"", ""Neural Network""]",0,35ba5342-b235-418a-a2f4-399839319664,2024-09-27,0.0
iclr_oIWN7eMhTb,2025,CityBench: Evaluating the Capabilities of Large Language Models for Urban Tasks,"Jie Feng, Jun Zhang, Tianhui Liu, Xin Zhang, Tianjian Ouyang, Junbo Yan, Yuwei Du, Siqi Guo, Yong Li",datasets and benchmarks,reject,Rejected,"[8, 6, 6, 5, 10]",7.0,"[4, 3, 2, 2, 4]",3.0,"[3, 3, 3, 2, 4]",3.0,"[3, 3, 3, 1, 4]",2.8,"[3, 5, 4, 4, 4]",4.0,"[""large language model"", ""urban science"", ""world model"", ""benchmark"", ""multi-modal""]",34,478bf51a-3b1d-4a98-8eca-5c91aa05f9dc,2024-09-26,1.7895
iclr_o9ewXD1JuB,2025,OLAPH: Improving Factuality in Biomedical Long-form Question Answering,"Minbyul Jeong, Hyeon Hwang, Chanwoong Yoon, Taewhoo Lee, Jaewoo Kang","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[5, 6, 8, 6]",6.25,"[3, 3, 4, 3]",3.25,"[3, 3, 4, 3]",3.25,"[3, 3, 3, 2]",2.75,"[3, 4, 3, 4]",3.5,"[""medical question answering"", ""automatic evaluation"", ""factuality"", ""hallucination""]",24,a1d3d41c-e534-423b-a35c-67dff026efa3,2024-09-26,1.2632
iclr_o9YC0B6P2m,2025,Scaling Law with Learning Rate Annealing,"Howe Tissue, Venus Wang, Lu Wang","foundation or frontier models, including LLMs",reject,Rejected,"[6, 5, 8, 8]",6.75,"[2, 3, 3, 3]",2.75,"[3, 2, 4, 2]",2.75,"[3, 2, 3, 3]",2.75,"[4, 3, 4, 4]",3.75,"[""Scaling Laws"", ""Full Loss Curve Prediction"", ""Learning Rate Schedule"", ""LLM Pretraining""]",16,e3bc116a-a143-4dc8-837f-29d1537d2a0b,2024-09-26,0.8421
iclr_o6aUi3ukdd,2025,An Open Quantum Chemistry Property Database of 120 Kilo Molecules with 20 Million Conformers,"Weiqi Liu, Xi Ai, ZhijianZhou, Chao Qu, Junyi An, Zhipeng Zhou, Yuan Cheng, Xu Yinghui, Fenglei Cao, Yuan Qi",datasets and benchmarks,reject,Rejected,"[1, 3, 3, 3]",2.5,"[1, 1, 2, 1]",1.25,"[2, 2, 1, 2]",1.75,"[1, 2, 1, 1]",1.25,"[4, 4, 4, 4]",4.0,"[""Quantum Chemistry"", ""Machine Learning"", ""Organic Molecules""]",3,1b0872c1-6913-41f3-94ab-8f8bb93c0425,2024-09-26,0.1579
iclr_o2uHg0Skil,2025,"RL, but don't do anything I wouldn't do","Michael K. Cohen, Marcus Hutter, Yoshua Bengio, Stuart Russell","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[8, 8, 3, 6]",6.25,"[3, 4, 1, 2]",2.5,"[3, 3, 2, 3]",2.75,"[3, 4, 3, 3]",3.25,"[2, 2, 4, 2]",2.5,"[""AI safety"", ""Superalignment"", ""Algorithmic information theory"", ""Kolmogorov complexity"", ""Reinforcement learning"", ""Large language models""]",5,c188b8a0-6b05-4e43-ba4c-ed03b1fc9e1d,2024-09-27,0.2632
iclr_o2arTYxsXd,2025,Overcoming Catastrophic Forgetting in Federated Class-Incremental Learning via Federated Global Twin Generator,"Thinh Nguyen, Khoa D Doan, Binh T. Nguyen, Danh Le-Phuoc, Kok-Seng Wong","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[5, 5, 5, 5]",5.0,"[4, 2, 3, 3]",3.0,"[2, 3, 3, 3]",2.75,"[2, 3, 3, 2]",2.5,"[4, 4, 4, 4]",4.0,"[""federated learning"", ""continual learning"", ""federated continual learning"", ""generative model""]",8,babe4bcc-78dd-4396-8a9b-e00f27ac3bfd,2024-09-27,0.4211
iclr_o1efpbvR6v,2025,Application of Metric Transformation in One-Step Retrosynthesis,"Ngoc Trinh Hung NGUYEN, Milo Roucairol, Tristan Cazenave","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[1, 1, 5]",2.33,"[1, 1, 2]",1.33,"[1, 1, 2]",1.33,"[1, 1, 2]",1.33,"[4, 5, 5]",4.67,"[""Retrosynthesis"", ""Chemistry"", ""Deep Metric Learning"", ""Transformer""]",0,9080db6b-8287-445b-9a1f-088151b23f3f,2024-09-27,0.0
iclr_o1SGGW53GF,2025,NativQA: Multilingual Culturally-Aligned Natural Queries for LLMs,"Md. Arid Hasan, Maram Hasanain, Fatema Ahmad, Sahinur Rahman Laskar, Sunaya Upadhyay, Vrunda N Sukhadia, Mucahid Kutlu, Shammur Absar Chowdhury, Firoj Alam",datasets and benchmarks,reject,Rejected,"[6, 3, 8, 8]",6.25,"[3, 2, 3, 3]",2.75,"[2, 3, 3, 2]",2.5,"[3, 2, 3, 2]",2.5,"[4, 4, 5, 4]",4.25,"[""resources for less-resourced languages"", ""multilingual benchmarks"", ""multilingual corpora"", ""NLP datasets"", ""datasets for low resource languages""]",23,2878da75-20c2-45d8-82b4-73c495a5f338,2024-09-27,1.2105
iclr_npBAHV5BJI,2025,Towards Better Benchmark Datasets for Inductive Knowledge Graph Completion,"Harry Shomer, Jay Revolinsky, Jiliang Tang",datasets and benchmarks,reject,Rejected,"[8, 8, 6, 6]",7.0,"[4, 3, 3, 3]",3.25,"[3, 3, 2, 3]",2.75,"[3, 3, 3, 2]",2.75,"[4, 3, 4, 4]",3.75,"[""Knowledge graphs"", ""graphs"", ""link prediction""]",5,77073861-d208-4db5-aa9d-668779051907,2024-09-26,0.2632
iclr_neDGc4slhd,2025,An Empirical Study on the Application of TDA to Deep Neural Networks,"Tyler Sky Trogden, Dan Ventura",interpretability and explainable AI,reject,Rejected,"[1, 5, 3, 1, 6, 1, 3]",2.86,"[1, 2, 1, 2, 3, 2, 2]",1.86,"[1, 2, 2, 3, 3, 2, 3]",2.29,"[1, 3, 1, 1, 3, 1, 2]",1.71,"[4, 3, 3, 2, 4, 4, 4]",3.43,"[""deep neural networks"", ""convolutional networks"", ""topological data analysis"", ""persistent homology"", ""Betti numbers"", ""Betti curves"", ""Betti curve similarity"", ""ImageNet"", ""functional graph""]",0,6acd515b-55e9-41f3-a443-b7152c7aee55,2024-09-26,0.0
iclr_nXTpz8pTHK,2025,Reweighting Local Mimina with Tilted SAM,"Tian Li, Tianyi Zhou, Jeff Bilmes",optimization,reject,Rejected,"[6, 6, 8, 5]",6.25,"[3, 3, 3, 2]",2.75,"[2, 3, 3, 2]",2.5,"[3, 3, 3, 2]",2.75,"[4, 4, 4, 5]",4.25,"[""sharpness-aware optimization"", ""exponential tilting"", ""generalization""]",1,dfd2b67f-e71b-432d-94ab-de7109914276,2024-09-26,0.0526
iclr_nUOmJ4Qop5,2025,Peacock: Multi-Objective Optimization for Deep Neural Network Calibration,"Dexter Neo, Tsuhan Chen","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[6, 3, 6, 5]",5.0,"[3, 3, 3, 3]",3.0,"[3, 2, 3, 2]",2.5,"[3, 1, 3, 3]",2.5,"[4, 4, 4, 4]",4.0,"[""Deep Neural Network Calibration"", ""Uncertainty Calibration"", ""Robustness"", ""Safety"", ""Out-of-Distribution""]",0,c0f16406-562f-466c-a4b7-349c39a8e459,2024-09-19,0.0
iclr_nTZOIlf8YH,2025,Differentiation of Multi-objective Data-driven Decision Pipeline,"Peng Li, Lixia Wu, Chaoqun Feng, Jieping Ye, Lei Fu",optimization,reject,Rejected,"[3, 1, 3]",2.33,"[2, 2, 2]",2.0,"[2, 2, 2]",2.0,"[2, 2, 2]",2.0,"[3, 3, 4]",3.33,"[""decision-focused learning"", ""multi-objective optimization"", ""smart prediction-and-optimization"", ""data-driven optimization""]",1,fb27d781-ccad-49b2-87c8-a8085dd6adb0,2024-09-26,0.0526
iclr_nA1D0Y65m2,2025,The Benefits of Being Categorical Distributional: Uncertainty-aware Regularized Exploration in Reinforcement Learning,"Ke Sun, Yingnan Zhao, Enze Shi, Yafei Wang, Xiaodong Yan, Bei Jiang, Linglong Kong",reinforcement learning,reject,Rejected,"[3, 6, 8, 3]",5.0,"[1, 3, 3, 2]",2.25,"[1, 3, 3, 3]",2.5,"[1, 2, 3, 2]",2.0,"[5, 3, 2, 3]",3.25,"[""distributional learning"", ""reinforcement learning"", ""exploration""]",1,7a772502-f5a5-44a6-b669-69be3d740ad0,2024-09-26,0.0526
iclr_n2xueVy5ek,2025,Evaluating Large Language Models' Capability to Conduct Cyberattacks On Embedded Devices,"Fred Heiding, Simon Lermen",datasets and benchmarks,reject,Rejected,"[3, 3, 3]",3.0,"[3, 2, 1]",2.0,"[1, 2, 2]",1.67,"[1, 2, 2]",1.67,"[4, 4, 4]",4.0,"[""Computer security"", ""red teaming"", ""IoT"", ""large language models""]",0,6da7942e-9abd-4a76-a7ef-d3896866152f,2024-09-26,0.0
iclr_myZNJSpiK1,2025,CoVT-CXR: Building Chain of Visual Thought for Interpretable Chest X-Ray Diagnosis,"Xianyun Wang, Jun Bao, Buyu Liu, Gai Zhenbiao, Jiacong Zhou, Xiaoxing You, Fangge Mao, Yiqian Zhang, Yan Yang, Jun Yu",datasets and benchmarks,reject,Rejected,"[5, 8, 6, 8]",6.75,"[2, 3, 4, 2]",2.75,"[2, 3, 3, 3]",2.75,"[2, 3, 3, 3]",2.75,"[4, 5, 4, 3]",4.0,"[""chain of visual thought"", ""multimodal understanding"", ""fine-grained dataset"", ""medical report generation"", ""interpretable LLM.""]",1,79db3b4d-cddc-4c66-8aa9-0b7ee0a1979a,2024-09-27,0.0526
iclr_mmDkgLtYNI,2025,Spectral Shaping for Neural PDE Surrogates,"Daniel E. Worrall, Miles Cranmer, J. Nathan Kutz, Peter Battaglia",learning on time series and dynamical systems,reject,Rejected,"[5, 5, 5, 5]",5.0,"[3, 3, 3, 3]",3.0,"[2, 2, 2, 2]",2.0,"[3, 2, 2, 2]",2.25,"[5, 2, 4, 4]",3.75,"[""PDEs"", ""Fluid Mechanics"", ""Dynamical systems"", ""Autoregressive models"", ""Spectral methods""]",1,e08aa150-18bd-4266-b4b9-18dcfd14bb4a,2024-09-23,0.0526
iclr_mhyl7HhNM5,2025,Are LLMs Better than Reported? Detecting Label Errors and Mitigating Their Effect on Model Performance,"Omer Nahum, Nitay Calderon, Orgad Keller, Idan Szpektor, Roi Reichart","foundation or frontier models, including LLMs",reject,Rejected,"[5, 6, 8]",6.33,"[4, 3, 4]",3.67,"[1, 3, 4]",2.67,"[2, 2, 3]",2.33,"[4, 4, 4]",4.0,"[""LLMs"", ""label errors detection"", ""label errors handling"", ""data annotation""]",0,1db1a02d-5629-45a0-ada4-0b2f60cbaf16,2024-09-27,0.0
iclr_mfTM4UdYnC,2025,LogicJitter: Let LLMs play Logic Games and they will Detect Misinformation,"Luca Herranz-Celotti, Marco Viviani","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[3, 3, 1, 3]",2.5,"[2, 2, 1, 2]",1.75,"[1, 1, 2, 2]",1.5,"[1, 3, 1, 2]",1.75,"[4, 4, 4, 4]",4.0,"[""llm"", ""misinformation"", ""rule based AI"", ""toxicity""]",0,eacc474d-ca3e-47ab-97fb-c3f5139c3b29,2024-09-26,0.0
iclr_mc97L2QVIa,2025,Offline Multi-agent Reinforcement Learning with Sequential Score Decomposition,"Dan Qiao, Wenhao Li, Shanchao Yang, Hongyuan Zha, Baoxiang Wang",reinforcement learning,reject,Rejected,"[3, 3, 3]",3.0,"[2, 2, 2]",2.0,"[1, 2, 1]",1.33,"[1, 2, 2]",1.67,"[2, 4, 5]",3.67,"[""Multi-agent Reinforcement Learning"", ""Offline RL"", ""Diffusion Models""]",0,d6220580-0c83-4620-b8ba-5e87bfd3c95b,2024-09-25,0.0
iclr_mVCcWCjeEz,2025,ToEdit: How to Synthesize Text Data to Avoid Model Collapse?,"Xuekai Zhu, Daixuan Cheng, Hengli Li, Kaiyan Zhang, Ermo Hua, Xingtai Lv, Ning Ding, Zhouhan Lin, Zilong Zheng, Bowen Zhou","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[3, 8, 8, 6]",6.25,"[3, 3, 4, 2]",3.0,"[2, 3, 3, 3]",2.75,"[1, 2, 3, 2]",2.0,"[4, 3, 3, 4]",3.5,"[""synthetic data"", ""model collapse""]",0,0d1c574b-48c9-4e11-8a9b-d91459ea3597,2024-09-27,0.0
iclr_mIl15VP7vt,2025,Reliable and Efficient Amortized Model-based Evaluation,"Sang T. Truong, Yuheng Tu, Percy Liang, Bo Li, Sanmi Koyejo",datasets and benchmarks,reject,Rejected,"[6, 8, 6, 8, 6, 5]",6.5,"[3, 4, 3, 3, 3, 2]",3.0,"[3, 3, 2, 3, 3, 2]",2.67,"[3, 3, 4, 2, 2, 2]",2.67,"[3, 3, 4, 3, 2, 3]",3.0,"[""Model Evaluation"", ""Amortization"", ""Adaptive Testing""]",15,05f6681e-750e-49d5-9bd5-7088b79abdc7,2024-09-28,0.7895
iclr_lvhEptUoFF,2025,Recursive Abstractive Processing for Retrieval in Dynamic Datasets,"Charbel Chucri, Rami Azouz, Joachim Ott",generative models,reject,Rejected,"[3, 3, 3]",3.0,"[2, 1, 2]",1.67,"[2, 2, 2]",2.0,"[2, 1, 1]",1.33,"[4, 3, 3]",3.33,"[""Retrieval Augmented Language Models"", ""Information Retrieval"", ""Dynamic Datasets""]",0,50da0fc9-a61e-405d-8bf0-f6e3754cf715,2024-09-18,0.0
iclr_lvgsPjRtLM,2025,VideoDiT: Bridging Image Diffusion Transformers for Streamlined Video Generation,"Ruoyu Feng, Tiankai Hang, Tianyu He, Kai Qiu, Qi Dai, Jianmin Bao, Zhibo Chen, Chong Luo",generative models,reject,Rejected,"[3, 1, 3, 3]",2.5,"[2, 2, 3, 2]",2.25,"[2, 1, 2, 2]",1.75,"[2, 1, 2, 2]",1.75,"[4, 5, 5, 5]",4.75,"[""Text-to-Video Generation"", ""Diffusion Models"", ""Image Diffusion Transformer""]",1,188a04d2-09bb-4f58-aec9-1b94eb397752,2024-09-13,0.0526
iclr_lja4JMesmC,2025,From Generalist to Specialist: Adapting Vision Language Models via Task-Specific Visual Instruction Tuning,"Yang bai, Yang Zhou, Jun Zhou, Rick Siow Mong Goh, Daniel Shu Wei Ting, Yong Liu","foundation or frontier models, including LLMs",reject,Rejected,"[5, 6, 8, 6]",6.25,"[2, 3, 3, 4]",3.0,"[2, 3, 3, 3]",2.75,"[2, 2, 3, 3]",2.5,"[3, 4, 4, 4]",3.75,"[""Vison Language Models"", ""Task Specific Models"", ""Visual Instruction Tuning""]",6,143139f1-c74b-4b9a-bbf1-8b2aa3402ce7,2024-09-17,0.3158
iclr_leBbjaUxut,2025,Multi-Scale Image Diffusion Transformers: Explainability Leads to Faster Training,"Joshua Fixelle, Mircea Stan",generative models,reject,Rejected,"[3, 3, 6, 8]",5.0,"[1, 2, 3, 4]",2.5,"[2, 2, 3, 3]",2.5,"[2, 2, 3, 3]",2.5,"[5, 4, 4, 4]",4.25,"[""Diffusion Models"", ""Vision Transformers"", ""Generative Images"", ""Explainable AI"", ""Training Efficiency""]",0,d26fc591-0211-4a3b-94bd-34517027233d,2024-09-26,0.0
iclr_ldGz1DSut1,2025,On the Coexistence and Ensembling of Watermarks,"Aleksandar Petrov, Shruti Agarwal, Philip Torr, Adel Bibi, John Collomosse","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[6, 8, 6, 6]",6.5,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 3]",3.0,"[3, 2, 3, 3]",2.75,"[5, 4, 4, 5]",4.5,"[""watermarking"", ""watermark"", ""ensembles"", ""content provenance""]",4,28b141fe-b52f-493b-be44-80165f5ec077,2024-09-22,0.2105
iclr_lbj0i29Z92,2025,Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge,"Tianhao Wu, Weizhe Yuan, Olga Golovneva, Jing Xu, Yuandong Tian, Jiantao Jiao, Jason E Weston, Sainbayar Sukhbaatar","foundation or frontier models, including LLMs",reject,Rejected,"[6, 5, 6, 3]",5.0,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 2]",2.75,"[3, 3, 3, 2]",2.75,"[4, 4, 4, 4]",4.0,"[""self-improving"", ""self-rewarding"", ""LLM"", ""LLM-as-a-judge"", ""instruction following"", ""super alignment""]",188,c901d12c-184e-48d3-ac95-a50591a6e691,2024-09-26,9.8947
iclr_lZRRfupxYn,2025,A new perspective on applying mesoscience to explore the model generalizability,Fanyong Meng,interpretability and explainable AI,reject,Rejected,"[3, 3, 3]",3.0,"[2, 2, 1]",1.67,"[2, 1, 1]",1.33,"[2, 2, 2]",2.0,"[3, 3, 3]",3.0,"[""Mesoscience"", ""Compromise in competition"", ""Machine learning"", ""Generalizability""]",0,0bd50853-c7c7-471a-9069-20046a409f2a,2024-09-23,0.0
iclr_lWGXftRS5h,2025,On Inductive Biases That Enable Generalization in Diffusion Transformers,"Jie An, De Wang, Pengsheng Guo, Jiebo Luo, Alex Schwing",generative models,reject,Rejected,"[3, 6, 8, 3]",5.0,"[3, 2, 3, 2]",2.5,"[2, 3, 3, 2]",2.5,"[2, 2, 3, 2]",2.25,"[3, 3, 2, 3]",2.75,"[""Diffusion Model"", ""Diffusion Transformer"", ""Generalization"", ""Inductive Bias""]",6,2e332537-9f05-4707-8212-907300653e1f,2024-09-15,0.3158
iclr_lQYi2zeDyh,2025,Demystifying amortized causal discovery with transformers,"Francesco Montagna, Max Cairney-Leeming, Dhanya Sridhar, Francesco Locatello",causal reasoning,reject,Rejected,"[6, 3, 5, 6]",5.0,"[3, 2, 3, 3]",2.75,"[4, 2, 2, 3]",2.75,"[3, 1, 2, 3]",2.25,"[3, 4, 4, 3]",3.5,"[""causal discovery"", ""amortized inference"", ""transformers"", ""identifiability""]",5,f8e886f5-2699-4a83-9b58-d0830c86446b,2024-09-25,0.2632
iclr_lOTfiKt4Gc,2025,GUARD: Guideline Upholding Test through Adaptive Role-play and Jailbreak Diagnostics for LLMs,"Haibo Jin, Ruoxi Chen, Peiyan Zhang, Andy Zhou, Yang Zhang, Haohan Wang","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[3, 3, 6, 8]",5.0,"[2, 2, 3, 2]",2.25,"[2, 2, 2, 3]",2.25,"[3, 2, 2, 4]",2.75,"[5, 3, 3, 3]",3.5,"[""Large Language Models"", ""Jailbreak"", ""Red-teaming"", ""Safety""]",1,8b76ab90-3940-4de5-8b91-0c09d60d528b,2024-09-16,0.0526
iclr_lHBQrqVYji,2025,Provable Post-Deployment Deterioration Monitoring,"Viet Nguyen, Changjian Shui, Vijay Giri, Siddharth Arya, Amol Verma, Fahad Razak, Rahul Krishnan","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[3, 6, 5, 6]",5.0,"[2, 3, 3, 2]",2.5,"[2, 2, 3, 3]",2.5,"[2, 3, 3, 3]",2.75,"[4, 3, 4, 2]",3.25,"[""Deterioration Monitoring"", ""AI Safety"", ""Trustworthy ML"", ""AI for Healthcare"", ""Guardrails for AI""]",0,e979706d-eb09-4df3-bb97-f08fda8b0c60,2024-09-24,0.0
iclr_lFijzkTUNB,2025,A Bounding Box is Worth One Token: Interleaving Layout and Text in a Large Language Model for Document Understanding,"Jinghui Lu, Haiyang Yu, Yanjie Wang, Yongjie Ye, Jingqun Tang, Ziwei Yang, Binghong Wu, Qi Liu, Hao Feng, Han Wang, Hao Liu, Can Huang","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[5, 3, 6, 6]",5.0,"[2, 2, 3, 3]",2.5,"[2, 2, 3, 3]",2.5,"[2, 1, 3, 3]",2.25,"[4, 5, 3, 4]",4.0,"[""LLM"", ""DocAI"", ""Visually Rich Document Understanding"", ""KIE""]",59,f9b8a492-5ac5-42f6-ad61-0296abbbcdad,2024-09-26,3.1053
iclr_lCqNxBGPp5,2025,vVLM: Exploring Visual Reasoning in VLMs against Language Priors,"Tiange Luo, Ang Cao, Gunhee Lee, Justin Johnson, Honglak Lee",datasets and benchmarks,reject,Rejected,"[8, 3, 6, 3]",5.0,"[4, 2, 3, 2]",2.75,"[3, 2, 3, 1]",2.25,"[4, 2, 2, 2]",2.5,"[4, 3, 4, 4]",3.75,"[""Vision Language Model""]",2,0856a5d9-c66d-443a-a57a-29a30bce2c14,2024-09-27,0.1053
iclr_l6K688mhDT,2025,Rethinking the Bias of Foundation Model under Long-tailed Distribution,"Jiahao Chen, Yurou Liu, Bin Qin, Jiangmeng Li, Bing Su","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[5, 8, 6, 6]",6.25,"[3, 4, 3, 1]",2.75,"[3, 3, 2, 2]",2.5,"[3, 4, 2, 2]",2.75,"[4, 3, 3, 2]",3.0,"[""foundation model"", ""long-tailed learning""]",3,2e4b440f-62c6-4aad-a9ea-c3a39aab30aa,2024-09-26,0.1579
iclr_l3Q0scRuT9,2025,Gradient based Causal Discovery with Diffusion Model,"Furui Liu, Yuanhang Feng, Huiyang Wang, Zhouhan Lin, Bowen Zhou",causal reasoning,reject,Rejected,"[5, 5, 5, 5]",5.0,"[3, 2, 3, 3]",2.75,"[2, 2, 3, 2]",2.25,"[2, 1, 2, 2]",1.75,"[3, 3, 4, 3]",3.25,"[""Causal discovery"", ""generative models""]",0,fb253de3-7506-4768-9b06-e61c22ee856f,2024-09-24,0.0
iclr_ksBhCsSUaE,2025,Probabilistic Token Alignment for Large Language Model Fusion,"Runjia Zeng, James Chenhao Liang, Cheng Han, Zhiwen Cao, Jiahao Liu, Xiaojun Quan, Qifan Wang, Tong Geng, Dongfang Liu","foundation or frontier models, including LLMs",reject,Rejected,"[8, 6, 6, 5]",6.25,"[3, 3, 3, 3]",3.0,"[3, 3, 2, 3]",2.75,"[3, 3, 2, 2]",2.5,"[3, 3, 2, 4]",3.0,"[""Large Language Models"", ""Model Fusion""]",3,af3ef48b-9b2a-4440-a4c5-539a678fc5e1,2024-09-24,0.1579
iclr_kqdNvAhJrJ,2025,AC-PKAN: Attention-Enhanced and Chebyshev Polynomial-Based Physics-Informed Kolmogorov–Arnold Networks,"Hangwei Zhang, Yan Wang","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[8, 6, 6, 5]",6.25,"[3, 4, 3, 3]",3.25,"[3, 2, 3, 2]",2.5,"[3, 2, 3, 2]",2.5,"[3, 4, 4, 4]",3.75,"[""Physics-Informed Neural Networks"", ""Kolmogorov\u2013Arnold Networks"", ""Attention Mechanism"", ""PDEs"", ""Chebyshev Polynomials""]",2,00441d06-cca3-445d-bafc-dddfa1440b4b,2024-09-21,0.1053
iclr_kqZCOliBhV,2025,Leveraging Side Information with Deep Learning for Linear Inverse Problems: Applications to MR Image Reconstruction,"Arda Atalik, Sumit Chopra, Daniel Sodickson","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[6, 3, 6, 5]",5.0,"[3, 1, 3, 2]",2.25,"[3, 2, 3, 3]",2.75,"[2, 2, 3, 2]",2.25,"[4, 3, 4, 3]",3.5,"[""MR image reconstruction"", ""side information"", ""linear inverse problems"", ""Trust-Guided Variational Network""]",0,08fdc56d-a718-4bcd-a6ed-c0826dc194e2,2024-09-28,0.0
iclr_kqSmedTcgb,2025,Text-Augmented Multimodal LLMs for Chemical Reaction Condition Recommendation,"Yu Zhang, Ruijie Yu, Kaipeng Zeng, Ding Li, Feng Zhu, Xiaokang Yang, Yaohui Jin, Yanyan Xu","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[3, 5, 6, 6]",5.0,"[2, 3, 2, 2]",2.25,"[2, 2, 3, 2]",2.25,"[2, 2, 2, 2]",2.0,"[4, 5, 3, 4]",4.0,"[""Text-augmented"", ""Multimodal LLM"", ""Chemical reaction condition recommendation""]",10,0e5b6e0e-56ad-41c8-a0a0-d166a71432dd,2024-09-13,0.5263
iclr_kpLMN5fok1,2025,From Complex to Atomic: Enhancing Augmented Generation via Knowledge-Aware Dual Rewriting and Reasoning,"Jinyu Wang, Jingjing Fu, Lei Song, Jiang Bian","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[6, 6, 5, 3]",5.0,"[3, 2, 3, 2]",2.5,"[3, 2, 2, 2]",2.25,"[3, 3, 3, 2]",2.75,"[3, 4, 3, 4]",3.5,"[""RAG"", ""Knowledge atomizing"", ""Knowledge-aware task decomposition"", ""Multihop QA""]",1,10c549e1-01eb-46e8-8e93-525eb806a729,2024-09-25,0.0526
iclr_koza5fePTs,2025,Exploring and Benchmarking  Planning Capabilities of  Large Language Models,"Bernd Bohnet, Azade Nova, Aaron T Parisi, Katayoon Goshvadi, Kevin Swersky, Hanjun Dai, Dale Schuurmans, Noah Fiedel, Hanie Sedghi","foundation or frontier models, including LLMs",reject,Rejected,"[1, 1, 3, 3]",2.0,"[2, 2, 2, 3]",2.25,"[1, 2, 2, 2]",1.75,"[1, 1, 2, 1]",1.25,"[5, 5, 5, 5]",5.0,"[""planning capability"", ""LLMs"", ""many-shot"", ""in-context learning""]",6,365c733d-2274-4518-8446-e8633bad269f,2024-09-27,0.3158
iclr_kn2OZa8rOf,2025,Rethinking Attentions in Zero-Shot Real Image Editing,"Dinh-Khoi Vo, Thanh-Toan Do, Tam V. Nguyen, Minh-Triet Tran, Trung-Nghia Le","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[5, 5, 5, 5]",5.0,"[3, 2, 2, 2]",2.25,"[3, 2, 2, 2]",2.25,"[2, 2, 1, 2]",1.75,"[4, 4, 5, 3]",4.0,"[""Stable Diffusion"", ""Image Editing"", ""Zero-Shot Algorithm"", ""Attention""]",0,d007e3fb-4be1-49a4-81b8-ace9786daefd,2024-09-25,0.0
iclr_kWtP5ZOErR,2025,EvoPress: Towards Optimal Dynamic Model Compression via Evolutionary Search,"Oliver Sieberling, Denis Kuznedelev, Eldar Kurtic, Dan Alistarh","foundation or frontier models, including LLMs",reject,Rejected,"[8, 8, 6, 3]",6.25,"[3, 3, 2, 3]",2.75,"[3, 4, 3, 3]",3.25,"[3, 4, 2, 2]",2.75,"[2, 3, 2, 3]",2.5,"[""large language models"", ""compression"", ""evolutionary algorithms"", ""quantization"", ""pruning""]",12,f7e519c7-3623-47cd-bd1b-a66d000ff8f4,2024-09-28,0.6316
iclr_kT6oc5CpEi,2025,BlackDAN: A Black-Box Multi-Objective Approach to Effective and Contextual Jailbreaking of Language Models,"Xinyuan Wang, Xijie Huang, Renmiao Chen, Hao Wang, Lei Sha, Chengwei Pan, Minlie Huang","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[3, 3, 3]",3.0,"[1, 3, 3]",2.33,"[3, 3, 3]",3.0,"[2, 2, 2]",2.0,"[5, 4, 4]",4.33,"[""LLM Safety"", ""Multi-Objective Optimization"", ""Genetic Algorithm"", ""Black-box Jailbreaking""]",1,ceada499-0a29-4747-a0b1-b5bb1728ad04,2024-09-20,0.0526
iclr_kS27PPs3yR,2025,Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection,"JIAWEN ZHU, Yew-Soon Ong, Chunhua Shen, Guansong Pang","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[5, 5, 5, 5]",5.0,"[4, 2, 3, 4]",3.25,"[3, 2, 3, 3]",2.75,"[2, 3, 2, 2]",2.25,"[3, 4, 4, 4]",3.75,"[""Zero-Shot Anomaly Detection; Prompt Learning; Visual Anomaly Detection""]",20,2a01ed7a-865e-495f-9fd5-d08a692f5edb,2024-09-27,1.0526
iclr_k5ixIlfHc0,2025,Error Bounds for Deep Learning-based Uncertainty Propagation in SDEs,"Chun-Wei Kong, Luca Laurenti, Jay McMahon, Morteza Lahijanian","neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)",reject,Rejected,"[3, 6, 5, 6]",5.0,"[3, 3, 2, 2]",2.5,"[3, 3, 3, 3]",3.0,"[2, 2, 2, 3]",2.25,"[3, 2, 4, 3]",3.0,"[""uncertainty propagation"", ""physics-informed learning"", ""learning error quantification"", ""stochastic differential equations""]",1,83e13dbe-15fb-4f4b-8652-8a84d7f71987,2024-09-27,0.0526
iclr_jsVehKnSj4,2025,Minimax-optimal trust-aware multi-armed bandits,"Changxiao Cai, Jiacheng Zhang",reinforcement learning,reject,Rejected,"[8, 8, 6, 8]",7.5,"[4, 3, 3, 3]",3.25,"[4, 3, 2, 3]",3.0,"[3, 3, 3, 3]",3.0,"[4, 4, 3, 3]",3.5,"[""multi-armed bandit"", ""trust-aware decision-making"", ""regret bound"", ""minimax optimality""]",0,50700a4e-d74e-4418-8642-3a4bf95736ee,2024-09-27,0.0
iclr_jqAqZhEMsk,2025,"Divide, Reweight, and Conquer: A Logit Arithmetic Approach for In-Context Learning","Chengsong Huang, Langlin Huang, Jiaxin Huang","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[5, 5, 5, 5]",5.0,"[2, 3, 3, 3]",2.75,"[2, 3, 3, 2]",2.5,"[3, 3, 3, 2]",2.75,"[4, 4, 4, 3]",3.75,"[""Efficient Inference"", ""In-context Learning"", ""Non-gradient Optimization"", ""Large Language Models""]",12,c970d189-ed7e-4ba8-9a09-6aaa0ae709a2,2024-09-26,0.6316
iclr_jdpELUL0T6,2025,Low-Dimension-to-High-Dimension Generalization and Its Implications for Length Generalization,"Yang Chen, Yitao Liang, Zhouchen Lin","neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)",reject,Rejected,"[5, 6, 6, 3]",5.0,"[2, 2, 2, 1]",1.75,"[2, 3, 3, 4]",3.0,"[3, 2, 2, 2]",2.25,"[4, 3, 2, 3]",3.0,"[""Length Generalization"", ""Position Embedding""]",2,0aa9df73-3164-4b28-ba3c-6e1065f819a8,2024-09-18,0.1053
iclr_jYP8Cd2bMW,2025,FedMAP: Unlocking Potential in Personalized Federated Learning through Bi-Level MAP Optimization,"Fan Zhang, Carlos Esteve Yague, Sören Dittmer, Carola-Bibiane Schönlieb, Michael roberts","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[5, 6, 3, 6]",5.0,"[3, 4, 2, 2]",2.75,"[3, 3, 1, 2]",2.25,"[2, 3, 1, 3]",2.25,"[3, 3, 5, 2]",3.25,"[""Federated Learning"", ""Personalized Federated Learning"", ""Non-IID Data Distributions"", ""Bi-level Optimization""]",0,bc15995f-2389-4b73-ad11-29516fe9df97,2024-09-27,0.0
iclr_jQyKywGtpW,2025,Offline-to-Online Reinforcement Learning with Prioritized Experience Selection,"Zhongjian Qiao, Jiafei Lyu, Qi Liu, Xiu Li",reinforcement learning,reject,Rejected,"[5, 5, 5, 5]",5.0,"[3, 3, 3, 2]",2.75,"[3, 2, 2, 2]",2.25,"[2, 2, 2, 2]",2.0,"[4, 3, 4, 4]",3.75,"[""Reinforcement Learning; Offline-to-Online Reinforcement Learning; Prioritized Experience Selection""]",0,86f50f93-75e0-43ac-847d-4072f169ce2a,2024-09-18,0.0
iclr_jCNRcHrfLo,2025,Hierarchical Prompts with Context-aware Calibration for Open-Vocabulary Object Detection,"Duorui Wang, Xiaowei Zhao, Yuqing Ma, Xianglong Liu, Zhiwan Fang, Chenjue Zhang","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[5, 5, 5, 5]",5.0,"[3, 3, 3, 3]",3.0,"[3, 3, 2, 3]",2.75,"[2, 2, 2, 2]",2.0,"[5, 4, 3, 4]",4.0,"[""open-vocabulary object detection"", ""prompts tuning"", ""knowledge distillation""]",0,6f4e3609-ad01-44dd-ad94-01322b0e8581,2024-09-19,0.0
iclr_j7b4mm7Ec9,2025,Towards Lightweight Deep Watermarking Framework,"Yupeng Qiu, Han Fang, Ee-Chien Chang","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[8, 8, 8, 8, 6]",7.6,"[3, 3, 4, 3, 2]",3.0,"[3, 3, 4, 3, 3]",3.2,"[3, 3, 3, 3, 3]",3.0,"[3, 3, 4, 3, 4]",3.4,"[""machine vision"", ""deep learning-based watermarking""]",0,3b2e403b-5d82-47e9-9bc4-4ccadbb590cc,2024-09-26,0.0
iclr_j7ZWfqCYCY,2025,Information-Theoretical Principled Trade-off between Jailbreakability and Stealthiness on Vision Language Models,"Ching-Chia Kao, Chia-Mu Yu, Chun-Shien Lu, Chu-Song Chen","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[3, 5, 6, 6]",5.0,"[1, 3, 3, 3]",2.5,"[1, 3, 2, 3]",2.25,"[2, 2, 2, 3]",2.25,"[4, 4, 4, 5]",4.25,"[""Jailbreak"", ""Vision-Language Models"", ""Security"", ""Information Theory""]",3,84976541-df57-4e16-8ab9-beccec112670,2024-09-26,0.1579
iclr_j0sq9r3HFv,2025,Automated Parameter Extraction for Biologically Realistic Neural Networks: An Initial Exploration with Large Language Models,"Gaganpreet Jhajj, Gutierrez Carlos Enrique, Kenji Doya",applications to neuroscience & cognitive science,reject,Rejected,"[5, 3, 1, 1]",2.5,"[2, 1, 1, 1]",1.25,"[2, 1, 1, 1]",1.25,"[2, 1, 1, 1]",1.25,"[4, 4, 3, 5]",4.0,"[""Large Language Models"", ""Knowledge Graphs"", ""Computational neuroscience"", ""Neural model construction""]",0,a299cd52-3f44-431c-a7ef-3a1fdbfae144,2024-09-28,0.0
iclr_irCuIdCdAl,2025,Improving Transformer Interpretability with Activation Contrast-Based Attribution,"Sungmin Han, Jeonghyun Lee, Sangkyun Lee",interpretability and explainable AI,reject,Rejected,"[6, 5, 6, 3]",5.0,"[2, 3, 2, 2]",2.25,"[3, 3, 2, 2]",2.5,"[3, 2, 2, 2]",2.25,"[3, 3, 3, 4]",3.25,"[""Transformer"", ""Interpretability"", ""XAI"", ""Attention"", ""Contrast-based""]",0,f8ad12df-adb4-4320-9e17-32a69b0aa5ee,2024-09-28,0.0
iclr_ijwYWoChN9,2025,Domain Shift Tuning over Knowledge Gap,Noriaki Kawamae,optimization,reject,Rejected,"[3, 3, 3]",3.0,"[3, 2, 2]",2.33,"[2, 2, 2]",2.0,"[2, 2, 2]",2.0,"[4, 4, 3]",3.67,"[""PEFT"", ""Domain gap"", ""Domain Shift""]",0,ac6298c5-f73e-4bd5-9e7c-3df65f41aa3c,2024-09-26,0.0
iclr_ijFdq8uqki,2025,BeHonest: Benchmarking Honesty in Large Language Models,"Steffi Chern, Zhulin Hu, Yuqing Yang, Ethan Chern, Yuan Guo, Jiahe Jin, Binjie Wang, Pengfei Liu",datasets and benchmarks,reject,Rejected,"[6, 6, 5, 3]",5.0,"[3, 4, 3, 3]",3.25,"[3, 3, 3, 2]",2.75,"[3, 3, 4, 2]",3.0,"[4, 4, 4, 5]",4.25,"[""large language models"", ""honesty"", ""benchmark""]",34,5ad4dfd1-4a0f-46f9-859d-4fffb3eff0eb,2024-09-27,1.7895
iclr_icUCCz8pAu,2025,MultiTrust: Enhancing Safety and Trustworthiness of Large Language Models from Multiple Perspectives,"Chejian Xu, Dawn Song, Bo Li","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[5, 5, 5, 5]",5.0,"[2, 3, 2, 3]",2.5,"[3, 2, 3, 3]",2.75,"[3, 1, 1, 2]",1.75,"[4, 3, 4, 3]",3.5,"[""Large Language Models"", ""Safety"", ""Trustworthiness"", ""Robustness"", ""Fairness"", ""Truthfulness""]",1,17870503-35d9-4bb1-beb3-47d3e896a05e,2024-09-27,0.0526
iclr_iZI1vCiTTA,2025,Mechanistic Behavior Editing of Language Models,"Joykirat Singh, Subhabrata Dutta, Tanmoy Chakraborty","foundation or frontier models, including LLMs",reject,Rejected,"[5, 6, 3, 6]",5.0,"[2, 3, 3, 3]",2.75,"[3, 3, 2, 3]",2.75,"[2, 2, 2, 3]",2.25,"[2, 4, 3, 3]",3.0,"[""Mechanistic Intervention"", ""Bayesian Optimization""]",1,6661a68e-5508-48af-b586-011c59883928,2024-09-27,0.0526
iclr_iN7EIQRUbF,2025,Advancing Few-shot Continual Learning via Selective Knowledge Transfer,"Cuong N. Nguyen, Quang Pham, Cuong V. Nguyen, Xiaoli Li, Savitha Ramasamy","transfer learning, meta learning, and lifelong learning",reject,Rejected,"[5, 5, 5, 5]",5.0,"[2, 2, 2, 2]",2.0,"[3, 2, 2, 2]",2.25,"[2, 2, 2, 2]",2.0,"[3, 4, 4, 4]",3.75,"[""continual learning"", ""transfer learning""]",0,0020cd76-9ec3-4ed5-8ea2-4a3799bbd514,2024-09-21,0.0
iclr_iM7MfzbF1B,2025,MAGE: Leveraging LLMs for Automated Mapper Generation in Parallel Programming,"Anjiang Wei, Allen Nie, Thiago S. F. X. Teixeira, Rohan Yadav, Wonchan Lee, Ke Wang, Alex Aiken","foundation or frontier models, including LLMs",reject,Rejected,"[6, 6, 3, 5]",5.0,"[3, 3, 2, 3]",2.75,"[3, 3, 2, 3]",2.75,"[3, 3, 1, 2]",2.25,"[3, 3, 5, 4]",3.75,"[""large language models"", ""reinforcement learning"", ""domain-specific language"", ""discrete optimization"", ""performance optimization""]",0,faee090a-0ab6-4860-a838-da14d70d9893,2024-09-27,0.0
iclr_iINUF4n33F,2025,Text-Based Person Search in Full Images via Semantic Context Disentangling and Prototype Learning,"Canlong Zhang, ZengliLuo, Zhixin Li, Zhiwen Wang","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[3, 3, 3, 1]",2.5,"[2, 1, 2, 2]",1.75,"[2, 1, 2, 1]",1.5,"[1, 1, 2, 1]",1.25,"[4, 4, 4, 5]",4.25,"[""Cross-modal Retrieval;Text-based Person Search;Context Disentangling;Prototype Learning""]",0,53e405be-8b35-4ddd-b477-c49d6dfed617,2024-09-27,0.0
iclr_iGX0lwpUYj,2025,When to retrain a machine learning model,"Florence Regol, Leo Schwinn, Kyle Sprague, Mark Coates, Thomas Markovich","probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)",reject,Rejected,"[6, 3, 5, 6]",5.0,"[3, 2, 2, 2]",2.25,"[2, 2, 3, 2]",2.25,"[2, 1, 3, 3]",2.25,"[3, 4, 5, 3]",3.75,"[""retraining;sequence modeling; forecasting performance""]",3,,2024-09-25,0.1579
iclr_iEHYbGbZ4D,2025,DAS-GNN: Degree-Aware Spiking Graph Neural Networks for Graph Classification,"SunJong Park, Hyeyoon Lee, Kanghyun Choi, Dain Kwon, Jongkil Park, Seongsik Park, Jinho Lee",applications to neuroscience & cognitive science,reject,Rejected,"[8, 6, 5]",6.33,"[3, 3, 2]",2.67,"[3, 3, 2]",2.67,"[3, 3, 2]",2.67,"[4, 4, 4]",4.0,"[""Spiking Neural Network"", ""Graph Neural Network"", ""Graph Classification""]",0,1726e3c6-0203-4292-b664-032cb0b95424,2024-09-26,0.0
iclr_i4ouG6Kc8M,2025,A Dual-Metric Approach for Model Selection in self-supervised learning for histopathology,"Swaraj Nanda, Neeraj Kumar, Chad Vanderbilt","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[1, 3, 3, 3]",2.5,"[2, 2, 3, 3]",2.5,"[1, 2, 2, 2]",1.75,"[1, 2, 2, 1]",1.5,"[4, 4, 5, 4]",4.25,"[""Model selection"", ""Self-supervised Learning"", ""Histopathology"", ""Vision Transformer"", ""Deep Learning""]",0,9505b9c5-8fcd-449c-aa5b-59fc1cfdea25,2024-09-26,0.0
iclr_i3f2N3iHl0,2025,Adaptive Tensor Attention Networks with Cross-Domain Transfer for Drug-Target Interaction Prediction,"Zhenghan chen, youhuan yang, Lang Zheng, Ruxue Xing, Han Quan","transfer learning, meta learning, and lifelong learning",reject,Rejected,"[1, 1, 5, 3]",2.5,"[1, 1, 2, 2]",1.5,"[2, 1, 3, 2]",2.0,"[1, 1, 2, 2]",1.5,"[4, 4, 3, 5]",4.0,"[""Domain Adaptive Prediction\uff0cAttention""]",1,e0a54388-e597-4c10-8f68-74cc4fa768bd,2024-09-28,0.0526
iclr_i25WJWnsmq,2025,Optimizing Dynamic Treatment Strategies with Reinforcement Learning and Dual-Hawkes Process in Clinical Environments,"Yuyao Zhang, Ke Wan, Yifan Cui, Ruoqing Zhu",reinforcement learning,reject,Rejected,"[3, 3, 3]",3.0,"[2, 3, 1]",2.0,"[2, 2, 1]",1.67,"[2, 2, 1]",1.67,"[4, 5, 3]",4.0,"[""Reinforcement Learning""]",0,b0b21ccf-5cc2-4869-bcc9-bf0003a27d02,2024-09-23,0.0
iclr_i0VqD2KaYt,2025,ViT-UWA: Vision Transformer Underwater-Adapter for Dense Predictions Beneath the Water Surface,"Qirui LIN, Hua Li, Yuheng Jia, Yutong Li, Shijie Lian, Huazhong Liu, Sam Kwong, Runmin Cong","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[8, 6, 5, 6]",6.25,"[3, 3, 3, 3]",3.0,"[3, 2, 2, 3]",2.5,"[3, 2, 1, 3]",2.25,"[4, 4, 4, 5]",4.25,"[""Underwater Image Dense Prediction"", ""Adapted ViT Backbone""]",2,a61eb84b-b294-4f39-b485-a22f62bc4098,2024-09-23,0.1053
iclr_hZ3QE0rUt1,2025,How to distill task-agnostic representations from many teachers?,"Philippe Formont, Maxime DARRIN, Banafsheh Karimian, Loïc Fosse, Eric Granger, Ismail Ben Ayed, Jackie CK Cheung, Mohammadhadi Shateri, Pablo Piantanida","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[3, 5, 6, 6]",5.0,"[3, 2, 3, 3]",2.75,"[2, 2, 3, 3]",2.5,"[2, 3, 3, 3]",2.75,"[4, 4, 4, 4]",4.0,"[""knowledge distillation"", ""representation learning"", ""natural language processing"", ""molecular modeling"", ""computer vision"", ""embedding models""]",0,b5d62a33-7954-4762-9842-b361eafd198a,2024-09-27,0.0
iclr_hYd6BCZTzg,2025,Revisit Self-Debugging with Self-Generated Tests for Code Generation,"Xiancai Chen, Zhengwei Tao, Kechi Zhang, Changzhi Zhou, Wanli Gu, Yuanpeng He, Mengdi Zhang, Xunliang Cai, Haiyan Zhao, Zhi Jin",generative models,reject,Rejected,"[5, 6, 6, 8]",6.25,"[3, 3, 3, 3]",3.0,"[2, 3, 3, 4]",3.0,"[2, 2, 3, 3]",2.5,"[3, 3, 4, 4]",3.5,"[""self-debugging"", ""code generation"", ""code reasoning"", ""large language models""]",17,5e62a8e5-4944-40f9-a5b0-6a0257526b1c,2024-09-26,0.8947
iclr_hQ2TUZmse1,2025,Refining Counterfactual Explanations With Joint-Distribution-Informed Shapley Towards Actionable Minimality,"Lei You, Yijun Bian, Lele Cao",interpretability and explainable AI,reject,Rejected,"[8, 6, 6, 8]",7.0,"[3, 2, 3, 3]",2.75,"[3, 3, 3, 3]",3.0,"[3, 3, 1, 3]",2.5,"[4, 3, 5, 3]",3.75,"[""Explainable artificial Intelligence"", ""Feature attributions"", ""Counterfactual explanations""]",2,0327f635-0796-44d3-b851-718b9ced4ee4,2024-09-25,0.1053
iclr_hMjUnF3aQ8,2025,SQT -- rough conservative actor critic,"Nitsan Soffair, Gilad Katz, Dotan Di Castro, Orly Avner, Shie Mannor",reinforcement learning,reject,Rejected,"[1, 3, 1, 3]",2.0,"[2, 3, 1, 1]",1.75,"[1, 2, 2, 2]",1.75,"[1, 3, 2, 2]",2.0,"[4, 4, 5, 2]",3.75,"[""Actor Critic"", ""Overestimation Bias""]",0,61ac8f13-0f6c-462e-a8bd-e4f198d3a28e,2024-09-13,0.0
iclr_hKMPz3wkPV,2025,Towards a formal theory of compositionality,"Eric Elmoznino, Thomas Jiralerspong, Yoshua Bengio, Guillaume Lajoie","other topics in machine learning (i.e., none of the above)",reject,Rejected,"[8, 6, 5, 8]",6.75,"[3, 1, 4, 4]",3.0,"[3, 2, 2, 4]",2.75,"[3, 3, 3, 3]",3.0,"[3, 3, 4, 4]",3.5,"[""compositionality"", ""complexity"", ""deep learning"", ""representation"", ""generalization""]",0,8967f114-99a5-413f-95cd-d56dc25d7596,2024-09-13,0.0
iclr_hIKsem01M5,2025,Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation,"Yutong He, Alexander Robey, Naoki Murata, Yiding Jiang, Joshua Nathaniel Williams, George J. Pappas, Hamed Hassani, Yuki Mitsufuji, Russ Salakhutdinov, J Zico Kolter","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[5, 5, 5, 5]",5.0,"[4, 3, 3, 3]",3.25,"[3, 3, 3, 2]",2.75,"[1, 3, 3, 2]",2.25,"[5, 3, 4, 3]",3.75,"[""Text-to-Image Generation"", ""Prompt Engineering"", ""Personalized Text-to-Image Generation""]",15,94a5b8a7-7e0f-44c4-b1da-877c507f9094,2024-09-25,0.7895
iclr_gzmInLJSoW,2025,Towards Infinite-Long Prefix in Transformer,"Yingyu Liang, Zhenmei Shi, Zhao Song, Chiwun Yang",learning theory,reject,Rejected,"[6, 6, 5, 3]",5.0,"[3, 3, 3, 3]",3.0,"[2, 3, 2, 2]",2.25,"[2, 3, 3, 2]",2.5,"[2, 5, 2, 4]",3.25,"[""Large Language Model"", ""Prefix Learning"", ""Neural Tangent Kernel""]",30,a5503c0e-46f5-43da-b83b-94e7db3924e2,2024-09-24,1.5789
iclr_gpKEDj9Dgg,2025,Optimizing Large Language Models with Automatic Speech Recognition for Medication Corpus in Low-Resource Healthcare Settings.,Abdulhameed Abiola Dere,generative models,reject,Rejected,"[1, 1, 5, 1]",2.0,"[1, 1, 1, 1]",1.0,"[1, 1, 2, 1]",1.25,"[1, 1, 1, 2]",1.25,"[5, 5, 4, 4]",4.5,"[""Automatic Speech Recognition"", ""Large Language Models"", ""Healthcare"", ""Low Resource Settings""]",0,0dbd0a5c-d534-4547-848b-d4ea63eb51d2,2024-09-15,0.0
iclr_gnexAe3kjx,2025,Quantum Neural Fields,"Shuteng Wang, Christian Theobalt, Vladislav Golyanik","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[5, 8, 1, 6]",5.0,"[2, 4, 1, 1]",2.0,"[2, 3, 1, 3]",2.25,"[3, 4, 1, 3]",2.75,"[3, 3, 5, 3]",3.5,"[""quantum neural fields"", ""representation learning"", ""Neuro-deterministic data encoding"", ""quantum ansatz""]",934,0201af93-3d11-45ec-a724-3c288dfe05ba,2024-09-13,49.1579
iclr_gjC3QvVh1U,2025,AlphaZero Neural Scaling and Zipf's Law: a Tale of Board Games and Power Laws,"Oren Neumann, Claudius Gros",reinforcement learning,reject,Rejected,"[6, 5, 6, 8]",6.25,"[3, 2, 3, 3]",2.75,"[4, 2, 3, 3]",3.0,"[2, 2, 2, 3]",2.25,"[4, 2, 4, 3]",3.25,"[""Scaling Laws"", ""Reinforcement Learning"", ""Zipf's Law""]",6,97514f61-e18e-4d9f-814a-59ab203a4a9c,2024-09-24,0.3158
iclr_ghk8lnOYRq,2025,Solving the 2-norm k-hyperplane clustering problem via multi-norm formulations,Stefano Coniglio,optimization,reject,Rejected,"[5, 5, 5, 5]",5.0,"[2, 2, 3, 2]",2.25,"[3, 2, 3, 2]",2.5,"[2, 2, 3, 2]",2.25,"[2, 4, 4, 4]",3.5,"[""hyperplane clustering; mathematical programming; spatial branch and bound""]",0,32f998e2-4945-404b-9b25-a13bf09e04ff,2024-09-28,0.0
iclr_gbJNFxcicC,2025,Mask R-CNN for Automated Multi-Species Malaria Parasite Detection,"Eugenia Mawuenya Akpo, Carine Mukamakuza","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[1, 3, 1]",1.67,"[2, 3, 2]",2.33,"[2, 2, 2]",2.0,"[1, 1, 1]",1.0,"[5, 5, 5]",5.0,"[""Mask R-CNN"", ""malaria parasite detection"", ""Plasmodium species"", ""deep learning"", ""instance segmentation"", ""microscopic image analysis""]",0,aa858484-a57f-4fb8-8405-11fdb51ad54d,2024-09-27,0.0
iclr_gaa7gWPZBz,2025,Mitigating Privacy Risk of Adversarial Examples with Counterfactual Explanations,"Aohan Sun, Yanrong Lu, ATHANASIOS V. VASILAKOS",interpretability and explainable AI,reject,Rejected,"[3, 3, 3]",3.0,"[2, 1, 1]",1.33,"[2, 1, 2]",1.67,"[2, 1, 2]",1.67,"[3, 4, 4]",3.67,"[""Adversarial Examples"", ""Privacy"", ""Counterfactual Explanations""]",0,5f887ab4-983a-469e-b4c2-7da5f2dae264,2024-09-28,0.0
iclr_ga4LyaucKr,2025,"Learning-based Mechanism Design: Scalable, Truthful, and Continuum Approaches for Utility Maximization","Yunxuan Ma, Siqiang Wang, Zhijian Duan, Xiaotie Deng","other topics in machine learning (i.e., none of the above)",reject,Rejected,"[1, 3, 3, 3]",2.5,"[1, 2, 2, 1]",1.5,"[2, 2, 1, 2]",1.75,"[1, 2, 2, 2]",1.75,"[5, 5, 4, 4]",4.5,"[""automated mechanism design"", ""differential economics"", ""function approximation"", ""mechanism representation""]",0,2efd09f8-6703-42e7-91db-757995c03cd8,2024-09-25,0.0
iclr_ga1sPJen12,2025,Craftium: Creating Efficient Environments for Open-Ended and Embodied Agents Beyond Gridworlds,"Mikel Malagón, Josu Ceberio, Jose A. Lozano",reinforcement learning,reject,Rejected,"[8, 3, 8, 6]",6.25,"[3, 2, 4, 3]",3.0,"[4, 3, 4, 3]",3.5,"[3, 2, 2, 2]",2.25,"[4, 4, 4, 2]",3.5,"[""reinforcement learning"", ""environment"", ""embodied"", ""open-ended"", ""continual learning"", ""meta reinforcement learning""]",0,813c8c9e-cfdc-48e0-8977-4df97ab5515b,2024-09-16,0.0
iclr_gZky2pakRK,2025,HAICOSYSTEM: An Ecosystem for Sandboxing Safety Risks in Human-AI Interactions,"Xuhui Zhou, Hyunwoo Kim, Faeze Brahman, Liwei Jiang, Hao Zhu, Ximing Lu, Frank F. Xu, Bill Yuchen Lin, Yejin Choi, Niloofar Mireshghallah, Ronan Le Bras, Maarten Sap","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[3, 8, 6, 8]",6.25,"[4, 4, 2, 4]",3.5,"[1, 4, 3, 2]",2.5,"[1, 4, 3, 3]",2.75,"[4, 4, 3, 3]",3.5,"[""AI Safety"", ""Multi-Agent Systems"", ""Human-AI Interaction"", ""Social Simulation""]",19,63d3378a-a8ee-424a-8770-5c5cf9e66ddc,2024-09-27,1.0
iclr_gXK3Y6WNVv,2025,Defects4C: Benchmarking C/C++ Faults to Assess LLM-Based Program Repair,"Jian Jornbowrl Wang, Xiaofei Xie, Shangqing Liu, Jiaolong Kong, Jiongchi Yu, Yi Li",datasets and benchmarks,reject,Rejected,"[6, 6, 5, 3]",5.0,"[2, 3, 3, 2]",2.5,"[3, 3, 3, 2]",2.75,"[3, 3, 2, 1]",2.25,"[4, 5, 3, 5]",4.25,"[""Defects4C; Large Language Model; Program Repair""]",0,484b5ff8-e009-466a-a74f-84ce3ace98df,2024-09-27,0.0
iclr_gW4bdLwypB,2025,Objective Soups: Multilingual Multi-Task Acoustic Modeling for Automatic Speech Recognition,"A F M Saif, Lisha Chen, Xiaodong Cui, Songtao Lu, Brian Kingsbury, Tianyi Chen","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[6, 5, 10, 5]",6.5,"[2, 3, 4, 2]",2.75,"[3, 3, 4, 3]",3.25,"[3, 3, 4, 2]",3.0,"[4, 4, 4, 4]",4.0,"[""multilingual speech recognition"", ""speech-to-text translation"", ""multi-objective optimization"", ""multi-task learning"", ""semi-supervised training""]",0,d00fdb22-ba24-4c1c-b2cb-3d8d6f13d287,2024-09-25,0.0
iclr_gVbPYihQag,2025,Stochastic Diffusion: A Diffusion Based Model for Stochastic Time Series Forecasting,"Yuansan Liu, Sudanthi Wijewickrema, Dongting Hu, Christofer Bester, Stephen O'Leary, James Bailey",learning on time series and dynamical systems,reject,Rejected,"[5, 6, 6, 3]",5.0,"[2, 3, 3, 2]",2.5,"[2, 3, 3, 1]",2.25,"[2, 2, 2, 2]",2.0,"[3, 4, 3, 4]",3.5,"[""diffusion probabilistic model"", ""stochastic time series forecasting"", ""data-driven prior""]",6,da643d1c-c98b-4279-8442-cd74f5548e2c,2024-09-25,0.3158
iclr_gVVoZtiQlt,2025,The Phase Transition Phenomenon of Shuffled Regression,"Hang Zhang, Ping Li",learning theory,reject,Rejected,"[3, 8, 6, 3]",5.0,"[2, 2, 3, 2]",2.25,"[1, 3, 3, 2]",2.25,"[1, 3, 2, 2]",2.0,"[2, 1, 2, 4]",2.25,"[""Message Passing"", ""Permuted Linear Regression"", ""Phase Transition""]",1,d5a661b7-ac9e-4297-bc76-757dfe247254,2024-09-28,0.0526
iclr_gInIbukM0R,2025,Quantifying Emergence in Neural Networks: Insights from Pruning and Training Dynamics,"Faisal Saleh Alshinaifi, Zeyad Almoaigel, Johnny Jingze Li, Abdulla Kuleib, Gabriel A. Silva",learning theory,reject,Rejected,"[3, 3, 3, 1]",2.5,"[2, 2, 1, 1]",1.5,"[1, 2, 1, 2]",1.5,"[1, 1, 2, 2]",1.5,"[3, 3, 3, 4]",3.25,"[""Emergence"", ""training dynamics"", ""pruning"", ""landscape""]",0,02c7c0c5-59c6-4a52-9b17-0e99de87d7bf,2024-09-26,0.0
iclr_gFUomIaycw,2025,Dynamic Routing Mixture of Experts for Enhanced Multi-Label Image Classification,Ashish Dubey,"applications to computer vision, audio, language, and other modalities",reject,Rejected,"[1, 3, 3, 3]",2.5,"[1, 3, 2, 2]",2.0,"[2, 2, 2, 1]",1.75,"[1, 2, 1, 2]",1.5,"[5, 5, 3, 4]",4.25,"[""Multi-label image classification"", ""Dynamic Routing Mixture of Experts"", ""Computer vision"", ""Dynamic gating networks"", ""Label heterogeneity""]",0,ca7e35af-2de0-40aa-88bb-dbc3a87463d2,2024-09-28,0.0
iclr_g3PuaFh5vV,2025,Non-invasive Neural Decoding in Source Reconstructed Brain Space,"Yonatan Gideoni, Ryan Charles Timms, Oiwi Parker Jones",applications to neuroscience & cognitive science,reject,Rejected,"[3, 3, 3, 1]",2.5,"[2, 2, 2, 3]",2.25,"[2, 2, 2, 1]",1.75,"[2, 2, 1, 1]",1.5,"[3, 3, 4, 5]",3.75,"[""Neural decoding"", ""MEG"", ""brain decoding"", ""structured learning"", ""brain computer interface""]",4,8839a08a-7a52-4a23-9608-bb764b1b8dd7,2024-09-27,0.2105
iclr_g0Doz4IRHU,2025,FedCVD: The First Real-World Federated Learning Benchmark on Cardiovascular Disease Data,"Yukun Zhang, Guanzhong Chen, Zenglin Xu, Jianyong Wang, Dun Zeng, Junfan Li, Jinghua Wang, Yuan Qi, Irwin King",datasets and benchmarks,reject,Rejected,"[6, 5, 3, 6]",5.0,"[3, 3, 4, 2]",3.0,"[3, 3, 4, 3]",3.25,"[3, 2, 2, 3]",2.5,"[4, 4, 5, 3]",4.0,"[""Federated Learning"", ""Healthcare Data"", ""Cardiovascular Diseases"", ""Real-world Datasets"", ""Data Heterogeneity""]",7,375d4317-40cb-4ea9-9072-083e25e1e574,2024-09-26,0.3684
iclr_fy4rCv3s5i,2025,Neural Lighting Priors for Indoor Scenes,"Peter Kocsis, Vincent Sitzmann, Matthias Nießner","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[3, 6, 6, 5]",5.0,"[2, 3, 3, 2]",2.5,"[2, 3, 2, 2]",2.25,"[2, 3, 3, 2]",2.5,"[5, 4, 3, 5]",4.25,"[""lighting representation"", ""prior learning"", ""neural field"", ""3D"", ""computer graphics""]",0,209883d3-704d-43a6-bf86-f3426464ab1a,2024-09-26,0.0
iclr_fk4czNKXPC,2025,Transformers meet Neural Algorithmic Reasoners,"Wilfried Bounsi, Borja Ibarz, Andrew Joseph Dudzik, Jessica B Hamrick, Larisa Markeeva, Alex Vitvitskyi, Razvan Pascanu, Petar Veličković",learning on graphs and other geometries & topologies,reject,Rejected,"[5, 6, 3, 6]",5.0,"[2, 3, 2, 3]",2.5,"[2, 3, 2, 3]",2.5,"[2, 3, 2, 3]",2.5,"[4, 4, 4, 3]",3.75,"[""Graph Neural Network"", ""Transformer"", ""Neural Algorithmic Reasoning"", ""Length Generalization""]",23,e7caf90d-474c-4db5-8ab3-57619f9de6ee,2024-09-27,1.2105
iclr_feFlfuOse1,2025,Gymnasium: A Standard Interface for Reinforcement Learning Environments,"Ariel Kwiatkowski, Mark Towers, J K Terry, John U. Balis, Gianluca De Cola, Tristan Deleu, Manuel Goulão, Kallinteris Andreas, Markus Krimmel, Arjun KG, Rodrigo De Lazcano Perez-Vicente, Andrea Pierré, Sander V Schulhoff, Jun Jet Tai, Hannah Tan, Omar G. Younis","infrastructure, software libraries, hardware, systems, etc.",reject,Rejected,"[5, 10, 6, 8]",7.25,"[4, 4, 3, 2]",3.25,"[2, 4, 4, 4]",3.5,"[2, 4, 2, 4]",3.0,"[3, 5, 2, 5]",3.75,"[""reinforcement learning"", ""api"", ""gymnasium"", ""artificial intelligence"", ""autonomous agents"", ""environment""]",893,67a55136-84c1-43d1-85b0-2180b6776e41,2024-09-25,47.0
iclr_fdvSCcB7i8,2025,Feature Level Instance Attribution,"Zhiyu Zhu, Jiayu Zhang, Xinyi Zhang, Zhibo Jin, Jiahao Huang, Jianlong Zhou, Fang Chen",interpretability and explainable AI,reject,Rejected,"[3, 3, 3]",3.0,"[2, 2, 2]",2.0,"[3, 1, 2]",2.0,"[2, 3, 2]",2.33,"[4, 3, 4]",3.67,"[""Interpretability"", ""attribution""]",53,6e551128-2a1b-4870-8641-325d9a5c206b,2024-09-19,2.7895
iclr_fSbPwHjdDG,2025,Llamas (mostly) think in English: On Causal Interventions in the Latent Language of Transformers,"David Quarel, Marcus Hutter",interpretability and explainable AI,reject,Rejected,"[5, 1, 3]",3.0,"[1, 1, 3]",1.67,"[3, 2, 2]",2.33,"[2, 2, 2]",2.0,"[4, 4, 4]",4.0,"[""mechanistic interpretability"", ""large language models"", ""transformers"", ""residual stream""]",0,104dbf91-1fb1-4da8-804f-7f3b8e582374,2024-09-28,0.0
iclr_fRNDDFkPiv,2025,Controlling Forgetting with Test-Time Data in Continual Learning,"Vaibhav Singh, Rahaf Aljundi, Eugene Belilovsky","transfer learning, meta learning, and lifelong learning",reject,Rejected,"[8, 8, 6, 5]",6.75,"[4, 1, 2, 3]",2.5,"[4, 3, 2, 3]",3.0,"[3, 3, 3, 3]",3.0,"[4, 4, 4, 4]",4.0,"[""Continual Learning"", ""Test Time Learning""]",6,efabc225-12ec-4032-ae3f-eb099e1eac30,2024-09-27,0.3158
iclr_fMaEbeJGpp,2025,Multimodal Retrieval-Augmented Generation Question-Answering System,Hanliang Chen,"foundation or frontier models, including LLMs",reject,Rejected,"[3, 3, 1, 3]",2.5,"[1, 1, 1, 1]",1.0,"[2, 2, 1, 2]",1.75,"[1, 1, 1, 1]",1.0,"[4, 4, 5, 5]",4.5,"[""Retrieval-Augmented Generation; Dataset Construction; Text-Image Retrieval; Visual Question-Answering System""]",1,37233291-ed94-450c-b757-05aac20a7c44,2024-09-23,0.0526
iclr_fHqwCsDK1z,2025,Conditioning on Time is All You Need for Synthetic Survival Data Generation,"Mohd Ashhad, Ricardo Henao",generative models,reject,Rejected,"[3, 3, 3]",3.0,"[2, 3, 2]",2.33,"[1, 2, 1]",1.33,"[1, 2, 1]",1.33,"[5, 4, 3]",4.0,"[""Survival Data Generation"", ""Survival Analysis"", ""Tabular Data Generation"", ""Generative Modeling""]",2,0f483431-a8de-4e7f-bf59-a9cde3466ce0,2024-09-26,0.1053
iclr_fBkdjUnymd,2025,Martryoshka: Learning to Drive Black-Box LLMs with LLMs,"ChangHao Li, Yuchen Zhuang, Rushi Qiang, Haotian Sun, Hanjun Dai, Chao Zhang, Bo Dai","foundation or frontier models, including LLMs",reject,Rejected,"[3, 6, 6, 5]",5.0,"[2, 3, 3, 2]",2.5,"[2, 2, 3, 2]",2.25,"[1, 2, 2, 2]",1.75,"[5, 3, 3, 4]",3.75,"[""Large Language Model"", ""Reasoning and Planning"", ""LLM Controller""]",8,585a32df-04ae-4d27-baed-1213d7c8298d,2024-09-27,0.4211
iclr_f7VXdQTbyW,2025,ThreadsGAN: Enhancing Coherence and Diversity in Discussion Thread Generation,"Yamien Cheng, Jheng-Long Wu",generative models,reject,Rejected,"[1, 1, 3, 3]",2.0,"[1, 1, 2, 1]",1.25,"[2, 1, 1, 1]",1.25,"[1, 1, 1, 2]",1.25,"[4, 5, 4, 4]",4.25,"[""discussion threads"", ""generative adversarial network"", ""natural language generating""]",0,9560d1eb-929a-42be-a503-cf0f0c37448d,2024-09-27,0.0
iclr_f6GMwpxXHG,2025,ZEPHYR GAN: REDEFINING GAN WITH FLEXIBLE GRADIENT CONTROL,"Anuradha Kumari, Ritik Mishra, M. Tanveer",generative models,reject,Rejected,"[1, 3, 3, 3, 1]",2.2,"[2, 3, 2, 2, 2]",2.2,"[1, 2, 3, 2, 1]",1.8,"[1, 1, 2, 2, 1]",1.4,"[4, 3, 4, 5, 5]",4.2,"[""Generative Adversarial Network"", ""Zephyr loss"", ""Adversarial Training"", ""Flexible Gradient Control.""]",0,c0493bca-64a2-43f8-8c06-c169c98b743b,2024-09-28,0.0
iclr_eq8jOD0tgl,2025,Multi-Task Best Arm Identification with Risk Constraint,"Mingjie Hu, Jianqiang Hu",reinforcement learning,reject,Rejected,"[6, 6, 5, 8]",6.25,"[3, 3, 1, 4]",2.75,"[3, 3, 3, 4]",3.25,"[2, 3, 2, 4]",2.75,"[2, 3, 4, 3]",3.0,"[""Multi-Task\uff0cBest Arm Identification\uff0cRisk Constraint\uff0c Fixed Confidence""]",2,0c1061e8-55c4-450c-9d4f-96bc77dc25f8,2024-09-27,0.1053
iclr_eciCtsqGc8,2025,Interpretable Pre-Trained Transformers for Heart Time-Series Data,"Harry J Davies, James Monsen, Danilo Mandic",interpretability and explainable AI,reject,Rejected,"[6, 8, 8]",7.33,"[3, 2, 4]",3.0,"[3, 3, 3]",3.0,"[3, 3, 3]",3.0,"[3, 5, 4]",4.0,"[""biosignals"", ""interpretability"", ""healthcare"", ""transformers""]",9,f47da04a-9909-464e-b56c-cdba3dde917a,2024-09-25,0.4737
iclr_ech9J3xl9X,2025,Narrow Transformer: Mono-lingual Code SLM for Desktop,"Kamalkumar Rathinasamy, Balaji A J, Ankush Kumar, Gagan Gayari, Harshini K, Rajab Ali Mondal, Sreenivasa Raghavan K S, Swayam Singh, Mohammed Rafee Tarafdar",generative models,reject,Rejected,"[1, 3, 3, 3]",2.5,"[2, 2, 2, 3]",2.25,"[3, 2, 2, 3]",2.5,"[1, 1, 1, 1]",1.0,"[5, 4, 5, 5]",4.75,"[""Narrow Transformer"", ""Code SLMs"", ""Desktop Deployment"", ""Lightweight Code Language Models"", ""Small Language Models"", ""Language Specific Models"", ""Monolingual Code Language Model""]",0,fed20d11-30ce-4ee0-9dd1-695b70c68bb0,2024-09-26,0.0
iclr_eYcK7lzlOi,2025,Unleashing Graph Transformers with Green and Martin Kernels,"Yoon Hyeok Lee, Jaemin Park, Taejin Paik, Doyun Kim, Bosun Hwang",learning on graphs and other geometries & topologies,reject,Rejected,"[8, 8, 5, 5]",6.5,"[3, 3, 4, 3]",3.25,"[3, 3, 2, 4]",3.0,"[3, 3, 2, 2]",2.5,"[3, 3, 5, 3]",3.5,"[""Graph Transformers"", ""Graph Neural Networks"", ""Structural Encodings"", ""Green Kernel"", ""Martin Kernel"", ""Non-aperiodic substructures"", ""DAGs""]",0,af0b0083-6812-47db-b105-2564fa73336b,2024-09-27,0.0
iclr_eNCyY81aW6,2025,FACTOR: Factoring Complexity and Context Length in Long-Context Model Evaluation,"Hongyi Liu, Zhuoming Chen, Yang Zhou, Beidi Chen",datasets and benchmarks,reject,Rejected,"[3, 6, 3, 8]",5.0,"[3, 3, 3, 4]",3.25,"[2, 2, 2, 4]",2.5,"[2, 2, 2, 4]",2.5,"[3, 4, 3, 4]",3.5,"[""Long-context reasoning"", ""Language models""]",0,e00dbace-f33b-4d4d-8b39-ac6780a7d822,2024-09-26,0.0
iclr_eN0RyRVbSm,2025,Double Descent Meets Out-of-Distribution Detection: Theoretical Insights and Empirical Analysis of the role of model complexity,"Mouin Ben Ammar, David Brellmann, Arturo Mendoza, Antoine Manzanera, Gianni Franchi",learning theory,reject,Rejected,"[8, 6, 5, 6]",6.25,"[3, 3, 3, 3]",3.0,"[3, 3, 2, 2]",2.5,"[3, 2, 2, 2]",2.25,"[4, 4, 2, 3]",3.25,"[""Out-Of-Distribution"", ""double descent.""]",0,98fa0adb-dcb0-4517-ac7e-1156a8834eec,2024-09-23,0.0
iclr_eI3hEAWe8W,2025,Dialogue Action Tokens: Steering Language Models in Goal-Directed Dialogue with a Multi-Turn Planner,"Kenneth Li, Yiming Wang, Fernanda Viégas, Martin Wattenberg","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[5, 6, 8, 8, 5]",6.4,"[2, 3, 3, 3, 2]",2.6,"[2, 3, 3, 3, 2]",2.6,"[2, 3, 3, 4, 2]",2.8,"[4, 3, 4, 3, 4]",3.6,"[""Large Language Model"", ""Dialogue Systems"", ""Social Intelligence"", ""Red Teaming""]",10,28e7ea7e-ed57-4c9c-9da0-197943e8f134,2024-09-27,0.5263
iclr_e92KW6htFO,2025,MICE: Memory-driven Intrinsic Cost Estimation for Mitigating Constraint Violations,"Shiqing Gao, Jiaxin Ding, Luoyi Fu, Xinbing Wang, Chenghu Zhou",reinforcement learning,reject,Rejected,"[6, 5, 3, 6]",5.0,"[3, 3, 2, 2]",2.5,"[3, 2, 2, 3]",2.5,"[2, 2, 2, 3]",2.25,"[4, 3, 4, 3]",3.5,"[""reinforcement learning"", ""constraint optimization"", ""underestimation"", ""intrinsic cost""]",0,084c3849-920c-4617-8967-fa6fac26bfcb,2024-09-26,0.0
iclr_e2F0mJJeN0,2025,Geometric Median (GM) Matching for Robust Data Pruning,"Anish Acharya, Inderjit S Dhillon, Sujay Sanghavi",datasets and benchmarks,reject,Rejected,"[3, 3, 3]",3.0,"[3, 3, 1]",2.33,"[3, 3, 1]",2.33,"[1, 2, 1]",1.33,"[4, 4, 5]",4.33,"[""data pruning"", ""robust"", ""data selection""]",3,8f845e1d-2236-4d42-9f0e-62a4640fea65,2024-09-17,0.1579
iclr_dxMffCAd4w,2025,CLF: Curve Line Fitting Neural Network Based On Bezier Curve,"Jianyi Yang, Guiling Wang",interpretability and explainable AI,reject,Rejected,"[3, 3, 1, 3]",2.5,"[3, 2, 1, 2]",2.0,"[2, 2, 1, 3]",2.0,"[1, 2, 1, 2]",1.5,"[4, 3, 5, 3]",3.75,"[""CLF"", ""MLP"", ""Interpretable Neural Networks""]",0,2552a50c-2353-4589-972d-e48fb9d9e3aa,2024-09-27,0.0
iclr_dugoA2gfhs,2025,Just Select Twice: Leveraging Low Quality Data to Improve Data Selection,"Yifei Zhang, Yusen Jiao, Jiayi Chen, Zhaoyang Li, Huaxiu Yao, Jieyu Zhang, Frederic Sala","other topics in machine learning (i.e., none of the above)",reject,Rejected,"[6, 8, 3, 3]",5.0,"[4, 3, 2, 2]",2.75,"[3, 3, 2, 2]",2.5,"[3, 3, 2, 2]",2.5,"[5, 3, 4, 4]",4.0,"[""data selection"", ""data valuation"", ""data-centric AI"", ""optimal transport"", ""robust statistics""]",0,be5ed5d3-1402-4744-8bed-0db42dfa31e4,2024-09-27,0.0
iclr_dug02AimLZ,2025,Second-Order Algorithms for Finding Local Nash Equilibria in Zero-Sum Games,"Kushagra Gupta, Xinjie Liu, Ross Emerson Allen, ufuk topcu, David Fridovich-Keil",optimization,reject,Rejected,"[8, 6, 5, 6]",6.25,"[3, 2, 3, 3]",2.75,"[3, 3, 2, 3]",2.75,"[3, 3, 2, 3]",2.75,"[3, 3, 3, 3]",3.0,"[""game theory"", ""nonconvex-nonconcave optimization"", ""dynamical systems"", ""Nash equilibrium""]",6,e4c2b7e4-096f-443d-9d60-15a1d27e0849,2024-09-27,0.3158
iclr_drrXhD2r8V,2025,Structure-Aware Parameter-Efficient Machine Unlearning on Transformer Models,"Wenjie Bao, Jian Lou, Yuke Hu, Xiaochen Li, Zhihao Liu, Jiaqi Liu, Zhan Qin, Kui Ren","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[5, 6, 6, 3]",5.0,"[2, 3, 4, 2]",2.75,"[3, 2, 3, 2]",2.5,"[2, 2, 3, 2]",2.25,"[3, 3, 3, 4]",3.25,"[""Machine Unlearning"", ""Parameter-Efficient"", ""Transformer""]",0,696cd3fb-9cc0-4afd-a3e7-84add6a88625,2024-09-28,0.0
iclr_dp1BH2bK4Y,2025,"Re-TASK: Revisiting LLM Tasks from Capability, Skill, and Knowledge Perspectives","Zhihu Wang, Shiwan Zhao, Yu Wang, Heyuan Huang, Sitao Xie, Yubo Zhang, Jiaxin Shi, Zhixing Wang, Hongyan Li, Junchi Yan","foundation or frontier models, including LLMs",reject,Rejected,"[3, 3, 3]",3.0,"[2, 2, 2]",2.0,"[2, 2, 3]",2.33,"[2, 2, 1]",1.67,"[4, 3, 3]",3.33,"[""LLM"", ""Task"", ""Capability"", ""Knowledge"", ""Skill"", ""Chain-of-Thought""]",21,6a5e4169-57da-41f5-8489-e1845e1284c5,2024-09-25,1.1053
iclr_dgb4rfPzaw,2025,World-simulation as pre-training for scalable perception,"Hao Xiang, Zhaoqi Leng, Alex Zihao Zhu, Kan Chen, Mengtian Li, Xia Chen, Yingwei Li, Tong He, Yanhui Liang, Junwen Yao, Yan Xu, Anant Subramanian, Cheolho Park, Runsheng Xu, Dragomir Anguelov, Mingxing Tan","applications to robotics, autonomy, planning",reject,Rejected,"[5, 5, 5, 5]",5.0,"[3, 3, 3, 2]",2.75,"[3, 3, 2, 2]",2.5,"[3, 2, 2, 2]",2.25,"[3, 3, 4, 4]",3.5,"[""autonomous driving; computer vision; autoregressive transformer; self-supervised learning""]",0,d5c29538-d693-416c-820b-3777c9649812,2024-09-28,0.0
iclr_dd0rUW29tQ,2025,GeNIe: Generative Hard Negative Images Through Diffusion,"Soroush Abbasi Koohpayegani, Anuj Singh, Navaneet K L, Hamed Pirsiavash, Hadi Jamali-Rad","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[5, 6, 6, 8]",6.25,"[3, 3, 2, 3]",2.75,"[3, 3, 2, 3]",2.75,"[2, 2, 2, 3]",2.25,"[4, 5, 4, 4]",4.25,"[""Data Augmentation;Diffusion Models; Computer Vision; Few-shot Learning; Long-tail Classification;""]",5,fc7b6dfa-bef6-4025-941f-d76d9a5fd971,2024-09-26,0.2632
iclr_dbwF3QFWGn,2025,Stochastic Online Conformal Prediction with Semi-Bandit Feedback,"Haosen Ge, Hamsa Bastani, Osbert Bastani","other topics in machine learning (i.e., none of the above)",reject,Rejected,"[5, 5, 5, 5]",5.0,"[2, 2, 2, 2]",2.0,"[3, 3, 2, 2]",2.5,"[2, 2, 2, 2]",2.0,"[4, 3, 3, 2]",3.0,"[""Conformal Predictions"", ""Online Learning"", ""Semi-bandit Feedback""]",6,4404ff15-5993-4022-8ff0-a6c0b2e609da,2024-09-27,0.3158
iclr_dbiLOMgMm7,2025,Early learning of the optimal constant solution in neural networks and humans,"Jirko Rubruck, Jan Philipp Bauer, Andrew M Saxe, Christopher Summerfield",applications to neuroscience & cognitive science,reject,Rejected,"[6, 6, 8, 5]",6.25,"[3, 2, 3, 1]",2.25,"[3, 3, 4, 3]",3.25,"[2, 3, 4, 2]",2.75,"[4, 3, 4, 3]",3.5,"[""Simplicity Bias"", ""Deep Linear Networks"", ""cognitive science"", ""neuroscience"", ""learning dynamics""]",3,22cf2121-18ed-472c-bea3-6be4c3135950,2024-09-26,0.1579
iclr_daByonGVyo,2025,Compression via Pre-trained Transformers: A Study on Byte-Level Multimodal Data,"David Heurtel-Depeiges, Anian Ruoss, Joel Veness, Tim Genewein","other topics in machine learning (i.e., none of the above)",reject,Rejected,"[3, 6, 6, 5]",5.0,"[4, 3, 4, 3]",3.5,"[3, 3, 2, 3]",2.75,"[2, 2, 3, 2]",2.25,"[4, 4, 4, 3]",3.75,"[""lossless compression"", ""transformers"", ""multi-modality""]",11,462a028b-6c9e-4e9f-8793-c89aa4cb8d05,2024-09-25,0.5789
iclr_dTQmayPKMs,2025,Understanding Impact of Human Feedback via Influence Functions,"Taywon Min, Haeone Lee, Hanho Ryu, Yongchan Kwon, Kimin Lee","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[6, 8, 5, 6, 8, 5]",6.33,"[3, 3, 3, 3, 4, 3]",3.17,"[3, 3, 3, 3, 4, 3]",3.17,"[3, 3, 1, 3, 2, 2]",2.33,"[3, 5, 3, 2, 2, 4]",3.17,"[""reinforcement learning from human feedback"", ""influence function"", ""reward learning"", ""alignment"", ""scalable oversight""]",18,8617935c-c8d9-426c-be0b-2a074a622ff5,2024-09-26,0.9474
iclr_dM1wO2OkbO,2025,Linear-Time Sequence Modeling with MLPs,"Chenwei Cui, Zehao Yan, Gedeon Muhawenayo, Hannah Kerner","foundation or frontier models, including LLMs",reject,Rejected,"[8, 5, 6]",6.33,"[3, 3, 3]",3.0,"[3, 4, 3]",3.33,"[3, 1, 2]",2.0,"[3, 4, 3]",3.33,"[""All-MLP"", ""Sequence Modeling"", ""Multilayer Perceptron"", ""Transformer""]",1,48d45393-c404-40c2-b2dd-dbf61efe1f59,2024-09-28,0.0526
iclr_dIaykjbiiL,2025,Are Synthetic Time-series Data Really not as Good as Real Data?,"Fanzhe Fu, Junru Chen, Jing Zhang, Carl Yang, Lvbin Ma, Yang Yang",generative models,reject,Rejected,"[3, 3, 1, 3]",2.5,"[1, 3, 1, 1]",1.5,"[1, 2, 1, 1]",1.25,"[1, 1, 1, 2]",1.25,"[4, 4, 4, 4]",4.0,"[""Time-Series Data"", ""Data Synthesis"", ""Non-Deep-Learning Data Synthesis"", ""InfoBoost"", ""Prediction"", ""Imputation"", ""Feature Decomposition""]",10,790be060-9163-47df-a94c-cc22b6452be9,2024-09-20,0.5263
iclr_dD6b5RREws,2025,Bootstrap Sampling Rate Greater than 1.0 May Improve Random Forest Performance,"Stanisław Kaźmierczak, Jacek Mańdziuk","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[1, 5, 1, 3]",2.5,"[1, 2, 3, 2]",2.0,"[1, 2, 3, 2]",2.0,"[1, 2, 1, 1]",1.25,"[5, 3, 5, 4]",4.25,"[""random forest"", ""bootstrap sampling rate""]",3,ecca0455-f794-4e15-a075-032ff11f9484,2024-09-27,0.1579
iclr_cya3eEczAx,2025,Adaptive Proximal Gradient Optimizer: Addressing Gradient Inexactness in Predict+Optimize Framework,"Shunyu Wu, Jingcheng Wang, Huihuang Cai, Xinwei Xiao",optimization,reject,Rejected,"[1, 3, 1]",1.67,"[1, 3, 1]",1.67,"[1, 1, 1]",1.0,"[1, 2, 1]",1.33,"[3, 4, 4]",3.67,"[""Predict+optimize"", ""Inexact gradient"", ""Proximal gradient descent"", ""Optimizer""]",0,4a69951a-29a6-461d-9ce3-2a2a998fa9f2,2024-09-14,0.0
iclr_cwbJxUGVOI,2025,OCN: Learning Object-centric Representations for Unsupervised Multi-object Segmentation,"Yafei YANG, Zihui Zhang, Bo Yang","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[6, 5, 8, 6]",6.25,"[3, 3, 3, 3]",3.0,"[2, 3, 3, 2]",2.5,"[3, 2, 3, 2]",2.5,"[3, 4, 2, 5]",3.5,"[""unsupervised learning"", ""object segmentation"", ""objectness representation""]",0,b859815a-3bfc-4f22-9442-c1f6efe9a3a6,2024-09-13,0.0
iclr_ctzGqxE3O0,2025,BID: Broad Incremental for Android Malware Detection,"Yao Shiyi, Zixuan Huang, Chen Ling, Fawen Li, CHANGHAI OU, XINGSHUO HAN, Tingting Wang","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[3, 3, 3, 1]",2.5,"[2, 3, 2, 2]",2.25,"[2, 2, 2, 2]",2.0,"[1, 2, 2, 2]",1.75,"[4, 4, 5, 5]",4.5,"[""Broad learning system"", ""Android malware detection"", ""Incremental learning"", ""Relational structure""]",0,e4c35cf8-41ee-4976-8d27-3a5f833eb20b,2024-09-28,0.0
iclr_cXxfVkRCHJ,2025,Offline-to-Online Reinforcement Learning with Classifier-Free Diffusion Generation,"Xiao Huang, Xu Liu, Enze Zhang, Tong Yu, Shuai Li",reinforcement learning,reject,Rejected,"[3, 3, 3]",3.0,"[2, 2, 2]",2.0,"[2, 2, 2]",2.0,"[1, 1, 1]",1.0,"[4, 4, 4]",4.0,"[""offline-to-online reinforcement learning"", ""data augmentation"", ""diffusion models""]",1,d4822597-5acc-4a9c-81e7-4cbe5883c5d8,2024-09-27,0.0526
iclr_beAlX6RjsW,2025,MPC-Minimized Secure LLM Inference,"Deevashwer Rathee, Dacheng Li, Ion Stoica, Hao Zhang, Raluca Popa","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[6, 5, 8]",6.33,"[2, 2, 3]",2.33,"[4, 2, 3]",3.0,"[2, 2, 3]",2.33,"[4, 5, 4]",4.33,"[""secure inference"", ""secure multi-party computation (MPC)"", ""transformer"", ""large language model (LLM)"", ""open-source foundational model"", ""fine-tuning"", ""LoRA"", ""head-merging""]",20,a165e328-fb4c-401d-867b-97e83b43b592,2024-09-26,1.0526
iclr_bdFzyzf4Qx,2025,A Q-learning approach to the Lowest Unique Positive Integer game,"Natalia Maślany, Tomasz Kania",reinforcement learning,reject,Rejected,"[3, 3, 3]",3.0,"[1, 1, 1]",1.0,"[2, 3, 1]",2.0,"[1, 2, 1]",1.33,"[4, 2, 4]",3.33,"[""Q-learning"", ""Lowest Unique Positive Integer Game"", ""Nash Equilibrium"", ""Poisson-Nash equilibrium"", ""real-time bidding"", ""Swedish Limbo Lottery"", ""multi-agent reinforcement learning"", ""normal-form game"", ""reverse auction"", ""Poisson distribution"", ""game theory.  TL;DR: This paper introduces a Q-learning-based approach to solve the Lowest Unique Positive Integer game"", ""outperforming traditional Poisson-based methods and demonstrating real-world applications such as in reverse auctions and real-time bidding systems""]",0,298923b4-2f1f-4c44-be34-21ee5a13435b,2024-09-17,0.0
iclr_bEgDEyy2Yk,2025,An efficient implementation for solving the all pairs minimax path problem in an undirected dense graph,Gangli Liu,"other topics in machine learning (i.e., none of the above)",reject,Rejected,"[1, 1, 1, 1]",1.0,"[1, 1, 2, 2]",1.5,"[1, 1, 3, 1]",1.5,"[1, 1, 1, 1]",1.0,"[5, 5, 4, 4]",4.5,"[""Minimax path problem"", ""Longest-leg path distance"", ""Min-Max-Jump distance"", ""Widest path problem"", ""Maximum capacity path problem"", ""Bottleneck edge query problem"", ""All points path distance"", ""Floyd-Warshall algorithm"", ""Minimum spanning tree""]",1,e5466c5b-482e-49df-9f0c-139082e7a645,2024-09-25,0.0526
iclr_bBUhlynfRX,2025,A Brain-Inspired Regularizer for Adversarial Robustness,"Elie Attias, Cengiz Pehlevan, Dina Obeid",applications to neuroscience & cognitive science,reject,Rejected,"[3, 3, 3]",3.0,"[3, 1, 2]",2.0,"[2, 2, 3]",2.33,"[2, 2, 1]",1.67,"[3, 3, 5]",3.67,"[""Neuroscience"", ""Machine Learning"", ""CNN"", ""Adversarial Attacks"", ""Image Classification"", ""Brain-Inspired""]",1,2b5d627e-b02b-438e-a70e-d74402151dc3,2024-09-27,0.0526
iclr_b9VSMQZl0j,2025,General Skeleton Semantics Learning with Probabilistic Masked Context Reconstruction for Skeleton-Based Person Re-Identification,"Haocong Rao, Chunyan Miao","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[6, 5, 6, 8, 8]",6.6,"[3, 3, 2, 3, 3]",2.8,"[3, 4, 2, 3, 3]",3.0,"[3, 2, 2, 3, 4]",2.8,"[3, 3, 3, 5, 4]",3.6,"[""General skeleton semantics learning"", ""Generality Assessment"", ""Skeleton-based person re-identification"", ""Probabilistic masked reconstruction"", ""Spatial-temporal context learning""]",0,3fe5c00d-92a5-4a57-98e5-8793cabf0720,2024-09-27,0.0
iclr_ak7r4He1qH,2025,AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments,"Samuel Schmidgall, Rojin Ziaei, Carl William Harris, Ji Woong Kim, Eduardo Pontes Reis, Jeffrey K Jopling, Michael Moor",datasets and benchmarks,reject,Rejected,"[6, 8, 8, 8, 6]",7.2,"[4, 3, 3, 3, 2]",3.0,"[3, 3, 3, 3, 3]",3.0,"[3, 3, 3, 3, 2]",2.8,"[5, 3, 4, 4, 4]",4.0,"[""Language Agents"", ""Medical Benchmark"", ""Multimodal Benchmark"", ""Multimodal Language Models""]",192,9cf4dbf3-944c-44ff-bec4-0c47c6889edb,2024-09-27,10.1053
iclr_aIAFDFpNXz,2025,Cradle: Empowering Foundation Agents towards General Computer Control,"Weihao Tan, Wentao Zhang, Xinrun Xu, Haochong Xia, Ziluo Ding, Boyu Li, Bohan Zhou, Junpeng Yue, Jiechuan Jiang, Yewen Li, Ruyi An, Molei Qin, Chuqiao Zong, Longtao Zheng, YuJie Wu, Xiaoqiang Chai, Yifei Bi, Tianbao Xie, Pengjie Gu, Xiyun Li, Ceyao Zhang, Long Tian, Chaojie Wang, Xinrun Wang, Börje F. Karlsson, Bo An, Shuicheng YAN, Zongqing Lu","applications to robotics, autonomy, planning",reject,Rejected,"[5, 5, 8, 8]",6.5,"[2, 1, 3, 4]",2.5,"[3, 3, 3, 4]",3.25,"[2, 2, 3, 4]",2.75,"[4, 3, 3, 3]",3.25,"[""Foundation Agents"", ""Large Multimodal Models"", ""Decision-making"", ""General Computer Control""]",77,135bbb73-5a6e-463d-9aaf-ffdbeeea268f,2024-09-26,4.0526
iclr_a8XwgTZzE0,2025,Reconstruct the Understanding of Grokking through Dynamical Systems,Zihan Gu,"other topics in machine learning (i.e., none of the above)",reject,Rejected,"[1, 1, 5, 1]",2.0,"[1, 1, 2, 1]",1.25,"[1, 2, 2, 1]",1.5,"[1, 1, 2, 2]",1.5,"[5, 4, 3, 3]",3.75,"[""interpretability"", ""grokking"", ""dynamical systems"", ""progress measures""]",0,9ddb50fe-f21f-4a04-810d-e935d5aeb6a8,2024-09-25,0.0
iclr_Zs8Z3sgnAA,2025,Fine-grained Attention I/O Complexity: Comprehensive  Analysis for Backward Passes,"Xiaoyu Li, Yingyu Liang, Zhenmei Shi, Zhao Song, Yufa Zhou",learning theory,reject,Rejected,"[8, 5, 6]",6.33,"[3, 3, 3]",3.0,"[3, 3, 3]",3.0,"[3, 2, 3]",2.67,"[4, 3, 2]",3.0,"[""Attention"", ""I/O Complexity"", ""FlashAttention"", ""Gradient"", ""Backward Pass""]",24,f2fbeef3-635e-4a7d-8920-207cca1745ed,2024-09-19,1.2632
iclr_Zq8wylMZ8A,2025,Interpretable Language Modeling via Induction-head Ngram Models,"Eunji Kim, Sriya Mantena, Weiwei Yang, Chandan Singh, Sungroh Yoon, Jianfeng Gao",interpretability and explainable AI,reject,Rejected,"[8, 6, 5, 8]",6.75,"[3, 3, 2, 3]",2.75,"[3, 2, 2, 4]",2.75,"[3, 3, 3, 3]",3.0,"[2, 3, 4, 3]",3.0,"[""interpretability"", ""ngram"", ""language modeling"", ""fmri"", ""neuroscience""]",2,a1a7d358-02b0-41e2-8f02-748a50b372d7,2024-09-26,0.1053
iclr_ZkDgQ2PDDm,2025,$\alpha$-Divergence Loss Function for Neural Density Ratio Estimation,Yoshiaki Kitazawa,"probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)",reject,Rejected,"[3, 3, 3]",3.0,"[2, 2, 3]",2.33,"[1, 2, 2]",1.67,"[2, 2, 3]",2.33,"[4, 4, 4]",4.0,"[""density ratio estimation"", ""variational divergence optimization"", ""$\\alpha$-divergence"", ""Kullback\u2013Leibler divergence"", ""and $f$-divergence.""]",1,113b8cd2-61a4-4b2e-875e-b1fcb7c16821,2024-09-19,0.0526
iclr_ZP1HqLus4y,2025,CONTINUAL FINITE-SUM MINIMIZATION UNDER THE POLYAK-ŁOJASIEWICZ CONDITION,"Ioannis Mavrothalassitis, Stratis Skoulakis, Elias Abad Rocamora, Andrej Janchevski, Volkan Cevher",optimization,reject,Rejected,"[3, 3, 3]",3.0,"[3, 3, 2]",2.67,"[2, 3, 3]",2.67,"[2, 1, 2]",1.67,"[4, 3, 4]",3.67,"[""Continual Learning"", ""Finite Sum Minimization""]",0,132574fb-e56f-4195-976b-748ea2373b78,2024-09-27,0.0
iclr_ZHTYtXijEn,2025,Directed Structural Adaptation to Overcome Statistical Conflicts and Enable Continual Learning,"Zeki Doruk Erden, Boi Faltings, Ewa Miazga","other topics in machine learning (i.e., none of the above)",reject,Rejected,"[3, 3, 1]",2.33,"[1, 2, 1]",1.33,"[2, 3, 1]",2.0,"[2, 2, 1]",1.67,"[3, 3, 5]",3.67,"[""structural adaptation"", ""continual learning"", ""growth""]",6,b77fe5e3-12ca-46c1-a622-573666101af3,2024-09-27,0.3158
iclr_ZDoN4W5s8d,2025,Lossgate: Incomplete Information and Misaligned Incentives Hinder Regulation of Societal Risks in Machine Learning,"Mohammad Yaghini, Patty Liu, Andrew Magnuson, Franziska Boenisch, Nicolas Papernot","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[3, 3, 3]",3.0,"[1, 1, 1]",1.0,"[2, 1, 2]",1.67,"[2, 2, 3]",2.33,"[3, 3, 4]",3.33,"[""ml regulation"", ""fairness"", ""privacy""]",0,5e598b45-68b5-4e1a-8f6e-c4b7a7b6cd6c,2024-09-27,0.0
iclr_Z7aq3djHZw,2025,JPEG-LM: LLMs as Image Generators with Canonical Codec Representations,"Xiaochuang Han, Marjan Ghazvininejad, Pang Wei Koh, Yulia Tsvetkov","foundation or frontier models, including LLMs",reject,Rejected,"[8, 6, 6, 5]",6.25,"[3, 4, 3, 4]",3.5,"[3, 4, 3, 2]",3.0,"[2, 4, 3, 3]",3.0,"[3, 5, 4, 5]",4.25,"[""LLM for visual generation"", ""codec-based LLMs""]",8,20df4f87-6d2f-4911-ae24-94ba57fce186,2024-09-28,0.4211
iclr_YcaFqY8LWD,2025,GyroAtt: A Gyro Attention Framework for Matrix Manifolds,"Rui Wang, Chen Hu, Xiaojun Wu, Xiaoning Song, Nicu Sebe, Ziheng Chen",learning on graphs and other geometries & topologies,reject,Rejected,"[5, 6, 8, 6]",6.25,"[3, 3, 4, 2]",3.0,"[4, 3, 3, 3]",3.25,"[2, 2, 3, 2]",2.25,"[4, 2, 5, 4]",3.75,"[""Manifold Learning"", ""Representation Learning"", ""Gyrovector Spaces"", ""Riemannian Manifolds"", ""Riemannian Self Attention""]",0,a686a9cd-301b-4afb-9927-499b30425210,2024-09-13,0.0
iclr_YK8eO7BEkJ,2025,An Empirical Study on Normalization in Mamba,"Peilin Feng, Yuanshuai Wang, Yunhao Ni, Zekun Li, Wenjun Wu, Lei Huang","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[3, 3, 3]",3.0,"[2, 3, 3]",2.67,"[3, 2, 3]",2.67,"[1, 1, 2]",1.33,"[3, 3, 4]",3.33,"[""Mamba"", ""long-sequence modeling"", ""normalization"", ""performance"", ""stability""]",1,cf2a8ff8-668f-439e-a530-d4a05d0fa761,2024-09-27,0.0526
iclr_YHDY5uXOSN,2025,VARIATIONAL DIFFUSION CHANNEL DECODING: A ULTRA-LOW-COST NEURAL CHANNEL DECODER,"Siyu Liao, Meiqi Wang",generative models,reject,Rejected,"[1, 3, 5]",3.0,"[2, 1, 1]",1.33,"[1, 2, 2]",1.67,"[2, 2, 3]",2.33,"[4, 5, 4]",4.33,"[""diffusion; channel coding""]",0,d9173b67-4e58-41df-b2be-80734f5fdcfa,2024-09-25,0.0
iclr_YD6xlDstbz,2025,HarmoniCa: Harmonizing Training and Inference for Better Feature Cache in Diffusion Transformer Acceleration,"Yushi Huang, Zining Wang, Ruihao Gong, Jing Liu, Xinjie Zhang, Jun Zhang",generative models,reject,Rejected,"[6, 6, 5, 8]",6.25,"[3, 2, 3, 2]",2.5,"[2, 3, 3, 2]",2.5,"[2, 2, 2, 2]",2.0,"[4, 3, 4, 1]",3.0,"[""diffusion"", ""acceleration"", ""feature cache""]",15,f2bcbc1f-00c3-4ce2-b660-05f47b2f149f,2024-09-18,0.7895
iclr_Y9yQ9qmVrc,2025,scKGOT: Intercellular Signaling Inference with Knowledge Graph Optimal Transport for Single-cell Transcriptomics,"Haihong Yang, Xin Shao, Chengyu Li, Qiang Zhang, Renjun Xu, Xiaohui Fan, Huajun Chen","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[3, 3, 1, 3]",2.5,"[2, 2, 1, 2]",1.75,"[3, 2, 1, 2]",2.0,"[2, 3, 1, 2]",2.0,"[4, 4, 5, 3]",4.0,"[""Knowledge Graph"", ""Optimal Transport"", ""Cell-cell Communication""]",0,53916e4a-8ec2-4d32-973f-40ec63804d04,2024-09-28,0.0
iclr_XnX7xRoroC,2025,Distilling Reinforcement Learning into Single-Batch Datasets,"Connor Wilhelm, Dan Ventura","transfer learning, meta learning, and lifelong learning",reject,Rejected,"[3, 8, 6, 8]",6.25,"[1, 3, 2, 4]",2.5,"[2, 3, 3, 4]",3.0,"[1, 3, 3, 3]",2.5,"[2, 4, 4, 5]",3.75,"[""distillation"", ""reinforcement learning"", ""RL"", ""meta-learning"", ""compression""]",2,27761dd7-430e-4dba-b464-36b360becad0,2024-09-26,0.1053
iclr_Xk9Q0CrJQc,2025,Understanding and Mitigating Distribution Shifts for Machine Learning Force Fields,"Tobias Kreiman, Aditi S. Krishnapriyan","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[6, 5, 6, 8]",6.25,"[2, 3, 3, 3]",2.75,"[3, 2, 3, 3]",2.75,"[2, 2, 3, 3]",2.5,"[3, 5, 4, 4]",4.0,"[""machine learning force fields"", ""test-time training"", ""distribution shifts""]",5,ef1dcf1f-28c3-450d-946b-c8a5283ac7d4,2024-09-27,0.2632
iclr_XH3OiIhtvf,2025,Unsupervised Federated Learning for Privacy Preserving in Face Recognition System,Enoch Solomon,"unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[1, 3, 3, 1]",2.0,"[1, 2, 1, 1]",1.25,"[1, 2, 1, 1]",1.25,"[1, 2, 1, 1]",1.25,"[5, 5, 5, 4]",4.75,"[""Unsupervised Federated Learning for Face Recognition in Decentralized Environments""]",0,11831619-feca-4098-951e-f8d82122c507,2024-09-28,0.0
iclr_Wxl0JMgDoU,2025,Understanding Skill Adaptation in Transformers Using Sparse Autoencoders: Chess as a Model System,"Difan Jiao, George Eilender, Zhenwei Tang, Ashton Anderson",interpretability and explainable AI,reject,Rejected,"[1, 3, 3, 3]",2.5,"[1, 2, 2, 1]",1.5,"[1, 2, 2, 2]",1.75,"[1, 2, 2, 1]",1.5,"[3, 2, 3, 4]",3.0,"[""Skill Adaptation"", ""Chess"", ""Sparse Autoencoders"", ""Mechanistic Interpretability""]",0,628da759-f40d-4d9c-b0f6-e7882b3269dc,2024-09-28,0.0
iclr_Wo66GEFnXd,2025,Learning Time-Dependent Density Functional Theory via Geometry and Physics Aware Latent Evolution,"Xuan Zhang, Jacob Helwig, Haiyang Yu, Xiaofeng Qian, Shuiwang Ji","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[8, 8, 6, 5]",6.75,"[4, 3, 3, 2]",3.0,"[3, 3, 3, 2]",2.75,"[3, 3, 3, 2]",2.75,"[3, 3, 3, 3]",3.0,"[""AI for science"", ""Density functional theory"", ""Real-time TDDFT"", ""Neural PDE solver""]",0,f6a2ba0d-75d0-4d9b-abca-49e45440bb60,2024-09-28,0.0
iclr_WFlLqUmb9v,2025,Efficient Time Series Forecasting via Hyper-Complex Models and Frequency Aggregation,"EYAL YAKIR, Dor Tsur, Haim H. Permuter",learning on time series and dynamical systems,reject,Rejected,"[5, 1, 3, 1]",2.5,"[3, 1, 3, 1]",2.0,"[3, 1, 3, 2]",2.25,"[2, 1, 3, 1]",1.75,"[3, 5, 3, 5]",4.0,"[""time-series forecasting"", ""frequency models"", ""hyper-complex machine learning"", ""short-time Fourier transform""]",0,b8d4599f-2fc4-433f-9796-6a6dfb384bb2,2024-09-27,0.0
iclr_WDxa9hnz4p,2025,Auto-Demo Prompting: Leveraging Generated Outputs as Demonstrations for Enhanced Batch Prompting,"Longyu Feng, Mengze Hong, Chen Jason Zhang","other topics in machine learning (i.e., none of the above)",reject,Rejected,"[3, 3, 1]",2.33,"[2, 2, 1]",1.67,"[1, 2, 1]",1.33,"[1, 2, 1]",1.33,"[4, 4, 5]",4.33,"[""Batch Prompting"", ""In-Context Learning"", ""Large Language Models""]",7,238a81d0-6169-4445-a247-41e744c9997a,2024-09-27,0.3684
iclr_Vi1PJjvEdh,2025,Can I Understand What I Create? Self-Knowledge Evaluation of Large Language Models,"Zhiquan Tan, Lai Wei, Jindong Wang, Xing Xie, Weiran Huang",datasets and benchmarks,reject,Rejected,"[3, 1, 3, 3]",2.5,"[2, 1, 1, 2]",1.5,"[2, 1, 2, 2]",1.75,"[2, 1, 2, 2]",1.75,"[4, 5, 3, 4]",4.0,"[""Evaluation"", ""Self-knowledge""]",8,46c2a237-ad82-4429-aebe-8201de3b5376,2024-09-27,0.4211
iclr_VT2R3UCcBL,2025,Efficient Privacy-Preserving Federated Learning With Selective Parameter Encryption,"Weizhao Jin, Yuhang Yao, Shanshan Han, Jiajun Gu, Carlee Joe-Wong, Srivatsan Ravi, Salman Avestimehr, Chaoyang He","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[8, 8, 6, 5]",6.75,"[4, 3, 2, 3]",3.0,"[4, 3, 3, 2]",3.0,"[3, 3, 2, 2]",2.5,"[3, 3, 4, 3]",3.25,"[""Federated learning"", ""privacy"", ""homomorphic encryption"", ""inversion attack""]",0,946af999-aad8-4d32-9857-3397c777258b,2024-09-25,0.0
iclr_VRbypIkXrt,2025,MetaOptimize: A Framework for Optimizing Step Sizes and Other Meta-parameters,"Arsalan Sharifnassab, Saber Salehkaleybar, Richard S. Sutton",optimization,reject,Rejected,"[5, 5, 5]",5.0,"[2, 1, 2]",1.67,"[2, 2, 3]",2.33,"[2, 2, 2]",2.0,"[3, 3, 2]",2.67,"[""Optimization"", ""Automatic step-size optimization"", ""Automatic hyperparameter optimization"", ""Continual learning""]",6,a403da2d-2db4-41bc-88f8-df4fe8cc338a,2024-09-27,0.3158
iclr_V4Xs283LHH,2025,FlashSampling: Fast and Memory-Efficient Exact Sampling with Group-Gumbel-Max,"Zhen Qin, Xuyang Shen, Yifan Zhang, Yiran Zhong","probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)",reject,Rejected,"[3, 3, 1, 3]",2.5,"[2, 1, 1, 2]",1.5,"[2, 1, 1, 3]",1.75,"[2, 3, 1, 2]",2.0,"[3, 3, 4, 3]",3.25,"[""Fast sampling""]",0,eabcd1ca-eb06-4e43-8699-a6c17e5a442a,2024-09-27,0.0
iclr_V1N6MmDY27,2025,Towards Fully Autonomous Driving with Automated Commonsense Reasoning,"Keegan Kimbrell, Tianhao Wang, Gopal Gupta, Feng Chen","neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)",reject,Rejected,"[1, 5, 3, 1]",2.5,"[1, 2, 2, 2]",1.75,"[2, 3, 1, 1]",1.75,"[1, 3, 1, 1]",1.5,"[3, 3, 4, 3]",3.25,"[""Commonsense Reasoning"", ""Autonomous Vehicles"", ""Uncertainty""]",0,42f5def5-a8c0-46b8-aca9-fb8bf2564dc5,2024-09-27,0.0
iclr_UmhC7fuhzs,2025,"Skin, Muscles, and Bones in MultiSensory Simulation","Yichen Li, Antonio Torralba","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[6, 6, 6, 8]",6.5,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 3]",3.0,"[3, 4, 3, 3]",3.25,"[""multimodal learning; video simulators""]",0,492c597f-fb74-41f8-97a3-6b153099fb76,2024-09-13,0.0
iclr_UkGrcekmSZ,2025,Leveraging deep learning for comprehensive classification of renal diseases: A transfer learning approach,Agniva Banerjee,"transfer learning, meta learning, and lifelong learning",reject,Rejected,"[3, 3, 1, 1]",2.0,"[2, 2, 1, 2]",1.75,"[2, 1, 1, 1]",1.25,"[2, 1, 1, 1]",1.25,"[4, 5, 5, 5]",4.75,"[""CNN"", ""Kidney"", ""image classification"", ""deep learning"", ""transfer learning""]",0,f61b1b20-b3d0-45be-956d-c5a21d964b5f,2024-09-26,0.0
iclr_UdGwotKVQI,2025,Certified Defense on the Fairness of Graph Neural Networks,"Yushun Dong, Binchi Zhang, Hanghang Tong, Jundong Li",learning on graphs and other geometries & topologies,reject,Rejected,"[8, 6, 5, 8]",6.75,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 3]",3.0,"[2, 2, 1, 3]",2.0,"[4, 4, 4, 4]",4.0,"[""Algorithmic Fairness"", ""Graph Neural Networks"", ""Attack and Defense""]",0,98095df0-74d9-4654-920d-9586c26fc23f,2024-09-26,0.0
iclr_UYXq4q1GpW,2025,A Healthy Food Recommender System Using Collaborative Filtering and Transformers,"Mustafa Zaki, Junyuan Lin, Mandy Barrett Korpusik","other topics in machine learning (i.e., none of the above)",reject,Rejected,"[1, 1, 3, 3]",2.0,"[2, 2, 3, 2]",2.25,"[2, 2, 2, 1]",1.75,"[1, 1, 2, 1]",1.25,"[5, 4, 4, 5]",4.5,"[""Collaborative Filtering"", ""EASE"", ""Nutrition"", ""BERT""]",0,c5dd166c-9a7f-41ff-aba2-9394f1ce701b,2024-09-25,0.0
iclr_UVaLZMv0uk,2025,Private Stochastic Optimization for Achieving Second-Order Stationary Points,"Youming Tao, Dongxiao Yu, Xiuzhen Cheng, Falko Dressler, Di Wang","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[6, 8, 8, 5]",6.75,"[3, 3, 4, 3]",3.25,"[3, 3, 3, 3]",3.0,"[4, 4, 3, 2]",3.25,"[3, 3, 4, 4]",3.5,"[""Differential privacy"", ""non-convex optimization"", ""saddle points""]",0,c792a1df-63e8-4d67-900c-2d7ad68a383c,2024-09-26,0.0
iclr_US2UCMvzvP,2025,Why Not Transform Chat Large Language Models to Non-English?,"Xiang Geng, Ming Zhu, Jiahuan Li, Zhejian Lai, Wei Zou, Shuaijie She, Jiaxin GUO, Xiaofeng Zhao, Yinglu Li, Yuang Li, Chang Su, Yanqing Zhao, Xinglin Lyu, Min Zhang, Jiajun Chen, Hao Yang, Shujian Huang","foundation or frontier models, including LLMs",reject,Rejected,"[8, 5, 6, 6]",6.25,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 2]",2.75,"[3, 2, 3, 3]",2.75,"[4, 4, 3, 3]",3.5,"[""Large Language Model"", ""Low Resource Languages"", ""Knowledge Transfer"", ""Catastrophic Forgetting""]",10,ed5b9e67-a657-4327-b480-449ccb18b3b3,2024-09-28,0.5263
iclr_UKZqSYB2ya,2025,Transformer-Based CT Anomaly Detection and Auto-Segmentation of Sparse Lung Nodules,Hooman Ramezani,"applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[3, 3, 3, 1]",2.5,"[2, 2, 1, 3]",2.0,"[2, 2, 2, 2]",2.0,"[1, 2, 2, 1]",1.5,"[5, 5, 5, 5]",5.0,"[""Transformer"", ""CT scans"", ""lung nodules"", ""anomaly detection"", ""auto-segmentation"", ""Deformable-DETR"", ""sparse data"", ""medical imaging"", ""self-attention"", ""multi-scale learning"", ""object detection"", ""Focal Loss"", ""segmentation""]",0,4f2397dd-af3b-4232-b10f-f9a6ac8c3d7a,2024-09-27,0.0
iclr_UFKC0lMTdK,2025,Emerging Pixel Grounding in Large Multimodal Models Without Grounding Supervision,"Shengcao Cao, Liangyan Gui, Yu-Xiong Wang","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[5, 5, 5]",5.0,"[2, 3, 3]",2.67,"[3, 3, 2]",2.67,"[3, 3, 2]",2.67,"[4, 5, 5]",4.67,"[""Large Multimodal Model"", ""Foundation Model"", ""Visual Grounding"", ""Weakly Supervised Learning""]",4,118cdad3-609d-430f-a691-27e12a185c74,2024-09-21,0.2105
iclr_U3UtvOYMiw,2025,Seeded LoRA: Collaborative Fine-Tuning Through Seed Initialization of Adapters,"Alejandro R. Salamanca, Ahmet Üstün, Nicki Skafte Detlefsen, Tim Dettmers","foundation or frontier models, including LLMs",reject,Rejected,"[5, 5, 5]",5.0,"[2, 2, 3]",2.33,"[2, 3, 3]",2.67,"[2, 2, 2]",2.0,"[2, 3, 4]",3.0,"[""PEFT"", ""LoRA"", ""MoE""]",2,7205f19b-db07-42a4-b23a-cd07e6036bec,2024-09-27,0.1053
iclr_TVwD2zIQ1F,2025,Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoors,"Lukas Gosch, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar, Stephan Günnemann",learning on graphs and other geometries & topologies,reject,Rejected,"[6, 6, 8, 6]",6.5,"[4, 3, 3, 3]",3.25,"[3, 3, 3, 3]",3.0,"[3, 2, 3, 4]",3.0,"[3, 4, 3, 2]",3.0,"[""graph neural networks"", ""provable robustness"", ""certificates"", ""poisoning"", ""data poisoning"", ""backdoor attacks"", ""neural tangent kernel"", ""adversarial robustness"", ""mixed-integer linear programming"", ""support vector machines""]",0,6664a330-2f79-409c-9e11-109c348abb38,2024-09-27,0.0
iclr_TROUDY6Wg4,2025,Accelerated Preference Optimization for Large Language Model Alignment,"Jiafan He, Huizhuo Yuan, Quanquan Gu","foundation or frontier models, including LLMs",reject,Rejected,"[5, 5, 5]",5.0,"[3, 4, 3]",3.33,"[2, 3, 2]",2.33,"[2, 3, 2]",2.33,"[4, 3, 3]",3.33,"[""large language models"", ""RLHF"", ""DPO""]",5,24d750a7-c616-47f9-9520-d1d38fe7fbd9,2024-09-28,0.2632
iclr_THSm9HyCKo,2025,JustLogic: A benchmark for natural language deductive reasoning,"Michael K. Chen, Xikun ZHANG, Dacheng Tao",datasets and benchmarks,reject,Rejected,"[5, 5, 5]",5.0,"[3, 3, 3]",3.0,"[3, 2, 3]",2.67,"[2, 2, 2]",2.0,"[5, 5, 4]",4.67,"[""benchmark"", ""logical reasoning"", ""LLM"", ""natural language processing (NLP)"", ""propositional logic""]",0,1c7833a0-1228-42eb-aa9e-ee71e2c0bd58,2024-09-26,0.0
iclr_THOgGo8SX7,2025,Efficient Reinforcement Learning for Global Decision Making in the Presence of Local Agents at Scale,"Emile Timothy Anand, Guannan Qu",reinforcement learning,reject,Rejected,"[5, 5, 5]",5.0,"[2, 2, 2]",2.0,"[3, 3, 3]",3.0,"[2, 2, 2]",2.0,"[2, 2, 2]",2.0,"[""Reinforcement Learning"", ""Multi-agent Systems"", ""Large-scale Systems"", ""Mean-field Approximation""]",6,29973adf-67a7-41a7-9f98-f54c913b8929,2024-09-27,0.3158
iclr_TH4gKbZS1E,2025,KAN versus MLP on Irregular or Noisy Functions,"Chen Zeng, Jiahui Wang, HaoranShen, Qiao Wang","foundation or frontier models, including LLMs",reject,Rejected,"[1, 3, 3, 3]",2.5,"[1, 2, 3, 2]",2.0,"[1, 1, 3, 2]",1.75,"[1, 1, 1, 1]",1.0,"[4, 3, 4, 4]",3.75,"[""Kolmogorov-Arnold networks"", ""Multi-layer Perceptrons"", ""KAN"", ""MLP"", ""Irregularization"", ""Noise""]",26,4b6e6b7b-06ef-4e47-8c7e-6a1e36652057,2024-09-16,1.3684
iclr_SsWMJ42hJO,2025,Preventing Collapse in Contrastive Learning with Orthonormal Prototypes (CLOP),"Huanran Li, Manh Nguyen, Daniel L. Pimentel-Alarcón","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[5, 5, 5]",5.0,"[3, 2, 3]",2.67,"[2, 1, 2]",1.67,"[2, 2, 2]",2.0,"[4, 3, 5]",4.0,"[""Deep Learning"", ""Contrastive Learning"", ""Neural Collapse"", ""Image Classification""]",2,35ca6097-3aaa-4e8d-b8dd-8d75b70d5980,2024-09-20,0.1053
iclr_SrGP0ILoYa,2025,TopER: Topological Embeddings in Graph Representation Learning,"Astrit Tola, Funmilola Mary Taiwo, Cuneyt Gurcan Akcora, Baris Coskunuzer",learning on graphs and other geometries & topologies,reject,Rejected,"[6, 6, 5, 8]",6.25,"[3, 3, 2, 4]",3.0,"[2, 3, 2, 3]",2.5,"[2, 2, 2, 4]",2.5,"[3, 3, 4, 4]",3.5,"[""Graph embeddings"", ""graph classification"", ""graph representation learning"", ""interpretability"", ""data visualization"", ""topological data analysis""]",1,7eb8e632-0630-45c8-b0b0-613346cb0cc9,2024-09-24,0.0526
iclr_SfNmgDqeEa,2025,Looking Beyond the Top-1: Transformers Determine Top Tokens in Order,"Daria Lioubashevski, Tomer M. Schlank, Gabriel Stanovsky, Ariel Goldstein",interpretability and explainable AI,reject,Rejected,"[8, 5, 8, 8, 3]",6.4,"[2, 3, 3, 3, 2]",2.6,"[3, 2, 4, 3, 1]",2.6,"[4, 2, 4, 3, 2]",3.0,"[3, 3, 4, 4, 4]",3.6,"[""mechanistic interpretability"", ""transformer"", ""large language model"", ""efficient inference""]",9,6aee280f-334e-43eb-90aa-a3c1411020c6,2024-09-26,0.4737
iclr_SaOxhcDCM3,2025,Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loop,"Martin Briesch, Dominik Sobania, Franz Rothlauf",generative models,reject,Rejected,"[5, 5, 5, 10]",6.25,"[3, 3, 3, 4]",3.25,"[2, 3, 2, 4]",2.75,"[2, 2, 2, 4]",2.5,"[4, 3, 4, 4]",3.75,"[""self-consuming training loop"", ""large language models"", ""model collapse"", ""generative models""]",84,bcbf709a-4f60-46fd-af4a-b84d5ae545ec,2024-09-26,4.4211
iclr_SPViZd7rvi,2025,Bypassing Skip-Gram Negative Sampling: Dimension Regularization as a More Efficient Alternative for Graph Embeddings,"David Mingfei Liu, Arjun Seshadri, Tina Eliassi-Rad, Johan Ugander",learning on graphs and other geometries & topologies,reject,Rejected,"[5, 5, 5]",5.0,"[3, 3, 2]",2.67,"[2, 3, 3]",2.67,"[3, 2, 2]",2.33,"[3, 4, 3]",3.33,"[""graph embeddings"", ""negative sampling"", ""skip gram"", ""dimension regularization""]",1,cdfde104-2030-4790-a7ba-d75bc71739d5,2024-09-27,0.0526
iclr_SI6zocV2SS,2025,CAN - CONTINUOUSLY ADAPTING NETWORKS,"Harikrishna Satheesh Pillai, Pakhi Banchalia","transfer learning, meta learning, and lifelong learning",reject,Rejected,"[1, 1, 1, 3]",1.5,"[2, 1, 1, 2]",1.5,"[1, 2, 1, 2]",1.5,"[1, 1, 1, 2]",1.25,"[3, 4, 4, 3]",3.5,"[""Continual Learning"", ""Catastrophic Forgetting"", ""Synaptic Plasticity"", ""Hebbian Learning"", ""Adaptive Neural Networks""]",0,c632b604-2eff-4942-a226-299ceae3edb1,2024-09-26,0.0
iclr_RxQOKupaui,2025,Towards Optimal Adapter Placement for Efficient Transfer Learning,"Aleksandra Nowak, Otniel-Bogdan Mercea, Anurag Arnab, Jonas Pfeiffer, Yann Dauphin, Utku Evci","transfer learning, meta learning, and lifelong learning",reject,Rejected,"[6, 3, 6]",5.0,"[3, 2, 4]",3.0,"[3, 2, 4]",3.0,"[3, 2, 3]",2.67,"[2, 4, 4]",3.33,"[""Parameter Efficient Transfer Learning"", ""Adapters"", ""Fine-Tuning""]",4,b2c2c9d3-f498-494b-986e-89ad9a5bd022,2024-09-27,0.2105
iclr_RtzxJLPxGk,2025,Adapprox: Memory Efficient Optimization via Adaptive Randomized Low-Rank Approximation,"Pengxiang Zhao, Ping Li, Yingjie Gu, Yi ZHENG, Stephan Ludger Kölker, Zhefeng Wang, Xiaoming Yuan",optimization,reject,Rejected,"[5, 5, 8, 6, 8]",6.4,"[2, 3, 3, 2, 4]",2.8,"[2, 2, 3, 2, 3]",2.4,"[3, 2, 3, 2, 3]",2.6,"[4, 4, 4, 3, 3]",3.6,"[""memory-efficient optimization"", ""large language models"", ""low-rank approximation""]",0,cf5f3254-b01b-47a2-86e4-2d32107211b9,2024-09-27,0.0
iclr_Rsr913dhyJ,2025,ICFI: a Feature Importance Measure For Multi-Class Classification,"Tommaso Amico, Pernille Matthews, Ira Assent",interpretability and explainable AI,reject,Rejected,"[5, 5, 5]",5.0,"[3, 2, 2]",2.33,"[3, 2, 2]",2.33,"[2, 2, 2]",2.0,"[3, 4, 4]",3.67,"[""Feature Importance"", ""Explainable Artificial Intelligence"", ""Multi-class classification""]",0,db95672f-cd34-4dc5-9423-061def9eb9bf,2024-09-26,0.0
iclr_RqJ0px8osW,2025,A unified lightweight complex scenes-oriented network for infrared and visible image fusion,"Xilai Li, Xiaosong Li, Tianshu TAN, Wuyang Liu, ye tao","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[5, 5, 8, 8, 8]",6.8,"[2, 3, 3, 3, 3]",2.8,"[2, 3, 3, 3, 3]",2.8,"[2, 2, 3, 3, 3]",2.6,"[5, 5, 5, 5, 5]",5.0,"[""Infrared and visible image fusion"", ""Complex Scenes"", ""Unified Network"", ""Frequency domain"", ""Real time""]",0,ea6f3879-d285-44a8-8e0f-7f0b61379e46,2024-09-26,0.0
iclr_ReKWjKvkJE,2025,Structure-Guided Large Language Models for Text-to-SQL Generation,"Qinggang Zhang, Hao Chen, Junnan Dong, Wentao Li, Feiran Huang, Xiao Huang","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[5, 10, 8, 3]",6.5,"[2, 4, 4, 2]",3.0,"[2, 4, 4, 2]",3.0,"[2, 4, 4, 2]",3.0,"[4, 5, 4, 3]",4.0,"[""Text-to-SQL"", ""large language model"", ""structure learning""]",6,70ae3658-c06b-44e3-aa9a-d05bd7f1d8d5,2024-09-26,0.3158
iclr_Re4Z3Wt2DS,2025,Variational Mirror Descent for Robust Learning in Schrödinger Bridge,"Dong-Sig Han, Jaein Kim, HEE BIN YOO, Byoung-Tak Zhang",learning theory,reject,Rejected,"[8, 8, 5, 8, 5]",6.8,"[3, 2, 1, 3, 2]",2.2,"[3, 3, 2, 3, 2]",2.6,"[3, 3, 2, 3, 2]",2.6,"[2, 3, 3, 3, 4]",3.0,"[""optimal transport"", ""mirror descent"", ""variational methods""]",2,6cfe33a8-1ccd-4c5d-8702-596043f9ce7e,2024-09-26,0.1053
iclr_RVSQpkfsLq,2025,Evolving Virtual World with Delta-Engine,"Hongqiu Wu, Zekai Xu, Tianyang Xu, Yan Wang, Weiqi Wu, Jiale Hong, Linfeng Liu, hai zhao, Min Zhang, Zhezhi He","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[3, 3, 1, 1]",2.0,"[1, 2, 1, 1]",1.25,"[1, 2, 1, 1]",1.25,"[2, 1, 1, 1]",1.25,"[4, 2, 4, 4]",3.5,"[""virtual world"", ""role-playing games"", ""large language model""]",0,1f07bddc-5b50-4275-bbc8-a18947aa4a8f,2024-09-25,0.0
iclr_RDFkGZ9Dkh,2025,Large Language Models as Markov Chains,"Oussama Zekri, Ambroise Odonnat, Abdelhakim Benechehab, Linus Bleistein, Nicolas Boulle, Ievgen Redko","foundation or frontier models, including LLMs",reject,Rejected,"[3, 6, 6]",5.0,"[2, 2, 3]",2.33,"[3, 3, 3]",3.0,"[2, 2, 3]",2.33,"[4, 3, 2]",3.0,"[""Large language models"", ""Markov chain"", ""in-context learning"", ""generalization bounds"", ""convergence analysis""]",29,cc675684-589d-4fc8-af3f-7a5747182265,2024-09-22,1.5263
iclr_R9OHszNtpA,2025,Generative Modeling of Individual Behavior at Scale,"Nabil Omi, Lucas Caccia, Anurag Sarkar, Jordan T. Ash, Siddhartha Sen",generative models,reject,Rejected,"[5, 5, 10, 6]",6.5,"[2, 2, 3, 3]",2.5,"[2, 2, 2, 3]",2.25,"[2, 3, 4, 2]",2.75,"[3, 4, 3, 3]",3.25,"[""style"", ""parameter efficient fine-tuning"", ""peft"", ""chess"", ""stylometry"", ""playstyle"", ""representation learning"", ""steerability""]",1,aac8a31c-c26e-4f18-b883-0ee47444bedc,2024-09-27,0.0526
iclr_R1rNN22IoP,2025,MeshLRM: Large Reconstruction Model for High-Quality Meshes,"Xinyue Wei, Kai Zhang, Sai Bi, Hao Tan, Fujun Luan, Valentin Deschaintre, Kalyan Sunkavalli, Hao Su, Zexiang Xu","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[8, 6, 6, 5]",6.25,"[3, 3, 4, 3]",3.25,"[3, 4, 3, 2]",3.0,"[3, 2, 3, 2]",2.5,"[4, 4, 5, 5]",4.5,"[""Sparse-view reconstruction"", ""High-quality mesh"", ""Large Reconstruction Models""]",144,88e1347e-eced-440a-81c6-e266afe623fb,2024-09-24,7.5789
iclr_QdiMWcwU5w,2025,Dynamic Noise Preference Optimization for LLM Self-Improvement via Synthetic Data,"Haoyan Yang, Ting Hua, Shangqian Gao, Binfeng Xu, Zheng Tang, Jie Xu, Hongxia Jin, Vijay Srinivasan","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[3, 6, 6]",5.0,"[2, 3, 2]",2.33,"[2, 2, 3]",2.33,"[2, 3, 3]",2.67,"[2, 2, 2]",2.0,"[""Preference Optimization"", ""Alignment of LLMs"", ""Self-improvement"", ""Synthetic Data"", ""Noise""]",3,53cc985f-4765-46c7-bc64-a4ea5ccd2f09,2024-09-28,0.1579
iclr_Q7uE3M5aMD,2025,Discrimination-free Insurance Pricing with Privatized Sensitive Attributes,"Tianhe Zhang, Suhan Liu, Peng Shi","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[6, 8, 8, 6]",7.0,"[3, 3, 3, 3]",3.0,"[4, 3, 4, 3]",3.5,"[3, 2, 3, 3]",2.75,"[2, 3, 3, 3]",2.75,"[""Fairness"", ""Discrimination-free Insurance Pricing"", ""Insurance"", ""Privatized Attributes"", ""Privacy""]",1,aa6dc71c-4c74-4beb-814b-c0abd8ad6262,2024-09-21,0.0526
iclr_PtnttTKgQw,2025,Leaving the barn door open for Clever Hans: Simple features predict LLM benchmark answers,"Lorenzo Pacchiardi, Marko Tesic, Lucy G Cheke, Jose Hernandez-Orallo","foundation or frontier models, including LLMs",reject,Rejected,"[5, 5, 5]",5.0,"[2, 2, 3]",2.33,"[2, 2, 3]",2.33,"[2, 2, 3]",2.33,"[4, 4, 2]",3.33,"[""LLM benchmarks"", ""Benchmark validity"", ""Clever Hans effect"", ""LLM evaluation""]",9,60c869f6-d672-4543-b9c3-96a29bd10120,2024-09-27,0.4737
iclr_Pt3lfU1NqC,2025,RODIN: Injecting 2D Foundational Features to 3D Vision Language Understanding,"Ayush Jain, Alexander Swerdlow, Yuzhou Wang, Alexander Sax, Franziska Meier, Katerina Fragkiadaki","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[6, 6, 8, 5]",6.25,"[3, 3, 3, 1]",2.5,"[3, 3, 3, 2]",2.75,"[2, 2, 2, 2]",2.0,"[4, 2, 4, 5]",3.75,"[""3D vision-language understanding""]",0,1e40b329-cb3c-4df2-99aa-225cd2ecffb7,2024-09-14,0.0
iclr_PqeMnyGU1B,2025,Understanding Learning with Sliced-Wasserstein Requires Re-thinking Informative Slices,"Huy Tran, Ashkan Shahbazi, Yikun Bai, John R. Hershey, Soheil Kolouri","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[8, 6, 5]",6.33,"[3, 2, 3]",2.67,"[3, 3, 3]",3.0,"[3, 3, 3]",3.0,"[3, 3, 4]",3.33,"[""Optimal Transport"", ""Sliced Wasserstein"", ""Concentration of Measure""]",5,5fd5c090-797b-49ec-aa95-d0d21d9a1ad2,2024-09-27,0.2632
iclr_PcE0yAGAGW,2025,FSL-MIC: An Attentional Few-Shot Learning Framework for EEG Motor Imagery Classification,"Elnaz Lashgari, Mohamamd Salehan, Ali Lashgari",applications to neuroscience & cognitive science,reject,Rejected,"[1, 3, 1, 3, 3]",2.2,"[1, 2, 2, 2, 2]",1.8,"[1, 2, 1, 3, 2]",1.8,"[1, 2, 1, 1, 2]",1.4,"[5, 4, 5, 4, 4]",4.4,"[""Few-shot learning"", ""Data Augmentation"", ""EEG"", ""motor imagery"", ""BCI"", ""Transformer"", ""CNN""]",0,f7aa8ae4-0b81-4c00-96d6-d56b9b6ae68e,2024-09-26,0.0
iclr_PWtx9fJqM5,2025,A Study of Necessity & Sufficiency of Linear Transformations in the Attention Mechanism,"Mehran Hosseini, Peyman Hosseini","other topics in machine learning (i.e., none of the above)",reject,Rejected,"[6, 6, 3]",5.0,"[3, 3, 3]",3.0,"[2, 3, 2]",2.33,"[2, 2, 2]",2.0,"[2, 4, 5]",3.67,"[""Transformers"", ""Attention"", ""Self-Attention""]",0,2496af69-b8a9-4e31-961f-015ecf2e47fc,2024-09-26,0.0
iclr_PTgTlj6x0W,2025,TREANT: Red-teaming Text-to-Image Models with Tree-based Semantic Transformations,"Yi Liu, Guowei Yang, Gelei Deng, Feiyue Chen, Yuqi Chen, Ling Shi, Tianwei Zhang, Yang Liu","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[8, 6, 5, 6]",6.25,"[2, 3, 3, 3]",2.75,"[3, 3, 3, 3]",3.0,"[2, 3, 2, 2]",2.25,"[4, 2, 3, 4]",3.25,"[""Red-teaming""]",0,e8511279-306e-414a-8594-5f1ad0b2cc03,2024-09-23,0.0
iclr_PQrkWvQSL0,2025,DrugAgent: Multi-Agent Large Language Model-Based Reasoning for Drug-Target Interaction Prediction and Repurposing,"Yoshitaka Inoue, Tianci Song, Tianfan Fu, Augustin Luna","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[3, 3, 3, 1]",2.5,"[2, 4, 1, 1]",2.0,"[2, 2, 1, 1]",1.5,"[2, 1, 1, 1]",1.25,"[3, 4, 5, 5]",4.25,"[""Multi-agent"", ""Drug-target interaction"", ""drug-protein binding prediction"", ""Large Language Models""]",0,ea3831ca-0980-462a-97e8-470c43fd688b,2024-09-27,0.0
iclr_POCT74JhAl,2025,Discriminative Estimation of Total Variation Distance: A Fidelity Auditor for Generative Data,"Lan Tao, Shirong Xu, Chi-Hua Wang, Namjoon Suh, Guang Cheng",learning theory,reject,Rejected,"[5, 5, 5]",5.0,"[3, 3, 3]",3.0,"[3, 2, 3]",2.67,"[2, 2, 2]",2.0,"[2, 4, 3]",3.0,"[""Classification; Total Variation Distance; Learning Theory; Generative Data""]",11,424281b2-21d0-4ecd-bb5c-1c2811886225,2024-09-27,0.5789
iclr_PJjHILiQHC,2025,Approaching Deep Learning through the Spectral Dynamics of Weights,"David Yunis, Kumar Kshitij Patel, Samuel Wheeler, Pedro Henrique Pamplona Savarese, Gal Vardi, Karen Livescu, Michael Maire, Matthew Walter",interpretability and explainable AI,reject,Rejected,"[5, 6, 6, 8]",6.25,"[3, 2, 3, 3]",2.75,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 3]",3.0,"[""simplicity bias"", ""grokking"", ""lottery tickets"", ""linear mode connectivity""]",23,b90dcdc2-eaac-4c64-8077-e08f4d4acd4b,2024-09-27,1.2105
iclr_PH09buDIBT,2025,Glocal Hypergradient Estimation with Koopman Operator,"Ryuichiro Hataya, Yoshinobu Kawahara",learning on time series and dynamical systems,reject,Rejected,"[5, 5, 5]",5.0,"[2, 2, 3]",2.33,"[2, 2, 3]",2.33,"[2, 3, 2]",2.33,"[3, 3, 3]",3.0,"[""gradient-based hyperparameter optimization"", ""koopman operator theory"", ""dynamical systems""]",2,aaeb0c96-cfcb-46d7-8f81-2a292f71304f,2024-09-26,0.1053
iclr_P49gSPmrvN,2025,Time-dependent Development of Scientific Discourse: A Novel Approach Using UMAP and Word Embeddings,Jonah Lynch,learning on graphs and other geometries & topologies,reject,Rejected,"[1, 1, 1]",1.0,"[1, 1, 2]",1.33,"[1, 1, 1]",1.0,"[1, 1, 1]",1.0,"[5, 5, 4]",4.67,"[""visualization"", ""umap"", ""dimension reduction"", ""history of science"", ""word embeddings""]",0,2fc9d4c9-36ff-4754-b777-77c9ac4b183e,2024-09-26,0.0
iclr_OYT7yZfBFw,2025,TrajGPT: Irregular Time-Series Representation Learning for Health Trajectory Analysis,"Ziyang Song, Qincheng Lu, He Zhu, David L. Buckeridge, Yue Li",learning on time series and dynamical systems,reject,Rejected,"[6, 6, 5, 8]",6.25,"[3, 3, 2, 4]",3.0,"[3, 3, 3, 4]",3.25,"[3, 3, 3, 4]",3.25,"[5, 4, 4, 4]",4.25,"[""GPT"", ""Representation learning"", ""Linear attention"", ""ODE"", ""Irregularly-sample time series"", ""zero-shot learning"", ""trajectory analysis""]",1,cf03f6dc-714d-4511-a32b-8cb7e2a0019c,2024-09-28,0.0526
iclr_OXIIFZqiiN,2025,A Dual-Modal Framework Utilizing Visual Prompts for Enhanced Patch Analysis,"Mingqiao Mo, Yifang Xu, Yiyang Niu, Chunyang Ye, Hanyi Yu, Wu Tingting, Zhenghan chen","transfer learning, meta learning, and lifelong learning",reject,Rejected,"[1, 3, 1, 1]",1.5,"[1, 2, 1, 1]",1.25,"[1, 2, 1, 1]",1.25,"[1, 2, 1, 1]",1.25,"[3, 2, 3, 1]",2.25,"[""Code Generation"", ""Domain Adaptation""]",0,dcc1e04a-28c7-414a-9bbe-fc094826d126,2024-09-28,0.0
iclr_OUhR7Ghg3K,2025,The Disparate Benefits of Deep Ensembles,"Kajetan Schweighofer, Adrian Arnaiz-Rodriguez, Sepp Hochreiter, Nuria M Oliver","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[5, 6, 8]",6.33,"[3, 3, 3]",3.0,"[3, 2, 3]",2.67,"[2, 2, 3]",2.33,"[4, 4, 4]",4.0,"[""Deep Ensembles"", ""Algorithmic Fairness"", ""Disparate Benefits"", ""Post-Processing""]",7,81456e75-dd18-4dd8-a085-6c83d01d46ff,2024-09-27,0.3684
iclr_OCHSgafZ1Y,2025,Zero-shot Mixed Precision Quantization via Joint Optimization of Data Generation and Bit Allocation,"Hoang Anh Dung, Cuong Pham, Trung Le, Jianfei Cai, Thanh-Toan Do","transfer learning, meta learning, and lifelong learning",reject,Rejected,"[6, 5, 8]",6.33,"[3, 2, 3]",2.67,"[4, 3, 3]",3.33,"[4, 2, 2]",2.67,"[3, 4, 4]",3.67,"[""Zero-shot Quantization"", ""Post-training quantization"", ""mixed-precision quantization""]",0,bf99f290-c396-4bd7-9947-fb487726a531,2024-09-26,0.0
iclr_O6DKGUwv0m,2025,Empowering Teachers with Enhanced Knowledge via Variable Scale Distillation Framework,"Hanqi Guo, Shuyong Gao, Xinyu Zhou, Yicheng Song, Yan Wang, Wenqiang Zhang","transfer learning, meta learning, and lifelong learning",reject,Rejected,"[5, 5, 5]",5.0,"[2, 2, 2]",2.0,"[2, 2, 1]",1.67,"[2, 2, 1]",1.67,"[4, 4, 4]",4.0,"[""knowledge distillation"", ""hierarchical distillation"", ""self-supervision"", ""cross-scale image processing.""]",0,239a1384-6816-4111-ab9f-b0ca43e1e20b,2024-09-18,0.0
iclr_NzEIjnIIzv,2025,Bitune: Leveraging Bidirectional Attention to Improve Decoder-Only LLMs,"Dawid Jan Kopiczko, Tijmen Blankevoort, Yuki M Asano","foundation or frontier models, including LLMs",reject,Rejected,"[6, 10, 5, 8]",7.25,"[4, 4, 3, 4]",3.75,"[3, 4, 3, 4]",3.5,"[3, 4, 2, 3]",3.0,"[4, 4, 4, 4]",4.0,"[""Instruction tuning"", ""Parameter-efficient fine-tuning"", ""Transformer"", ""PEFT"", ""LLM""]",0,52c2784f-1e20-4e61-ba87-b0473bd80c0d,2024-09-27,0.0
iclr_NxLWeK4P3q,2025,Unified Universality Theorem for Deep and Shallow Joint-Group-Equivariant Machines,"Sho Sonoda, Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[6, 3, 6]",5.0,"[4, 2, 4]",3.33,"[3, 4, 3]",3.33,"[4, 1, 3]",2.67,"[3, 3, 3]",3.0,"[""group theory"", ""irreducible representation"", ""universality"", ""fully-connected network"", ""joint-group-equivariance"", ""ridgelet transform""]",0,186320e5-9698-4602-aa27-5092b649de6b,2024-09-19,0.0
iclr_Nsms7NeU2x,2025,How much can we Forget about Data Contamination?,"Sebastian Bordt, Suraj Srinivas, Valentyn Boreiko, Ulrike von Luxburg","foundation or frontier models, including LLMs",reject,Rejected,"[8, 6, 5, 8]",6.75,"[4, 3, 3, 3]",3.25,"[3, 2, 3, 4]",3.0,"[3, 3, 2, 3]",2.75,"[2, 2, 3, 4]",2.75,"[""Large Language Models"", ""Contamination"", ""Forgetting"", ""Scaling"", ""Optimization""]",14,a8d655e2-6040-49e9-af16-23823c9d922d,2024-09-27,0.7368
iclr_Ng1r9kTep4,2025,Inverted Activations: Reducing Memory Footprint in Neural Network Training,"Georgii Sergeevich Novikov, Ivan Oseledets","infrastructure, software libraries, hardware, systems, etc.",reject,Rejected,"[8, 5, 6]",6.33,"[3, 3, 3]",3.0,"[4, 2, 2]",2.67,"[3, 2, 4]",3.0,"[5, 3, 4]",4.0,"[""deep learning"", ""large language models""]",0,d192269b-269f-49e0-baf4-3863d401adf3,2024-09-27,0.0
iclr_NbbsRnPBoS,2025,Faster Gradient Descent in Deep Linear Networks: The Advantage of Depth,"Chandra Shekar Lakshminarayanan, Archish S, Arun Rajkumar, Harish Guruprasad Ramaswamy",optimization,reject,Rejected,"[3, 1, 3]",2.33,"[2, 2, 3]",2.33,"[2, 1, 3]",2.0,"[1, 1, 2]",1.33,"[4, 4, 4]",4.0,"[""Deep Linear Network; Gradient Descent; Faster Convergence in Finite Time""]",0,6c450725-c564-4311-b977-89190c35556a,2024-09-27,0.0
iclr_NYPJz0CL5X,2025,Optimal Hyperdimensional Representation for Learning and Cognitive Computation,"Prathyush Poduval, Hamza Errahmouni Barkam, Xiangjian Liu, Sanggeon Yun, Yang Ni, Zhuowen Zou, Mohsen Imani","neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)",reject,Rejected,"[1, 3, 5]",3.0,"[2, 3, 2]",2.33,"[1, 2, 2]",1.67,"[1, 2, 2]",1.67,"[5, 4, 4]",4.33,"[""hyperdimensional computing; vector symbolic architecture; decoding;""]",0,3b088026-defe-4ae1-b6a1-2688b5ba73e2,2024-09-27,0.0
iclr_NSlvSDQ8aE,2025,Force-Guided Bridge Matching for Full-Atom Time-Coarsened Dynamics of Peptides,"Ziyang Yu, Wenbing Huang, Yang Liu","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[5, 8, 8]",7.0,"[3, 4, 3]",3.33,"[2, 3, 3]",2.67,"[1, 4, 3]",2.67,"[5, 4, 3]",4.0,"[""molecular dynamics"", ""force-guided bridge matching"", ""graph neural network""]",7,ccb2baa4-5138-4bdf-9818-21f891de0b93,2024-09-25,0.3684
iclr_MyotJECv0D,2025,Correlation Analysis of Evaluation Metrics for Machine Translation,"Lin Wang, Yuan Wang, Wuying Liu","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[1, 3, 1, 5]",2.5,"[3, 2, 1, 3]",2.25,"[3, 2, 1, 2]",2.0,"[1, 2, 1, 2]",1.5,"[4, 4, 5, 5]",4.5,"[""Correlation Analysis"", ""Morphological Evaluation Metrics"", ""Semantic Evaluation Metrics"", ""Machine Translation""]",0,4dc61870-f4d6-49f3-8e10-0aa85454426e,2024-09-25,0.0
iclr_MqL2e85ZTp,2025,Uncertainty-Guided Optimization on Large Language Model Search Trees,"Julia Grosse, Ruotian Wu, Ahmad Rashid, Philipp Hennig, Pascal Poupart, Agustinus Kristiadi","probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)",reject,Rejected,"[6, 6, 6, 6, 8]",6.4,"[3, 3, 3, 2, 3]",2.8,"[3, 2, 4, 3, 3]",3.0,"[3, 2, 4, 3, 3]",3.0,"[4, 3, 4, 4, 3]",3.6,"[""LLMs"", ""Probabilistic Inference"", ""Tree Search""]",7,355808fd-7373-439e-9e94-c30ffa5bd756,2024-09-27,0.3684
iclr_MoJSnVZ59d,2025,SafeDPO: A Simple Approach to Direct Preference Optimization with Enhanced Safety,"Geon-Hyeong Kim, Youngsoo Jang, Yu Jin Kim, Byoungjip Kim, Honglak Lee, Kyunghoon Bae, Moontae Lee","foundation or frontier models, including LLMs",reject,Rejected,"[6, 6, 6, 8, 6]",6.4,"[2, 2, 3, 4, 3]",2.8,"[3, 3, 3, 3, 3]",3.0,"[2, 3, 3, 3, 3]",2.8,"[4, 4, 2, 3, 3]",3.2,"[""Safety Alignment"", ""LLM Fine-tuning"", ""Preferences"", ""Large Language Models"", ""AI Safety""]",24,cafaf170-84fb-424f-9c15-a46809b8c295,2024-09-25,1.2632
iclr_MjhTb4gwFP,2025,PerLDiff: Controllable Street View Synthesis Using Perspective-Layout Diffusion Model,"Jinhua Zhang, Hualian Sheng, Sijia Cai, Bing Deng, Qiao Liang, Wen Li, Ying Fu, Jieping Ye, Shuhang Gu",generative models,reject,Rejected,"[6, 6, 3]",5.0,"[4, 3, 2]",3.0,"[3, 3, 3]",3.0,"[3, 3, 2]",2.67,"[5, 4, 4]",4.33,"[""Controllable generation;3D annotation;Geometric priors;""]",11,15120a9d-2ca3-4a9d-a318-79bb554a6917,2024-09-13,0.5789
iclr_MW8DN8BE3g,2025,Uni-Map: Unified Camera-LiDAR Perception for Robust HD Map Construction,"Xiaoshuai Hao, Yifan Yang, Mengchuan Wei, Haimei Zhao, Hui Zhang, Shanghang Zhang, Jing Zhang","applications to robotics, autonomy, planning",reject,Rejected,"[5, 6, 8, 6]",6.25,"[4, 3, 3, 3]",3.25,"[3, 2, 4, 3]",3.0,"[1, 2, 3, 3]",2.25,"[4, 4, 4, 4]",4.0,"[""HD Map Construction"", ""Sensor Failures; Out-of-Distribution Robustness""]",0,9bb1a256-0b5d-4a5c-a018-d68bdb51c6b8,2024-09-23,0.0
iclr_MVpvyeVeyI,2025,Causal Bayesian Optimization with Unknown Causal Graphs,"Jean Durand, Yashas Annadani, Stefan Bauer, Sonali Parbhoo",causal reasoning,reject,Rejected,"[5, 8, 3, 10]",6.5,"[3, 4, 3, 4]",3.5,"[3, 4, 3, 4]",3.5,"[2, 3, 2, 4]",2.75,"[3, 4, 4, 4]",3.75,"[""causality; bayesian optimization; causal graph discovery; optimal intervention design""]",0,d8a68da2-c454-47e1-920e-d9a130ee10e3,2024-09-26,0.0
iclr_MV5j4Qpq7N,2025,Leveraging System-Prompt Attention to Counteract Novel Jailbreak Attacks,"Charlotte Siska, Anush Sankaran","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[1, 3, 3]",2.33,"[1, 1, 2]",1.33,"[1, 2, 1]",1.33,"[2, 2, 1]",1.67,"[3, 4, 4]",3.67,"[""jailbreaks"", ""agents"", ""safeguards"", ""latent representations""]",0,9e3cec66-9f6b-4477-bcb1-c8cb536e5d9f,2024-09-26,0.0
iclr_MSlF3GvUXI,2025,Structured-Initialization Learning,"Deyuan Liu, Peng Sun, Xufeng Li, Tao Lin","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[6, 6, 8]",6.67,"[3, 3, 3]",3.0,"[3, 2, 3]",2.67,"[2, 2, 3]",2.33,"[3, 3, 4]",3.33,"[""Efficient Learning""]",0,30534d1e-4d88-4545-bacc-232c2833dfe4,2024-09-27,0.0
iclr_MGceYYNvXp,2025,Project MPG: towards a generalized performance quotient for LLM intelligence,"Lucas Spangher, Tianle Li, Rama Kumar Pasumarthi, William F. Arnold, Peter Grabowski, Eugene Ie, Daniel Gruhl","foundation or frontier models, including LLMs",reject,Rejected,"[3, 1, 1, 1]",1.5,"[3, 1, 1, 2]",1.75,"[1, 2, 2, 2]",1.75,"[1, 2, 2, 1]",1.5,"[3, 4, 4, 4]",3.75,"[""evaluation"", ""LLM"", ""LMSys"", ""benchmarks""]",0,73a54729-5831-4db5-8ba1-42a37de711f4,2024-09-27,0.0
iclr_MEF8SyXuXG,2025,Learning in complex action spaces without policy gradients,"Arash Tavakoli, Sina Ghiassian, Nemanja Rakicevic",reinforcement learning,reject,Rejected,"[5, 5, 5]",5.0,"[3, 1, 3]",2.33,"[3, 2, 2]",2.33,"[3, 2, 2]",2.33,"[4, 4, 4]",4.0,"[""action-value learning"", ""policy gradient methods"", ""complex action spaces""]",0,1a545e18-46e1-4d68-b652-5fdbee6915b1,2024-09-26,0.0
iclr_MB53uAZKSc,2025,TiC-LM: A Multi-Year Benchmark for Continual Pretraining of Language Models,"Jeffrey Li, Mohammadreza Armandpour, Seyed Iman Mirzadeh, Sachin Mehta, Vaishaal Shankar, Raviteja Vemulapalli, Samy Bengio, Oncel Tuzel, Mehrdad Farajtabar, Hadi Pouransari, Fartash Faghri",datasets and benchmarks,reject,Rejected,"[6, 8, 5, 6]",6.25,"[3, 3, 2, 3]",2.75,"[3, 4, 3, 2]",3.0,"[2, 4, 2, 3]",2.75,"[4, 4, 4, 4]",4.0,"[""language models"", ""continual learning"", ""benchmark"", ""temporal adaptation""]",6,cd0f660c-81ac-47e5-8eee-e8c766435f8d,2024-09-27,0.3158
iclr_M922KJFO7O,2025,ClusterGen: Token Generation in Sublinear Time and Memory with Clustering KV Cache,"Amir Zandieh, Insu Han, Vahab Mirrokni, Amin Karbasi","foundation or frontier models, including LLMs",reject,Rejected,"[6, 5, 6, 8]",6.25,"[4, 3, 4, 3]",3.5,"[3, 3, 3, 3]",3.0,"[3, 2, 3, 3]",2.75,"[3, 4, 4, 4]",3.75,"[""KV cache"", ""large language models"", ""clustering""]",0,b414af9e-8c7a-4f36-8113-d02b51779a15,2024-09-28,0.0
iclr_M7CblLwJB8,2025,"AutoCustomization: A Unified Framework for Effortless, Selective LLM Bias and Style Finetuning","Jaroslaw Kochanowicz, Mateusz Olko, Gracjan Góral, Konrad Szewczyk, Krzysztof Dziedzic, Piotr Miłoś","other topics in machine learning (i.e., none of the above)",reject,Rejected,"[3, 3, 3, 1, 3]",2.6,"[3, 2, 2, 2, 2]",2.2,"[3, 2, 1, 1, 1]",1.6,"[1, 2, 2, 1, 1]",1.4,"[4, 4, 4, 4, 4]",4.0,"[""large language models"", ""model customization""]",0,c1d5e1a0-332b-4d96-b44d-29921d7e004b,2024-09-27,0.0
iclr_LqB8cRuBua,2025,Diffusion SigFormer for Interference Time-series Signal Recognition,"Zhouhuaji, Xiaoyu Hao, Xu Liu, Lingling Li, Fang Liu, Licheng Jiao",learning on time series and dynamical systems,reject,Rejected,"[3, 3, 1, 1]",2.0,"[2, 2, 1, 2]",1.75,"[2, 2, 2, 1]",1.75,"[1, 2, 1, 1]",1.25,"[5, 3, 4, 3]",3.75,"[""Anti-interference electromagnetic signal recognition"", ""diffusion"", ""SigFormer"", ""modulation"", ""bluetooth""]",1,1c075d92-0786-4da9-a4a5-0fadabd852b8,2024-09-27,0.0526
iclr_LphpWGimIa,2025,Interpreting Attention Layer Outputs with Sparse Autoencoders,"Connor Kissane, Robert Krzyzanowski, Joseph Isaac Bloom, Arthur Conmy, Neel Nanda",interpretability and explainable AI,reject,Rejected,"[6, 6, 3]",5.0,"[3, 3, 2]",2.67,"[2, 3, 1]",2.0,"[3, 3, 1]",2.33,"[3, 3, 4]",3.33,"[""mechanistic interpretability"", ""llm interpretability""]",52,4ad6edc0-b7de-4894-aba3-11c74283e786,2024-09-26,2.7368
iclr_LgfaMR6Sst,2025,Flexible Active Learning of PDE Trajectories,"Yegon Kim, Hyunsu Kim, Juho Lee","probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)",reject,Rejected,"[8, 5, 8, 8, 5]",6.8,"[3, 3, 3, 3, 3]",3.0,"[2, 3, 3, 3, 3]",2.8,"[3, 3, 2, 3, 2]",2.6,"[4, 3, 4, 3, 4]",3.6,"[""Active learning"", ""Partial Differential Equation (PDE)""]",1,66e582f6-59b5-46f8-8d02-e1f50c6aaa72,2024-09-27,0.0526
iclr_LTpab44sdG,2025,Practical alignment requires more than learning from human feedback,"Tu Trinh, Khanh Xuan Nguyen","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[5, 8, 5, 8]",6.5,"[2, 2, 2, 3]",2.25,"[3, 3, 3, 3]",3.0,"[1, 3, 3, 3]",2.5,"[3, 2, 4, 4]",3.25,"[""reinforcement learning"", ""alignment"", ""human feedback"", ""rlhf"", ""AI safety""]",0,6311a1f8-6c8a-415c-adba-5e2bd9438d77,2024-09-27,0.0
iclr_LQdaXixB0g,2025,pSAE-chiatry: Utilizing Sparse Autoencoders to Uncover Mental-Health-Related Features in Language Models,Declan Grabb,applications to neuroscience & cognitive science,reject,Rejected,"[3, 1, 1, 5]",2.5,"[1, 2, 2, 3]",2.0,"[1, 1, 1, 2]",1.25,"[2, 1, 1, 2]",1.5,"[3, 5, 5, 3]",4.0,"[""mental health"", ""psychiatry"", ""interpretability""]",1,cd9f717f-b287-4c67-bd21-f604ac478c26,2024-09-27,0.0526
iclr_LOiYxBcGA9,2025,How does Your RL Agent Explore? An Optimal Transport Analysis of Occupancy Measure Trajectories,"Reabetswe M. Nkhumise, Debabrota Basu, Tony J. Prescott, Aditya Gilra",reinforcement learning,reject,Rejected,"[6, 3, 6]",5.0,"[3, 3, 3]",3.0,"[3, 2, 2]",2.33,"[2, 2, 2]",2.0,"[3, 4, 2]",3.0,"[""reinforcement learning"", ""wasserstein distance"", ""occupancy measure"", ""exploration-exploitation"", ""effort of learning""]",0,418926ed-b4d5-4cd6-a5d3-d48eb6ac97c1,2024-09-26,0.0
iclr_LFn7s8yRUF,2025,EXPLORING THE IMPACT OF DATA AUGMENTATION ON LOCALIZED PERSONALIZED AI TRAINING WITH LLAMA3 AND LORA,"Haoran Qi, Zehua Wang","foundation or frontier models, including LLMs",reject,Rejected,"[3, 1, 1, 1]",1.5,"[3, 1, 1, 1]",1.5,"[1, 2, 1, 1]",1.25,"[1, 2, 1, 1]",1.25,"[4, 5, 5, 5]",4.75,"[""Data Augmentation"", ""Personalized AI"", ""LLaMA3"", ""Low-Rank Adaptation"", ""NLP"", ""Synonym Replacement"", ""Random Insertion"", ""Random Swap"", ""Back Translation"", ""Paraphrasing"", ""Training Models"", ""Machine Learning"", ""Model Generalization""]",0,0494984f-edff-4216-9806-7d5a56d91bbe,2024-09-26,0.0
iclr_LC2KxRwC3n,2025,A is for Absorption: Studying Feature Splitting and Absorption in Sparse Autoencoders,"David Chanin, James Wilken-Smith, Tomáš Dulka, Hardik Bhatnagar, Joseph Isaac Bloom",interpretability and explainable AI,reject,Rejected,"[8, 6, 8, 8]",7.5,"[3, 2, 3, 3]",2.75,"[3, 3, 4, 3]",3.25,"[3, 3, 4, 3]",3.25,"[3, 3, 4, 4]",3.5,"[""Sparse Autoencoders"", ""SAEs"", ""LLMs"", ""interpretability""]",58,af833e9f-469c-492f-b7e8-5b4482b6bc1d,2024-09-26,3.0526
iclr_L3tW9nbcEM,2025,Schrodinger's Memory: Large Language Models,"Wei Wang, Li Qing",interpretability and explainable AI,reject,Rejected,"[1, 1, 1, 3]",1.5,"[2, 1, 1, 3]",1.75,"[1, 1, 1, 2]",1.25,"[1, 1, 1, 2]",1.25,"[4, 4, 5, 4]",4.25,"[""Large Language Models' Memory""]",4,03ad24c9-59b9-4fc7-9118-18bb41a8142f,2024-09-23,0.2105
iclr_KYipmCMmSO,2025,Characterizing the Training Dynamics of Private Fine-tuning with Langevin Diffusion,"Shuqi Ke, Charlie Hou, Sewoong Oh, Giulia Fanti","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[5, 6, 8]",6.33,"[2, 3, 3]",2.67,"[3, 3, 3]",3.0,"[2, 3, 3]",2.67,"[2, 3, 2]",2.33,"[""differential privacy"", ""convergence"", ""fine-tuning theory"", ""transfer learning theory"", ""langevin diffusion"", ""gradient flow""]",0,7a30682b-a472-41f1-a5de-b8ac2a03dfa3,2024-09-27,0.0
iclr_KWo4w1UXs8,2025,GUNet: A Graph Convolutional Network United Diffusion Model for Stable and Diversity Pose Generation,"Shuowen Liang, sisi li, Qingyun Wang, Cen Zhang, Kaiquanzhu, Tian YANG","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[3, 3, 3]",3.0,"[2, 1, 3]",2.0,"[2, 2, 1]",1.67,"[2, 2, 1]",1.67,"[3, 4, 4]",3.67,"[""Diffusion"", ""Text2Pose"", ""GCN"", ""UNet""]",0,03abaa62-2299-4a88-b139-2df5a74008ee,2024-09-26,0.0
iclr_KCTHM2Ffh3,2025,Runtime Learning Machine,"Hongpeng Cao, Yanbing Mao, Yihao Cai, Lui Sha, Marco Caccamo",reinforcement learning,reject,Rejected,"[6, 5, 8]",6.33,"[3, 2, 2]",2.33,"[3, 3, 3]",3.0,"[2, 2, 3]",2.33,"[3, 3, 3]",3.0,"[""Runtime Learning"", ""Deep Reinforcement Learning"", ""Safety"", ""Unknown Unknown"", ""Autonomous Systems""]",100,c0e8cc59-bba2-4a8c-8209-1ee2a0e9ee1b,2024-09-18,5.2632
iclr_JXvEzl8YkS,2025,Regularised Jump Models for Regime Identification and Feature Selection,"Edward Selig, Paul Alexander Bilokon",learning on time series and dynamical systems,reject,Rejected,"[3, 1, 3, 1]",2.0,"[1, 1, 2, 1]",1.25,"[2, 2, 3, 1]",2.0,"[2, 1, 2, 1]",1.5,"[3, 3, 4, 5]",3.75,"[""jump models"", ""regime identification"", ""feature selection""]",1,f3710b05-295c-461e-9d4b-43554b19c389,2024-09-23,0.0526
iclr_JXogIgQV86,2025,IMPROVING FLOW FIELD PREDICTION OF COMPLEX GEOMETRIES USING SIMPLE GEOMETRIES,"Loh Sher En Jessica, Wei Xian Lim, Thant Zin Oo, Wai Lee Chan, Adams Wai-Kin Kong","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[6, 6, 3]",5.0,"[2, 2, 2]",2.0,"[3, 2, 2]",2.33,"[3, 2, 1]",2.0,"[4, 4, 4]",4.0,"[""Computational Fluid Dynamics"", ""Tandem Airfoils"", ""Geometry Representations"", ""Graph Neural Network"", ""Machine Learning for Sciences""]",0,6975c4fe-7d8d-4701-a91c-7826e8bd4b0c,2024-09-23,0.0
iclr_JOBokGDcX0,2025,On Sequence Segmentation with overlapped Chunks in Machine Learning,"Joel Rixen, Matthias Renz","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[5, 3, 1, 1]",2.5,"[2, 1, 1, 1]",1.25,"[3, 2, 2, 1]",2.0,"[3, 2, 1, 2]",2.0,"[3, 3, 5, 5]",4.0,"[""sequence segmentation"", ""speech separation"", ""source separation"", ""audio super resolution"", ""stft"", ""signal processing""]",0,c7acfdf1-b74c-42e5-b1a7-44e8b3812e92,2024-09-24,0.0
iclr_JNZ3Om6NPS,2025,On inherent limitations of GPT/LLM \\ Architecture,Serge Berger,"foundation or frontier models, including LLMs",reject,Rejected,"[3, 3, 1, 1]",2.0,"[1, 1, 1, 1]",1.0,"[1, 2, 1, 1]",1.25,"[1, 2, 1, 1]",1.25,"[4, 3, 4, 3]",3.5,"[""0-1 laws"", ""first-order logic"", ""probabilistic spaces"", ""finite graphs""]",0,1cf21b72-c762-4202-8850-f5cf412c07b8,2024-09-16,0.0
iclr_JL18agpSc3,2025,AutoGeTS: Automated Generation of Text Synthetics for Improving Text Classification,"Chenhao Xue, Yuanzhe Jin, Adrián Carrasco Revilla, Joyraj Chakraborty, Min Chen","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[5, 5, 5]",5.0,"[2, 3, 2]",2.33,"[3, 2, 3]",2.67,"[3, 2, 3]",2.67,"[4, 3, 3]",3.33,"[""Text Classification"", ""Synthetic Data"", ""Data Augmentation"", ""Large Language Model"", ""Text Analysis"", ""Optimization""]",0,cb0b619d-3e54-4caf-938b-6a26dd6ad980,2024-09-27,0.0
iclr_JHoC430Nxi,2025,CTNet: A CNN-Transformer Hybrid Network for 6D Object Pose Estimation,"Sijian Tian, Daoxiong Gong, Jianjun Yu, Junxi Chen, Zhihui Shen","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[3, 3, 3, 1]",2.5,"[1, 2, 3, 2]",2.0,"[1, 3, 2, 1]",1.75,"[2, 2, 2, 1]",1.75,"[3, 5, 4, 5]",4.25,"[""neural networks"", ""6D pose estimation"", ""RGB-D image""]",0,dbba84ca-b1a1-48d7-a2dd-a379042f0166,2024-09-26,0.0
iclr_JEmNgjuQHU,2025,KidSat: satellite imagery to map childhood poverty,"Makkunda Sharma, Fan Yang, Duy-Nhat Vo, Jack Gidney, Matthew Sutcliffe, Mengyan Zhang, Esra Suel, H Juliette T Unwin, Swapnil Mishra, Samir Bhatt, Oliver Fiala, William Rudgard, Seth Flaxman",datasets and benchmarks,reject,Rejected,"[3, 1, 3, 1]",2.0,"[1, 2, 2, 3]",2.0,"[2, 2, 2, 2]",2.0,"[1, 1, 3, 1]",1.5,"[5, 4, 4, 5]",4.5,"[""satellite imagery"", ""remote sensing"", ""social science"", ""global health"", ""economic"", ""health and development indicators""]",0,0781b137-339a-41d6-9805-90e5244c44b9,2024-09-26,0.0
iclr_JCFJFBm5rE,2025,SurfDesign: Effective Protein Design on Molecular Surfaces,"Fang Wu, Shuting Jin, Jianmin Wang, Zerui Xu, xiangxiang Zeng, Jinbo Xu, Brian Hie","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[8, 8, 5, 5, 6]",6.4,"[3, 4, 2, 4, 3]",3.2,"[3, 4, 2, 3, 3]",3.0,"[3, 3, 2, 3, 2]",2.6,"[4, 4, 4, 3, 5]",4.0,"[""Molecular Surfaces"", ""Protein Design"", ""Geometric Deep Learning""]",0,7347bece-8328-43dc-8490-2202356524eb,2024-09-19,0.0
iclr_J6qrIjTzoM,2025,Interpretability of Language Models for Learning Hierarchical Structures,"Zeyuan Allen-Zhu, Yuanzhi Li",interpretability and explainable AI,reject,Rejected,"[8, 3, 8, 6]",6.25,"[3, 1, 4, 2]",2.5,"[4, 3, 4, 3]",3.5,"[4, 3, 3, 3]",3.25,"[3, 3, 3, 3]",3.0,"[""generative language models"", ""interpretability"", ""induction head"", ""inner workings""]",2,ba8b89c8-1db7-4be1-be58-866d21780b67,2024-09-28,0.1053
iclr_IqGVIU4rvM,2025,Balancing Token Efficiency and Structural Accuracy in LLMs Image Generation by Combining VQ-VAE and Diffusion Tokenizers,"Yongqian Li, Yong Luo, Bo Du, Zhennan Meng, NiDong Wang, Yunlin Chen, Zhifei Li",generative models,reject,Rejected,"[3, 3, 1, 3]",2.5,"[2, 2, 1, 2]",1.75,"[2, 2, 1, 2]",1.75,"[1, 2, 1, 2]",1.5,"[4, 4, 4, 4]",4.0,"[""Visual Tokenizer"", ""VQ-VAE"", ""Diffusion""]",0,be82c94a-a99b-4a13-aad5-0d953d9bc800,2024-09-24,0.0
iclr_IoonroIpfD,2025,A Federated Graph Learning Framework With Attention Mechanism and Clustering Algorithm,"Zhaowei Liu, Rufei Gao, Dong Yang","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[3, 3, 1, 3]",2.5,"[2, 1, 1, 1]",1.25,"[2, 2, 1, 2]",1.75,"[1, 1, 1, 1]",1.0,"[4, 5, 4, 5]",4.5,"[""Industrial Internet of Things"", ""Federated Graph Learning"", ""Graph Neural Networks"", ""Attention Mechanism"", ""Clustering Algorithm""]",0,aedf59b4-a753-4153-949a-28f08c304ade,2024-09-27,0.0
iclr_IdynViNzwI,2025,Convergence-Aware Multi-Fidelity Bayesian Optimization,"WEI W. XING, Lu Zhenjie, Yuxin Wang","probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)",reject,Rejected,"[3, 6, 8, 8]",6.25,"[2, 4, 4, 4]",3.5,"[2, 4, 3, 4]",3.25,"[2, 3, 3, 3]",2.75,"[4, 4, 3, 3]",3.5,"[""Bayesian Optimization"", ""Multi-Fidelity Bayesian Optimization"", ""Gaussian process"", ""dynamic systems""]",0,9fd87c72-116b-4235-a055-eba67e2ba21e,2024-09-26,0.0
iclr_IcHHjgdb0o,2025,PASRL: Stabilising Reinforcement Learning with Past Action-State Representation Learning,"Tamas Endrei, Alicia Lozano-Diez, György Cserey",reinforcement learning,reject,Rejected,"[3, 3, 3]",3.0,"[3, 3, 1]",2.33,"[3, 2, 1]",2.0,"[1, 2, 2]",1.67,"[4, 5, 4]",4.33,"[""Reinforcement learning"", ""action smoothness"", ""recurrent neural networks""]",1,fb7c2975-d69e-42d2-849d-fbbed34018d2,2024-09-26,0.0526
iclr_IZjBfdVRB0,2025,Parameter-Efficient Fine-Tuning   via Circular Convolution,"Aochuan Chen, Jiashun Cheng, Zijing Liu, Ziqi Gao, Fugee Tsung, Yu Li, Jia Li","transfer learning, meta learning, and lifelong learning",reject,Rejected,"[6, 6, 3]",5.0,"[3, 3, 4]",3.33,"[3, 3, 2]",2.67,"[3, 2, 1]",2.0,"[4, 4, 4]",4.0,"[""transfer learning"", ""circular convolution"", ""adaptation"", ""efficiency""]",4,9159ea43-c64b-4a5c-b198-dd2b1d403dde,2024-09-13,0.2105
iclr_INFfvQArFY,2025,Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing,"Zhuoran Zhang, Yongxiang Li, Zijian Kan, Keyuan Cheng, Lijie Hu, Di Wang","foundation or frontier models, including LLMs",reject,Rejected,"[6, 5, 8, 6]",6.25,"[2, 3, 3, 2]",2.5,"[3, 3, 4, 2]",3.0,"[3, 3, 3, 3]",3.0,"[3, 4, 4, 4]",3.75,"[""Knowledge Editing""]",35,71ab3cdd-a13f-4c34-a554-d66cdafd47a0,2024-09-24,1.8421
iclr_IHRQif8VQC,2025,Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness,"Stanislav Fort, Balaji Lakshminarayanan","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[5, 8, 8, 6]",6.75,"[4, 4, 3, 3]",3.5,"[2, 3, 3, 3]",2.75,"[4, 4, 3, 3]",3.5,"[4, 3, 4, 5]",4.0,"[""robustness"", ""ensemble"", ""adversarial attacks"", ""generator""]",14,b9cb7c2c-f513-46a1-a32b-bf072ce75729,2024-09-28,0.7368
iclr_IFOgfaX2Fj,2025,Automated Zonal level implant loosening detection from Hip X-ray using a multi-staged approach,"DIVYA MANOHARLAL BHATIA, Aparna Kanakatte Gurumurthy, Murali Poduval, RUPSHA MUKHERJEE, Avik Ghose","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[1, 1, 5, 3]",2.5,"[1, 2, 3, 2]",2.0,"[1, 2, 3, 2]",2.0,"[1, 2, 2, 1]",1.5,"[5, 5, 3, 4]",4.25,"[""Radiolucency"", ""Implant loosening"", ""Gruen zones"", ""Charnley zones""]",0,ca4de6f3-ee3a-4338-8afa-cd576b013cf4,2024-09-27,0.0
iclr_ICR3swcnaa,2025,Spatio-temporal Diffusion Transformer for Action Recognition,"Jing Gu, Yusong Bai, Desheng Zhai, Biao Hou, Shasha Mao, Shuyuan Yang, Licheng Jiao","other topics in machine learning (i.e., none of the above)",reject,Rejected,"[3, 3, 3]",3.0,"[2, 1, 2]",1.67,"[2, 1, 2]",1.67,"[1, 2, 1]",1.33,"[4, 5, 5]",4.67,"[""Video action recognition"", ""fine-grained action"", ""information diffusion"", ""spatiotemporal feature""]",0,846a3bec-3be7-435a-9fea-feaeb2365529,2024-09-27,0.0
iclr_I1VCj1l1Zn,2025,"DLP-LoRA: Efficient Task-Specific LoRA Fusion with a Dynamic, Lightweight Plugin for Large Language Models","Yuxuan Zhang, Ruizhe Li","transfer learning, meta learning, and lifelong learning",reject,Rejected,"[3, 3, 3]",3.0,"[2, 2, 2]",2.0,"[1, 2, 2]",1.67,"[1, 2, 2]",1.67,"[3, 4, 3]",3.33,"[""Multi-LoRA fusion"", ""Parameter Efficient Tuning"", ""LoRA"", ""Cross-Task Generalization""]",7,3702ec27-1cac-4cce-b796-ed0e4fdf6f57,2024-09-28,0.3684
iclr_I0To0G5J7g,2025,On the Surprising Efficacy of Online Self-Improvement for Embodied Multimodal Foundation Models,"Seyed Kamyar Seyed Ghasemipour, Ayzaan Wahid, Jonathan Tompson, Pannag R Sanketi, Igor Mordatch","applications to robotics, autonomy, planning",reject,Rejected,"[5, 10, 5, 5]",6.25,"[1, 3, 2, 2]",2.0,"[3, 4, 3, 3]",3.25,"[3, 4, 2, 2]",2.75,"[3, 3, 3, 4]",3.25,"[""Robotics"", ""Multimodal Foundation Models"", ""Post-Training"", ""Self-Improvement"", ""Reinforcement Learning""]",0,d526985e-7189-4fd8-889d-003716d452c8,2024-09-28,0.0
iclr_HyPofygOCT,2025,ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models,"Zhihang Yuan, Yuzhang Shang, Yue Song, Dawei Yang, Qiang Wu, Yan Yan, Guangyu Sun","infrastructure, software libraries, hardware, systems, etc.",reject,Rejected,"[5, 6, 8, 6]",6.25,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 3]",3.0,"[2, 3, 3, 2]",2.5,"[4, 4, 3, 5]",4.0,"[""LLM"", ""model compression"", ""Low-rank decomposition"", ""efficient AI""]",185,bd3558db-fe0a-4fa8-a7a5-a8eca9c37b8b,2024-09-27,9.7368
iclr_HozsY9Gdcl,2025,Leveraging Set Assumption for Membership Inference in Language Models,"Xinxi Lyu, Ari Holtzman, Niloofar Mireshghallah, Yanai Elazar, Sewon Min, Hannaneh Hajishirzi, Pradeep Dasigi","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[5, 5, 5]",5.0,"[3, 3, 3]",3.0,"[2, 3, 3]",2.67,"[2, 3, 2]",2.33,"[3, 3, 4]",3.33,"[""Large language models"", ""membership Inference"", ""pretraining data""]",0,b1faa894-0d12-4101-9824-50cbecbbca00,2024-09-28,0.0
iclr_Hjk1tWIdvL,2025,Hierarchy-Aided Sparse Attention For Fast LLMs Prefilling Inference,"Wenhao Li, Mingbao Lin, Zhanpeng Zeng, Shuicheng YAN, Rongrong Ji",generative models,reject,Rejected,"[5, 5, 5]",5.0,"[2, 2, 4]",2.67,"[3, 2, 3]",2.67,"[3, 2, 3]",2.67,"[3, 4, 4]",3.67,"[""Long-Context LLM; Pre-Filling Acceleration; Sparse Attention""]",0,3b767324-2b71-48c1-98e7-be3553b5b3d1,2024-09-17,0.0
iclr_HYWdlCPtao,2025,Curvature Enhanced Manifold Sampling,"Ilya Kaufman, Omri Azencot","other topics in machine learning (i.e., none of the above)",reject,Rejected,"[6, 5, 6, 8]",6.25,"[2, 3, 3, 2]",2.5,"[3, 2, 3, 2]",2.5,"[2, 2, 3, 3]",2.5,"[3, 3, 3, 3]",3.0,"[""Manifold learning"", ""Data augmentation"", ""Regression""]",0,647281c2-3214-4218-bd6f-d68bed4c844e,2024-09-26,0.0
iclr_HSLClc1a7W,2025,Latent Score-Based Reweighting for Robust Classification on Imbalanced Tabular Data,"Yunze Tong, Fengda Zhang, Zihao Tang, Kaifeng Gao, Kai Huang, Pengfei Lyu, Jun Xiao","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[6, 8, 5, 6]",6.25,"[4, 3, 2, 2]",2.75,"[3, 3, 1, 2]",2.25,"[4, 3, 2, 2]",2.75,"[4, 3, 5, 3]",3.75,"[""robustness"", ""score model"", ""reweighting""]",8,8fcd5063-b1ea-43b5-8148-2de428ab80e6,2024-09-25,0.4211
iclr_HSGCCUwH7r,2025,Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence,"Shangbin Feng, Zifeng Wang, Yike Wang, Sayna Ebrahimi, Hamid Palangi, Lesly Miculicich, Achin Kulshrestha, Nathalie Rauschmayr, Yejin Choi, Yulia Tsvetkov, Chen-Yu Lee, Tomas Pfister","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[5, 6, 8, 8]",6.75,"[3, 3, 4, 4]",3.5,"[3, 3, 4, 4]",3.5,"[2, 2, 3, 4]",2.75,"[4, 3, 4, 4]",3.75,"[""evolutionary algorithm"", ""model adaptation"", ""model merging""]",28,05fed031-4ef5-44c3-bf3f-a60541c85694,2024-09-25,1.4737
iclr_HB4lr0ykTi,2025,Wasserstein Flow Matching: Generative modeling over families of distributions,"Doron Haviv, Aram-Alexandre Pooladian, Dana Pe'er, Brandon Amos",generative models,reject,Rejected,"[5, 8, 6]",6.33,"[3, 4, 3]",3.33,"[2, 3, 3]",2.67,"[2, 3, 2]",2.33,"[3, 3, 4]",3.33,"[""Optimal Transport"", ""Flow Matching"", ""Point Clouds"", ""Generative Modeling"", ""Single Cell""]",26,d73e0404-1d22-4390-892d-e899ef47b5b5,2024-09-27,1.3684
iclr_GzLepH6MBB,2025,MMTryon: Multi-Modal Multi-Reference Control for High-Quality Fashion Generation,"xujie zhang, Lin ente, Xiu Li, Yuxuan Luo, Xin Dong, Michael Kampffmeyer, Xiaodan Liang",generative models,reject,Rejected,"[8, 10, 3]",7.0,"[4, 4, 2]",3.33,"[4, 4, 2]",3.33,"[4, 4, 2]",3.33,"[5, 5, 5]",5.0,"[""Multi-modal Fashion Generation"", ""Compositional Virtual Try-on""]",25,53560c4b-5a87-42de-b09f-d977c9525255,2024-09-25,1.3158
iclr_GYik1jT3gE,2025,Initialization Matters: Unraveling the Impact of Pre-Training on Federated Learning,"Divyansh Jhunjhunwala, Pranay Sharma, Zheng Xu, Gauri Joshi",optimization,reject,Rejected,"[8, 8, 6, 6]",7.0,"[3, 3, 3, 2]",2.75,"[3, 3, 3, 2]",2.75,"[2, 3, 2, 2]",2.25,"[3, 5, 3, 3]",3.5,"[""Federated Learning"", ""Initialization"", ""Two-Layer CNN"", ""Pre-training"", ""Generalization""]",2,edcc8cfa-94b4-4833-a933-a28477ff6f50,2024-09-27,0.1053
iclr_GLmqHCwbOJ,2025,Rethinking Table Instruction Tuning,"Naihao Deng, Rada Mihalcea","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[8, 6, 5]",6.33,"[3, 3, 3]",3.0,"[3, 3, 3]",3.0,"[3, 2, 1]",2.0,"[4, 4, 4]",4.0,"[""table instruction tuning"", ""table understanding""]",9,b03b93d8-7bc4-4abc-9b09-c0115a6e316c,2024-09-27,0.4737
iclr_G6DLQ40VVR,2025,DivScene: Benchmarking LVLMs for Object Navigation with Diverse Scenes and Objects,"Zhaowei Wang, Hongming Zhang, Tianqing Fang, Ye Tian, Yue Yang, Kaixin Ma, Xiaoman Pan, Yangqiu Song, Dong Yu","foundation or frontier models, including LLMs",reject,Rejected,"[8, 6, 5, 6]",6.25,"[3, 2, 2, 3]",2.5,"[4, 3, 2, 2]",2.75,"[4, 2, 2, 2]",2.5,"[3, 3, 4, 5]",3.75,"[""Embodied AI"", ""Object Navigation"", ""Large Vision Language Models"", ""LVLM"", ""Imitation Learning""]",7,ade18757-d901-4e79-899d-6688feb43b47,2024-09-27,0.3684
iclr_FsgGBhNIt4,2025,Unsupervised Learning of Facial Attribute Representations Using StyleGAN,"shuang song, Jing Wu, Yu-Kun Lai, Yipeng Qin","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[3, 3, 3]",3.0,"[2, 3, 3]",2.67,"[2, 2, 3]",2.33,"[2, 1, 2]",1.67,"[3, 5, 4]",4.0,"[""facial attributes"", ""unsupervised representation learning"", ""GAN"", ""StyleGAN""]",0,7bbb0620-4eb6-4b42-91ff-32e68d17d6fa,2024-09-27,0.0
iclr_FZa1UCC9SC,2025,Exact risk curves of signSGD in High-Dimensions: quantifying preconditioning and noise-compression effects,"Ke Liang Xiao, Noah Marshall, Atish Agarwala, Elliot Paquette",optimization,reject,Rejected,"[6, 3, 6]",5.0,"[3, 1, 3]",2.33,"[3, 2, 3]",2.67,"[3, 2, 2]",2.33,"[3, 4, 5]",4.0,"[""signSGD"", ""stochastic optimization"", ""Deep learning theory"", ""high-dimensional probability"", ""stochastic differential equation""]",7,2d51611c-25a7-4bd9-969e-c5104f06910c,2024-09-27,0.3684
iclr_FOcleL0ltt,2025,UniComposer: Band-Level Music Composition with Symbolic and Audio Unification,"Hangqi Li, Zeyu Zheng","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[3, 3, 3]",3.0,"[2, 1, 1]",1.33,"[2, 2, 3]",2.33,"[1, 1, 2]",1.33,"[4, 3, 4]",3.67,"[""Symbolic and Audio Music"", ""Unified Latent Space"", ""Band-Level Music Generation"", ""Feature Extraction"", ""Generative Models""]",0,5fd72402-4b9e-4783-b881-d0619a3f7a7f,2024-09-20,0.0
iclr_FK8tl47xpP,2025,Greedy Learning to Optimize with Convergence Guarantees,"Patrick Fahy, MOHAMMAD GOLBABAEE, Matthias J Ehrhardt",optimization,reject,Rejected,"[6, 5, 6, 8]",6.25,"[3, 2, 3, 3]",2.75,"[4, 2, 3, 4]",3.25,"[3, 2, 3, 3]",2.75,"[4, 3, 3, 4]",3.5,"[""Optimization"", ""Inverse Problems"", ""Learning to Optimize"", ""Preconditioning"", ""Imaging""]",4,b772c4c0-c4cd-453c-8232-bb44253297e4,2024-09-26,0.2105
iclr_FGd9mXHhM5,2025,Achieving Optimal Breakdown for Byzantine-Robust Gossip,"Renaud Gaucher, Aymeric Dieuleveut, Hadrien Hendrikx",optimization,reject,Rejected,"[6, 6, 3]",5.0,"[3, 3, 2]",2.67,"[3, 2, 1]",2.0,"[3, 2, 2]",2.33,"[3, 4, 4]",3.67,"[""Byzantine"", ""Robustness"", ""Decentralized"", ""Gossip"", ""Averaging"", ""SGD""]",2,2ee092f0-c286-4fd3-ae08-ad175e3cd7b0,2024-09-23,0.1053
iclr_FB84Wkn3Xp,2025,Differentiable Solver Search for fast diffusion sampling,"Shuai Wang, Zexian Li, Qipeng zhang, Tianhui Song, Xubin Li, Tiezheng Ge, Bo Zheng, Limin Wang",generative models,reject,Rejected,"[8, 6, 5]",6.33,"[2, 4, 1]",2.33,"[2, 3, 2]",2.33,"[3, 3, 2]",2.67,"[2, 3, 2]",2.33,"[""Generative models"", ""Solver"", ""Sampler"", ""FlowMatching""]",5,,2024-09-16,0.2632
iclr_F0Zd3knG9j,2025,How transformers learn structured data: insights from hierarchical filtering,"Jerome Garnier-Brun, Marc Mezard, Emanuele Moscato, Luca Saglietti",interpretability and explainable AI,reject,Rejected,"[5, 5, 5]",5.0,"[3, 3, 2]",2.67,"[2, 2, 3]",2.33,"[2, 2, 2]",2.0,"[3, 3, 2]",2.67,"[""Transformers"", ""Belief Propagation"", ""mechanistic explanation"", ""structured data"", ""hierarchical data model"", ""attention"", ""masked language modeling""]",19,d9f620be-90aa-417e-b475-03b3d55aa924,2024-09-25,1.0
iclr_EqCbc4wrzy,2025,MDPE: A Multimodal Deception Dataset with Personality and Emotional Characteristics,"Cong Cai, Shan Liang, Xuefei Liu, Kang Zhu, Zhengqi Wen, Jianhua Tao, Heng Xie, Jizhou Cui, Yiming Ma, Zhenhua Cheng, Hanzhe Xu, Ruibo Fu, Bin Liu, Yongwei Li",datasets and benchmarks,reject,Rejected,"[3, 3, 1, 3]",2.5,"[1, 2, 1, 2]",1.5,"[2, 2, 2, 2]",2.0,"[1, 3, 1, 3]",2.0,"[4, 5, 5, 3]",4.25,"[""deception detection; affective computing; multimodal dataset""]",21,fb033fac-3ef8-475b-be35-cc6af9df3758,2024-09-27,1.1053
iclr_Eaw1ZrsNUN,2025,USDC: A Dataset of $\underline{U}$ser $\underline{S}$tance and $\underline{D}$ogmatism in Long $\underline{C}$onversations,"mounika marreddy, SUBBA REDDY OOTA, Venkata Charan Chinni, Manish Gupta, Lucie Flek",datasets and benchmarks,reject,Rejected,"[6, 8, 6, 5]",6.25,"[2, 3, 3, 3]",2.75,"[3, 3, 4, 3]",3.25,"[3, 3, 3, 2]",2.75,"[4, 3, 3, 4]",3.5,"[""large language models"", ""annotators"", ""user opinions"", ""stance"", ""dogmatism"", ""human-llm alignment"", ""open-source llms"", ""closed-source llms""]",0,415034e5-a311-494e-a8f4-45eb151f0402,2024-09-25,0.0
iclr_EWKPEtwjTy,2025,A Discrete Actor and Critic for Reinforcement Learning on Continuous Tasks,"Jundong Zhang, Tianqi Wei",reinforcement learning,reject,Rejected,"[3, 1, 3, 3]",2.5,"[1, 2, 2, 2]",1.75,"[2, 2, 2, 1]",1.75,"[1, 1, 2, 1]",1.25,"[4, 4, 3, 4]",3.75,"[""reinforcement Learning"", ""discrete action space"", ""continuous control"", ""bipedal locomotion""]",0,b2f1a843-f32f-419e-913c-51f90b97b63e,2024-09-27,0.0
iclr_EQAHilKZ8D,2025,Utilizing Visual Properties to Achieve Better Representations of Objects,"Zhiyu Xu, Qingliang Chen","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[3, 3, 3, 1, 1]",2.2,"[1, 1, 1, 1, 1]",1.0,"[2, 1, 2, 1, 1]",1.4,"[1, 1, 1, 1, 1]",1.0,"[4, 4, 3, 5, 4]",4.0,"[""Vision"", ""Segmentation""]",0,db7e44e3-a7c5-493c-8358-9e744c916043,2024-09-23,0.0
iclr_EIwGR0w8VG,2025,Scalable Approximate Message Passing for Bayesian Neural Networks,"Romeo Sommerfeld, Christian Helms, Ralf Herbrich","probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)",reject,Rejected,"[3, 6, 6]",5.0,"[3, 2, 3]",2.67,"[3, 3, 3]",3.0,"[2, 3, 3]",2.67,"[4, 2, 3]",3.0,"[""Message Passing"", ""Bayesian Neural Networks"", ""Uncertainty Estimation"", ""Factor Graphs""]",0,7dc0df57-8969-4e5b-883a-f43d99a35104,2024-09-26,0.0
iclr_EDJ7cPZk7V,2025,Forgetting Order of Continual Learning: What is Learned First is Forgotten Last,"Guy Hacohen, Tinne Tuytelaars","transfer learning, meta learning, and lifelong learning",reject,Rejected,"[8, 3, 6, 5, 10]",6.4,"[3, 2, 3, 2, 4]",2.8,"[3, 2, 3, 2, 4]",2.8,"[4, 2, 2, 2, 4]",2.8,"[4, 5, 4, 5, 4]",4.4,"[""continual learning"", ""catastrophic forgetting"", ""replay buffer""]",0,494f8dc7-9e2f-452e-add3-d1a14ce603eb,2024-09-26,0.0
iclr_DLhjxxXYwH,2025,Advancing Neural Network Performance through Emergence-Promoting Initialization Scheme,"Johnny Jingze Li, Vivek Kurien George, Gabriel A. Silva",learning theory,reject,Rejected,"[3, 3, 3]",3.0,"[1, 2, 2]",1.67,"[2, 2, 2]",2.0,"[1, 2, 2]",1.67,"[3, 4, 4]",3.67,"[""Emergence"", ""Initialization"", ""cascade effect""]",1,2e9db1c2-d8ca-4e71-8678-c6d5ef335d69,2024-09-26,0.0526
iclr_DIAaRdL2Ra,2025,Convergence of Adafactor under Non-Convex Smooth Stochastic Optimization,"Yusu Hong, Junhong Lin",optimization,reject,Rejected,"[5, 5, 5]",5.0,"[4, 2, 2]",2.67,"[2, 3, 3]",2.67,"[2, 2, 2]",2.0,"[3, 4, 2]",3.0,"[""Adafactor"", ""stochastic optimization"", ""non-convex smooth optimization"", ""convergence""]",0,9ec4f1c4-e847-483c-9d82-d2c11c489b34,2024-09-28,0.0
iclr_DAEXilQHYU,2025,Link Prediction with Relational Hypergraphs,"Xingyue Huang, Miguel Romero Orth, Pablo Barcelo, Michael M. Bronstein, Ismail Ilkan Ceylan",learning on graphs and other geometries & topologies,reject,Rejected,"[6, 6, 8, 6]",6.5,"[3, 3, 3, 3]",3.0,"[2, 3, 3, 3]",2.75,"[2, 3, 3, 2]",2.5,"[4, 4, 3, 3]",3.5,"[""link prediction"", ""relational hypergraphs"", ""expressivity study""]",16,1e3cd4a6-6f37-4e69-80ac-c01272749a5b,2024-09-27,0.8421
iclr_D2as3jDmRA,2025,"LinFusion: 1 GPU, 1 Minute, 16K Image","Songhua Liu, Weihao Yu, Zhenxiong Tan, Xinchao Wang",generative models,reject,Rejected,"[6, 5, 6, 8]",6.25,"[3, 3, 4, 3]",3.25,"[3, 3, 3, 3]",3.0,"[3, 2, 3, 4]",3.0,"[5, 4, 4, 4]",4.25,"[""Linear Attention"", ""Diffusion Models"", ""Image Generation""]",36,dada064c-1afe-42c0-801b-d6fc7efd72f0,2024-09-18,1.8947
iclr_CvrXy1jVLh,2025,Neural Architecture Search by Learning a Hierarchical Search Space,"Mehraveh Javan Roshtkhari, Matthew Toews, Marco Pedersoli","probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)",reject,Rejected,"[6, 6, 3]",5.0,"[3, 2, 1]",2.0,"[3, 2, 2]",2.33,"[2, 2, 2]",2.0,"[4, 2, 3]",3.0,"[""Neural Architecture Search"", ""Monte-Carlo Tree Search"", ""Hierarchical Search Space"", ""Hierarchical Clustering""]",2,a763d39f-f546-45a3-b86b-1126ffa14a73,2024-09-27,0.1053
iclr_CuwjD3cazX,2025,Length Desensitization in Direct Preference Optimization,"Wei Liu, Yang Bai, Chengcheng Han, Rongxiang Weng, Jun Xu, Xuezhi Cao, Jingang Wang, Xunliang Cai",reinforcement learning,reject,Rejected,"[5, 5, 5]",5.0,"[2, 4, 4]",3.33,"[2, 3, 2]",2.33,"[2, 2, 3]",2.33,"[4, 4, 4]",4.0,"[""large language model"", ""reinforcement learning from human feedback"", ""preference optimization""]",10,9acd9802-48e1-4096-9c6c-0eaa32671601,2024-09-26,0.5263
iclr_CkozFajtKq,2025,Flow Matching for Accelerated Simulation of Atomic Transport in Materials,"Juno Nam, Sulin Liu, Gavin Winter, KyuJung Jun, Soojung Yang, Rafael Gomez-Bombarelli","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[8, 3, 8, 8, 5, 6]",6.33,"[4, 3, 3, 2, 4, 2]",3.0,"[4, 3, 4, 3, 3, 2]",3.17,"[3, 2, 4, 3, 2, 2]",2.67,"[2, 5, 3, 4, 5, 1]",3.33,"[""flow matching"", ""generative models"", ""atomistic simulations"", ""molecular dynamics"", ""materials science""]",6,6eca92f3-2b1c-42ae-8467-8e10b5ba59df,2024-09-27,0.3158
iclr_CiEOW1CdKc,2025,Latent Wasserstein Adversarial Imitation Learning,"Siqi Yang, Kai Yan, Alex Schwing, Yu-Xiong Wang",reinforcement learning,reject,Rejected,"[5, 5, 5]",5.0,"[3, 3, 1]",2.33,"[2, 3, 3]",2.67,"[2, 2, 2]",2.0,"[3, 4, 4]",3.67,"[""Adversarial Imitation Learning"", ""Wasserstein Distance""]",0,d06df6e2-2bc2-451d-9d05-6cb101a9c853,2024-09-27,0.0
iclr_CKYsXi0dOV,2025,BLIP-3-Video: You Only Need 32 Tokens to Represent a Video Even in VLMs,"Michael S Ryoo, Honglu Zhou, Shrikant Kendre, Can Qin, Le Xue, Manli Shu, Silvio Savarese, Ran Xu, Caiming Xiong, Juan Carlos Niebles","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[5, 8, 6, 6]",6.25,"[2, 3, 2, 2]",2.25,"[2, 3, 3, 2]",2.5,"[2, 4, 3, 1]",2.5,"[4, 4, 4, 4]",4.0,"[""video representation"", ""video foundation model"", ""vlm"", ""multimodal language model""]",0,4005dd58-97c4-4bda-a434-7fd9ec48e34a,2024-09-25,0.0
iclr_CI9JMBAsPg,2025,DocGenome: A Large Benchmark for Multi-Modal Language Models in Real-World Academic Document Understanding,"Renqiu Xia, Song Mao, Xiangchao Yan, Hongbin Zhou, Bo Zhang, Haoyang Peng, Jiahao Pi, Daocheng Fu, Wenjie Wu, Hancheng Ye, Shiyang Feng, Mingsheng Li, Bin Wang, Chao Xu, Conghui He, Pinlong Cai, Min Dou, Botian Shi, Sheng Zhou, Yongwei Wang, Bin Wang, Junchi Yan, Fei Wu, Yu Qiao",datasets and benchmarks,reject,Rejected,"[6, 5, 6, 8]",6.25,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 3]",3.0,"[3, 3, 2, 3]",2.75,"[4, 5, 4, 4]",4.25,"[""Scientific document structuring"", ""Document understanding"", ""Chart Table and Equation Understanding""]",0,2721f22b-aee6-46c2-aba2-2b6b923249e5,2024-09-26,0.0
iclr_CFMdrcK935,2025,Decomposition of one-layer neural networks via the infinite sum of reproducing kernel Banach spaces,"Seungcheol Shin, Myungjoo Kang",learning theory,reject,Rejected,"[6, 5, 8, 6]",6.25,"[3, 2, 3, 2]",2.5,"[3, 2, 3, 3]",2.75,"[3, 1, 3, 3]",2.5,"[3, 2, 3, 3]",2.75,"[""Neural networks"", ""Reproducing kernel Banach spaces"", ""Class of  Integral RKBSs""]",0,ec45052d-0758-4ece-b04f-7a74c5835514,2024-09-25,0.0
iclr_C9BA0T3xhq,2025,Optimizing Q-Learning Using Expectile Regression: A Dual Approach to Handle In-Sample and Out-of-Sample Data,"Caroline Chen, Yuwei Fu",reinforcement learning,reject,Rejected,"[1, 3, 1, 3]",2.0,"[1, 1, 2, 2]",1.5,"[1, 1, 1, 2]",1.25,"[1, 2, 1, 2]",1.5,"[5, 3, 4, 4]",4.0,"[""reinforcement learning""]",0,1b071088-e105-4e33-b04e-14523155324a,2024-09-27,0.0
iclr_C53FwQZigu,2025,IPSeg: Image Posterior Mitigates Semantic Drift in Class-Incremental Segmentation,"Xiao Yu, Yan Fang, Yunchao Wei, Yao Zhao","transfer learning, meta learning, and lifelong learning",reject,Rejected,"[8, 5, 8, 5]",6.5,"[3, 2, 4, 3]",3.0,"[3, 2, 4, 3]",3.0,"[3, 2, 4, 3]",3.0,"[4, 5, 5, 4]",4.5,"[""Incremental Learning"", ""Semantic Segmentation""]",5,91654a50-fff9-458a-90c1-ac17e98b836d,2024-09-14,0.2632
iclr_Bgz3okeZ7H,2025,AoPS Dataset: Leveraging Online Olympiad-Level Math Problems for LLMs Training and Contamination-Resistant Evaluation,"Sadegh Mahdavi, Muchen Li, Kaiwen Liu, Christos Thrampoulidis, Leonid Sigal, Renjie Liao","foundation or frontier models, including LLMs",reject,Rejected,"[8, 8, 6, 3]",6.25,"[4, 4, 3, 2]",3.25,"[4, 4, 3, 1]",3.0,"[4, 3, 2, 1]",2.5,"[4, 4, 4, 5]",4.25,"[""Mathematical Reasoning"", ""Large Language Models""]",18,3bb89545-ce5c-456b-bda0-279dd6761da5,2024-09-24,0.9474
iclr_BfI0D1ci9r,2025,"Physics-informed GNN for non-linear constrained optimization: PINCO, a solver for the AC-optimal power flow","Anna Varbella, Damien Briens, Giuseppe Alessio D'Inverno, Blazhe Gjorgiev, Giovanni Sansavini","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[3, 5, 1, 3, 1]",2.6,"[1, 4, 3, 3, 1]",2.4,"[1, 3, 2, 3, 2]",2.2,"[1, 2, 2, 2, 1]",1.6,"[4, 4, 5, 4, 5]",4.4,"[""Power systems optimization"", ""Non-linear optimization"", ""Graph neural networks (GNNs)"", ""Physics-informed neural networks (PINN)""]",13,768a4e98-66bd-4fac-bb22-e7bef1e377b2,2024-09-26,0.6842
iclr_BYwdia04ZA,2025,Measuring similarity between embedding spaces using induced neighborhood graphs,"Tiago Fernandes Tavares, Fábio José Ayres, Paris Smaragdis",interpretability and explainable AI,reject,Rejected,"[5, 5, 5]",5.0,"[3, 3, 2]",2.67,"[2, 3, 2]",2.33,"[2, 2, 2]",2.0,"[5, 3, 3]",3.67,"[""Embedding Space Geometry"", ""Paired Representation Similarity"", ""Graph-Based Embedding Comparison""]",3,8a2a8cf9-874f-4d05-a374-a5f969739fab,2024-09-27,0.1579
iclr_B6xUlbgP7j,2025,BRAIN: Behavioral Responses and Artificial Intelligence Neural-Modeling for Consumer Decision-Making,"Jesús Jaime Moreno Escobar, Veronica de Jesus Perez Franco, Ana Lilia Coria Páez, Mauro Daniel Castillo Pérez, Oswaldo Morales Matamoros",learning on time series and dynamical systems,reject,Rejected,"[1, 1, 3, 3]",2.0,"[1, 1, 2, 2]",1.5,"[1, 1, 2, 2]",1.5,"[1, 1, 2, 1]",1.25,"[5, 5, 2, 4]",4.0,"[""Decision-Making; PCA; DCNN; Neuromarketing""]",0,ced5cd32-8a87-4cda-9aec-e36042ca8b46,2024-09-27,0.0
iclr_B6AQzaQCsl,2025,Hot PATE: Private Aggregation of Distributions  for Diverse Tasks,"Edith Cohen, Benjamin Cohen-Wang, Xin Lyu, Jelani Nelson, Tamas Sarlos, Uri Stemmer","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[6, 8, 6, 6]",6.5,"[1, 4, 2, 2]",2.25,"[2, 3, 3, 3]",2.75,"[3, 3, 4, 2]",3.0,"[2, 3, 3, 4]",3.0,"[""PATE"", ""diverse tasks"", ""privacy-preserving machine learning"", ""coordinated sampling"", ""in-context learning""]",4,13fb061b-3ec5-460a-94f8-170c1fbd1a25,2024-09-27,0.2105
iclr_AkL2ID5rRV,2025,PRM:  Photometric Stereo based Large Reconstruction Model,"Wenhang Ge, Jiantao Lin, Guibao Shen, Jiawei Feng, Tao Hu, Xinli Xu, Ying-Cong Chen",generative models,reject,Rejected,"[6, 8, 6, 5]",6.25,"[2, 3, 3, 2]",2.5,"[3, 3, 3, 2]",2.75,"[3, 3, 3, 2]",2.75,"[4, 3, 3, 4]",3.5,"[""3D reconstruction"", ""feed-fowared reconstruction model"", ""photometric stereo""]",5,ad001fbe-2a6f-4e4f-a87a-e9f20ba4717b,2024-09-21,0.2632
iclr_AfZH9EEuRR,2025,EgoQR: Efficient QR Code Reading in Egocentric Settings,"Mohsen Moslehpour, Yichao Lu, Pierce Chuang, Ashish Shenoy, Debojeet Chatterjee, ABHAY HARPALE, Srihari Jayakumar, Vikas Bhardwaj, Seonghyeon Nam, Anuj Kumar","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[1, 1, 3, 3, 3]",2.2,"[3, 1, 3, 2, 1]",2.0,"[3, 2, 3, 2, 1]",2.2,"[2, 1, 2, 2, 1]",1.6,"[5, 4, 3, 3, 5]",4.0,"[""QR Code Reading"", ""Egocentric vision"", ""Smart Wearable Devices"", ""Resource-Constrained Computing"", ""QR Code Detection"", ""QR Code Decoding"", ""Super Resolution""]",1,b0cd394c-c8c6-4f5d-805e-78925cfe4dc0,2024-09-27,0.0526
iclr_AJQuTFd9es,2025,HandsOnVLM: Vision-Language Models for Hand-Object Interaction Prediction,"Chen Bao, Jiarui Xu, Xiaolong Wang, Abhinav Gupta, Homanga Bharadhwaj","foundation or frontier models, including LLMs",reject,Rejected,"[5, 6, 8]",6.33,"[3, 3, 3]",3.0,"[2, 3, 3]",2.67,"[2, 2, 3]",2.33,"[4, 4, 4]",4.0,"[""Vision-language Model"", ""Hand-object Interaction""]",15,69c14f5e-b5cc-4cd2-b88a-1c4231eea008,2024-09-15,0.7895
iclr_AAZ3vwyQ4X,2025,Multimodal Structure Preservation Learning,"Chang Liu, Jieshi Chen, Lee H Harrison, Artur Dubrawski","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[3, 1, 3, 3]",2.5,"[2, 3, 2, 2]",2.25,"[2, 1, 2, 3]",2.0,"[1, 1, 2, 2]",1.5,"[4, 4, 5, 3]",4.0,"[""multimodal machine learning"", ""structure preservation learning"", ""modality gap""]",0,2568408f-342d-4a0b-868a-eba853f69f8b,2024-09-27,0.0
iclr_9xsXEj2ile,2025,BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assembly,"Yan Shen, Ruihai Wu, YUBIN KE, Xinyuan Song, Zeyi Li, Xiaoqi Li, Hongwei Fan, Haoran Lu, Hao Dong","applications to robotics, autonomy, planning",reject,Rejected,"[6, 8, 6, 6]",6.5,"[2, 2, 3, 3]",2.5,"[3, 3, 3, 3]",3.0,"[3, 3, 2, 2]",2.5,"[3, 3, 4, 4]",3.5,"[""Bimanual Manipulation"", ""Robotics"", ""Shape Assembly""]",3,682f917f-f6a6-4f81-aff0-5cbc4f2ab21e,2024-09-18,0.1579
iclr_9UoBuhVNh6,2025,Applications of Modular Co-Design  for De Novo 3D Molecule Generation,"Danny Reidenbach, Filipp Nikitin, Olexandr Isayev, Saee Gopal Paliwal","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[5, 8, 6]",6.33,"[2, 4, 3]",3.0,"[3, 4, 3]",3.33,"[1, 3, 3]",2.33,"[4, 4, 4]",4.0,"[""molecule generation"", ""diffusion"", ""flow matching"", ""transformer""]",8,97862771-9dfc-4582-bf5d-560649e2dac6,2024-09-23,0.4211
iclr_99YEbiBbdy,2025,Dimension-Independent Rates for Structured Neural Density Estimation,"Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam",learning theory,reject,Rejected,"[5, 8, 6, 8]",6.75,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 2]",2.75,"[2, 3, 2, 4]",2.75,"[3, 3, 4, 3]",3.25,"[""density estimation"", ""nonparametric density estimation"", ""graphical model"", ""nonparametric"", ""neural network"", ""deep learning"", ""learning theory"", ""Markov random field"", ""generative model"", ""convergence rate"", ""image processing"", ""curse of dimensionality""]",6,f5e67eaf-74d3-4592-b1a5-63bc73c03e3e,2024-09-26,0.3158
iclr_8tlsJB28c9,2025,M2Edit: Locate and Edit Multi-Granularity Knowledge in Multimodal Large Language Model,"Yang Zhou, Pengfei Cao, Yubo Chen, Qingbin Liu, Dianbo Sui, Xi Chen, Kang Liu, Jun Zhao",generative models,reject,Rejected,"[6, 6, 3]",5.0,"[3, 3, 1]",2.33,"[3, 3, 2]",2.67,"[3, 3, 2]",2.67,"[4, 2, 5]",3.67,"[""Multimodal knowledge editing; Multi-Granularity Knowledge; M2Edit; Multimodal Large Language Model;""]",1,689be967-ff4e-47f3-923f-f1640712ad32,2024-09-27,0.0526
iclr_8o08LSkuAj,2025,Learning with Exact Invariances in Polynomial Time,"Ashkan Soleymani, Behrooz Tahmasebi, Stefanie Jegelka, Patrick Jaillet",learning theory,reject,Rejected,"[6, 8, 6, 6]",6.5,"[3, 4, 3, 2]",3.0,"[3, 4, 3, 3]",3.25,"[3, 4, 3, 3]",3.25,"[4, 4, 4, 4]",4.0,"[""Learning with Invariances"", ""Kernels"", ""Spectral Theory""]",5,d26dd6bc-db20-490d-b1a9-21ed58ece940,2024-09-24,0.2632
iclr_8kGonpsiHb,2025,Lens: Rethinking Multilingual Enhancement for Large Language Models,"Weixiang Zhao, Yulin Hu, Jiahe Guo, Xingyu Sui, Tongtong Wu, Yang Deng, Yanyan Zhao, Bing Qin, Wanxiang Che, Ting Liu","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[8, 8, 6, 5]",6.75,"[3, 4, 3, 3]",3.25,"[3, 4, 2, 3]",3.0,"[3, 3, 3, 2]",2.75,"[4, 5, 3, 4]",4.0,"[""Multilingual Enhancement"", ""Large Language Models""]",6,f67b5787-a4fd-4939-a991-ca153ba5de03,2024-09-27,0.3158
iclr_8QTpYC4smR,2025,"Systematic Review of Large Language Models: Applications, Limitations, Practical Usages and Future Directions","Enoch Solomon, Abraham Woubie Zewoudie","foundation or frontier models, including LLMs",reject,Rejected,"[1, 1, 1, 1]",1.0,"[1, 1, 1, 1]",1.0,"[1, 1, 1, 1]",1.0,"[1, 1, 1, 1]",1.0,"[4, 4, 5, 5]",4.5,"[""Large Language Models"", ""Systematic Review""]",0,2f57042f-4cb6-40c5-a7db-43914d3d93d9,2024-09-28,0.0
iclr_8NiTKmEzJV,2025,NETS: A Non-Equilibrium Transport Sampler,"Michael Samuel Albergo, Eric Vanden-Eijnden","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[5, 6, 6, 8]",6.25,"[2, 4, 2, 3]",2.75,"[2, 3, 3, 3]",2.75,"[1, 3, 3, 4]",2.75,"[4, 4, 4, 2]",3.5,"[""sampling"", ""measure transport"", ""statistical physics""]",53,764c4d3e-d837-40b8-b1f7-e87a886b3ad0,2024-09-27,2.7895
iclr_8LZ1D1yqeg,2025,Task Calibration: Calibrating Large Language Models on Inference Tasks,"Yingjie Li, Yun Luo, Xiaotian Xie, Yue Zhang","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[6, 10, 5]",7.0,"[3, 4, 3]",3.33,"[3, 4, 4]",3.67,"[3, 4, 2]",3.0,"[5, 3, 5]",4.33,"[""large language model"", ""zero-shot learning"", ""model calibration"", ""natural language inference""]",3,0025a400-e2fa-419d-b68f-16743c2811e7,2024-09-27,0.1579
iclr_8CKgS18uWx,2025,Structure-Enhanced Protein Instruction Tuning: Towards General-Purpose Protein Understanding,"Wei Wu, Chao Wang, Liyi Chen, Mingze Yin, Yiheng Zhu, Kun Fu, Jieping Ye, Hui Xiong, Zheng Wang","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[8, 6, 6, 5]",6.25,"[4, 3, 3, 3]",3.25,"[3, 3, 4, 2]",3.0,"[3, 3, 3, 2]",2.75,"[4, 3, 4, 4]",3.75,"[""Large Language Models"", ""Insturction Tuning"", ""Multi-modal Learning"", ""Mixture of Experts"", ""Protein""]",1,434e40c3-f7b1-4ad4-ac36-5096e620f07f,2024-09-26,0.0526
iclr_8CJDYx8GwF,2025,Gradient Flow Provably Learns Robust Classifiers for Data from Orthonormal Clusters,"Hancheng Min, Rene Vidal",learning theory,reject,Rejected,"[5, 6, 6, 8]",6.25,"[3, 3, 3, 4]",3.25,"[3, 3, 3, 4]",3.25,"[1, 3, 3, 3]",2.5,"[4, 5, 4, 3]",4.0,"[""Orthonormal Clusters"", ""Robust classifier"", ""Two-layer Network"", ""Gradient Flow""]",0,d9030ce4-f48a-4fdd-96df-2d4fed40a417,2024-09-25,0.0
iclr_7ienVkNf83,2025,EReLELA: Exploration in Reinforcement Learning via Emergent Language Abstractions,"Kevin Yandoka Denamganai, Tim Bradley, Pierluigi Vito Amadori, Sondess Missaoui, Guy Moss, James Alfred Walker",reinforcement learning,reject,Rejected,"[5, 3, 1]",3.0,"[2, 2, 1]",1.67,"[2, 2, 1]",1.67,"[2, 3, 1]",2.0,"[4, 2, 5]",3.67,"[""Emergent Communication"", ""Exploration"", ""Reinforcement Learning"", ""Abstraction"", ""Emergent Languages"", ""Natural Languages""]",0,26675645-d1dc-4d2a-94e9-1a829932a7cc,2024-09-27,0.0
iclr_7dPrT34fHF,2025,Realizable Abstractions: Near-Optimal Hierarchical Reinforcement Learning,"Roberto Cipollone, Luca Iocchi, Matteo Leonetti",reinforcement learning,reject,Rejected,"[8, 6, 3, 8]",6.25,"[4, 3, 2, 2]",2.75,"[4, 3, 2, 3]",3.0,"[4, 4, 1, 3]",3.0,"[4, 2, 3, 2]",2.75,"[""Hierarchical Reinforcement Learning"", ""Reinforcement Learning theory"", ""PAC algorithm"", ""MDP abstractions""]",0,3e89af6a-52e3-4ffc-beba-553932d2a78c,2024-09-27,0.0
iclr_7ZyFjPUeJp,2025,Self-predictive Mamba: Improving Multi-agent Reinforcement Learning with Self-predictive Encoding,"Zhaohan Feng, Runqing Wang, Boxuan Zhang, Jian Sun, Fang Deng, Gang Wang",reinforcement learning,reject,Rejected,"[3, 3, 3]",3.0,"[3, 3, 2]",2.67,"[2, 1, 2]",1.67,"[2, 2, 2]",2.0,"[4, 4, 4]",4.0,"[""Sequence model"", ""state space model"", ""Mamba"", ""multi-agent reinforcement learning"", ""self-predictive representation learning""]",1,ef59e4e0-d619-4cbd-89ae-4e1392b3bcfd,2024-09-25,0.0526
iclr_7LmuXey1lH,2025,Learning Generalizable Environment Models via Discovering Superposed Causal Relationships,"Siyuan Xiao, Xiong-Hui Chen, Linjun Zhou, Yu-Ren Liu, Ziyi Zhang, Yang Yu, Fangsheng Huang, Mengyue Yang",reinforcement learning,reject,Rejected,"[3, 3, 3]",3.0,"[2, 2, 2]",2.0,"[3, 3, 3]",3.0,"[2, 1, 2]",1.67,"[4, 4, 4]",4.0,"[""Offline Reinforcement Learning"", ""Dynamics Model Learning""]",0,3914c645-a72e-4447-881b-d45bf5d3d4ab,2024-09-21,0.0
iclr_7JlL8ECPJ7,2025,Kernel Banzhaf: A Fast and Robust Estimator for Banzhaf Values,"Yurong Liu, R. Teal Witter, Flip Korn, Tarfah Alrashed, Dimitris Paparas, Juliana Freire",interpretability and explainable AI,reject,Rejected,"[6, 5, 8, 6]",6.25,"[3, 3, 4, 3]",3.25,"[3, 3, 4, 3]",3.25,"[2, 2, 3, 2]",2.25,"[3, 3, 4, 4]",3.5,"[""Banzhaf values"", ""Shapley values"", ""Kernel SHAP"", ""Leverage Scores"", ""Least Squares Regression""]",5,23af74ce-010c-4168-9df8-348a9bdb9b7e,2024-09-25,0.2632
iclr_6w9qffvXkq,2025,Improving CNN training by Riemannian optimization on the generalized Stiefel manifold combined with a gradient-based manifold search,"Alexander Studt, Till Riedel, Michael Beigl","other topics in machine learning (i.e., none of the above)",reject,Rejected,"[3, 1, 3, 3, 3]",2.6,"[2, 1, 2, 2, 2]",1.8,"[3, 1, 2, 2, 3]",2.2,"[2, 1, 2, 1, 2]",1.6,"[3, 5, 4, 5, 5]",4.4,"[""Riemannian optimization"", ""Convolutional neural networks"", ""gradient-based optimization"", ""deep neural networks"", ""generalized Stiefel manifold""]",0,2c810e76-cae0-423b-99fe-7a5aa7429828,2024-09-28,0.0
iclr_6nabbltnLp,2025,Joint or Disjoint: Mixing Training Regimes for Early-Exit Models,"Piotr Kubaty, Bartłomiej Tomasz Krzepkowski, Bartosz Wójcik, Monika Michaluk, Franciszek Szarwacki, Tomasz Trzcinski, Jary Pomponi, Kamil Adamczewski","other topics in machine learning (i.e., none of the above)",reject,Rejected,"[5, 5, 5]",5.0,"[2, 2, 2]",2.0,"[3, 2, 3]",2.67,"[2, 2, 2]",2.0,"[5, 4, 5]",4.67,"[""early-exit"", ""efficient AI"", ""conditional computation""]",2,243acd10-17f2-4620-846a-b985ddac1894,2024-09-21,0.1053
iclr_6PcJEFKvBD,2025,offline_rl_ope: A Python package for off-policy evaluation of offline RL models with real world data,"Joshua William Spear, Matthieu Komorowski, REBECCA POPE, Neil J Sebire","infrastructure, software libraries, hardware, systems, etc.",reject,Rejected,"[3, 3, 1]",2.33,"[1, 2, 1]",1.33,"[2, 2, 1]",1.67,"[2, 2, 1]",1.67,"[4, 3, 4]",3.67,"[""Offline RL"", ""OPE"", ""Python"", ""PyTorch""]",0,4fced6c1-ba41-4fcf-92d6-96c918edc6a1,2024-09-25,0.0
iclr_6O8lh1jIwI,2025,Learning DAGs and Root Causes from Time-Series Data,"Panagiotis Misiakos, Markus Püschel",causal reasoning,reject,Rejected,"[6, 6, 3]",5.0,"[2, 2, 2]",2.0,"[3, 3, 2]",2.67,"[3, 2, 2]",2.33,"[4, 3, 5]",4.0,"[""time-series data"", ""root causes"", ""sparsity"", ""structured vector autoregression"", ""directed acyclic graphs""]",2,855e8ff2-e2da-48cf-aea2-4d17de457f0e,2024-09-26,0.1053
iclr_6Mdvq0bPyG,2025,EfficientQAT: Efficient Quantization-Aware Training for Large Language Models,"Mengzhao Chen, Wenqi Shao, Peng Xu, Jiahao Wang, Peng Gao, Kaipeng Zhang, Ping Luo","foundation or frontier models, including LLMs",reject,Rejected,"[3, 3, 3]",3.0,"[2, 2, 2]",2.0,"[2, 2, 2]",2.0,"[2, 2, 1]",1.67,"[4, 5, 4]",4.33,"[""Large Language Models; Efficient; Quantization-Aware Training""]",171,7a7bb3d9-c3f9-4411-a5ff-91ad71e61871,2024-09-26,9.0
iclr_689MfSyeNz,2025,ZoomVLM: A Tuning-Free Framework for Efficient Video Understanding via Adaptive Zooming in Vision-Language Models,"Zhongzhi Yu, Zheng Wang, Zhenyang Chen, Chaojian Li, Hyewon Suh, Yonggan Fu, Dachuan Shi, Hongxu Yin, Jan Kautz, Pavlo Molchanov, Yingyan Celine Lin","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[5, 5, 5]",5.0,"[3, 3, 2]",2.67,"[2, 3, 1]",2.0,"[2, 3, 2]",2.33,"[5, 4, 5]",4.67,"[""Vision Language Model"", ""Multi-modal""]",0,7a14aa1d-cf61-44d5-9d67-004da42988d2,2024-09-28,0.0
iclr_618qfjvSt9,2025,StyleGuide: Crafting visual style prompting with negative visual query guidance,"Jaeseok Jeong, Junho Kim, Gayoung Lee, Yunjey Choi, Youngjung Uh",generative models,reject,Rejected,"[8, 6, 5, 6]",6.25,"[3, 2, 3, 3]",2.75,"[3, 3, 3, 3]",3.0,"[3, 2, 3, 3]",2.75,"[4, 4, 3, 4]",3.75,"[""Style transfer"", ""Generative models"", ""Diffusion models"", ""Visual prompting"", ""Visual instruction"", ""Computer vision"", ""Content creation"", ""Image synthesis""]",0,ec9e257b-a581-422f-b32c-ba96e3c3425c,2024-09-26,0.0
iclr_5uUr3WFmyZ,2025,Almost sure convergence of stochastic Hamiltonian descent methods,"Måns Williamson, Tony Stillfjord",optimization,reject,Rejected,"[6, 3, 6]",5.0,"[4, 4, 3]",3.67,"[4, 3, 3]",3.33,"[3, 2, 2]",2.33,"[2, 5, 4]",3.67,"[""Stochastic optimization"", ""clipping methods"", ""non-convex optimization""]",1,7343f9cb-9e7f-4621-8149-e2262891033d,2024-09-27,0.0526
iclr_5rfj85bHCy,2025,HyResPINNs: Adaptive Hybrid Residual Networks for Learning Optimal Combinations of Neural and RBF Components for Physics-Informed Modeling,"Madison Cooley, Mike Kirby, Shandian Zhe, Varun Shankar","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[5, 5, 5]",5.0,"[3, 4, 3]",3.33,"[3, 2, 3]",2.67,"[3, 3, 2]",2.67,"[5, 5, 4]",4.67,"[""physics-informed neural networks"", ""residual networks"", ""partial differential equations"", ""radial basis function networks""]",3,9888b575-d79c-4cae-b5bd-d2be26c0822c,2024-09-27,0.1579
iclr_5nldnvvHfw,2025,Adaptive Exponential Decay Rates for Adam,"Weidong Zou, Yuanqing Xia, Weipeng Cao, Bineng Zhong",optimization,reject,Rejected,"[3, 3, 1, 3]",2.5,"[1, 2, 2, 1]",1.5,"[1, 2, 1, 2]",1.5,"[1, 1, 1, 1]",1.0,"[5, 4, 4, 4]",4.25,"[""Optimization method"", ""deep neural networks"", ""Adam and its variants""]",0,c43b1dbc-07bb-4672-a3fd-cd0b50849f35,2024-09-17,0.0
iclr_5kMwiMnUip,2025,NEMESIS \\  Jailbreaking LLMs with Chain of Thoughts Approach,"Vedanta S P, Ashiq Firoz, Sriharsha Bodicherla, Emmanuel George P, Madhav Rao","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[1, 3, 1, 1, 1]",1.4,"[1, 2, 1, 1, 1]",1.2,"[1, 1, 1, 2, 1]",1.2,"[1, 2, 1, 2, 1]",1.4,"[5, 3, 5, 4, 5]",4.4,"[""LLM"", ""Jailbreaking"", ""Chain-of-thought reasoning"", ""Reinforcement learning"", ""LLM security protocols"", ""Adversarial attacks"", ""Defense mechanisms"", ""LlamaGuard"", ""Multishot Jailbreaking"", ""Fine Tuning""]",0,ae2bd2db-e389-4c39-b325-1c6de2edd422,2024-09-23,0.0
iclr_5f3brwjeTl,2025,Physical Backdoor Attack can Jeopardize Driving with Vision-Large-Language Models,"Zhenyang Ni, Rui Ye, Yuxi Wei, Zhen Xiang, Yanfeng Wang, Siheng Chen","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[3, 8, 8, 6]",6.25,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 2]",2.75,"[3, 3, 3, 2]",2.75,"[3, 3, 3, 4]",3.25,"[""Backdoor Attack"", ""Vision Large Language Model"", ""Autonomous Driving""]",41,d3b14662-e302-41f2-9add-c29519fb9c97,2024-09-26,2.1579
iclr_5XL8c0Vg9k,2025,Infinite-parameter Large Language Model,Fei Ding,"transfer learning, meta learning, and lifelong learning",reject,Rejected,"[1, 1, 3, 3]",2.0,"[1, 1, 2, 1]",1.25,"[1, 2, 2, 2]",1.75,"[1, 2, 2, 2]",1.75,"[2, 4, 5, 3]",3.5,"[""lifelong learning""]",0,71efcc85-a91f-4dfa-a000-bf9ec61ae4eb,2024-09-26,0.0
iclr_5MNJKgaj54,2025,ScaLES: Scalable Latent Exploration Score for Pre-Trained Generative Networks,"Omer Ronen, Ahmed Imtiaz Humayun, Richard Baraniuk, Randall Balestriero, Bin Yu",generative models,reject,Rejected,"[5, 6, 8]",6.33,"[2, 3, 4]",3.0,"[3, 3, 4]",3.33,"[2, 2, 3]",2.33,"[3, 3, 3]",3.0,"[""VAE"", ""Latent Space Optimization"", ""OOD""]",0,ec1fb6c6-54c4-437e-9537-14eac388f221,2024-09-28,0.0
iclr_5Ky0W6sp8W,2025,The Buffer Mechanism for Multi-Step Information Reasoning in Language Models,"Zhiwei Wang, Yunji Wang, Zhongwang Zhang, Zhangchen Zhou, Hui Jin, Tianyang Hu, Jiacheng Sun, Zhenguo Li, Yaoyu Zhang, Zhi-Qin John Xu",interpretability and explainable AI,reject,Rejected,"[5, 6, 8, 6]",6.25,"[3, 4, 3, 3]",3.25,"[3, 3, 3, 3]",3.0,"[2, 3, 3, 3]",2.75,"[4, 3, 4, 3]",3.5,"[""Large language model"", ""buffer mechanism"", ""thinking strategies"", ""multi-step reasoning""]",7,50eb1c80-85ae-4377-9c07-8f00a226c243,2024-09-26,0.3684
iclr_5AB33izFxP,2025,Simultaneous Online System Identification and Control using Composite Adaptive Lyapunov-Based Deep Neural Networks,"Omkar Sudhir Patil, Emily J. Griffis, Wanjiku A Makumi, Warren Dixon","applications to robotics, autonomy, planning",reject,Rejected,"[8, 5, 6, 8]",6.75,"[3, 2, 3, 2]",2.5,"[3, 2, 3, 2]",2.5,"[3, 2, 3, 3]",2.75,"[3, 4, 2, 4]",3.25,"[""Adaptive control"", ""Online Learning"", ""Control Theory"", ""Robotics""]",1,96e65390-a1db-4e5e-a037-2eff7557512a,2024-09-27,0.0526
iclr_4qh6nurdYt,2025,Effective Learning with Node Perturbation in Multi-Layer Neural Networks,"Sander Dalm, Marcel van Gerven, Nasir Ahmad",optimization,reject,Rejected,"[3, 6, 6]",5.0,"[3, 3, 3]",3.0,"[2, 3, 3]",2.67,"[2, 3, 3]",2.67,"[4, 4, 4]",4.0,"[""efficient machine learning"", ""optimization""]",1,f2328ca1-10c6-4f91-afdb-9c9f33226468,2024-09-27,0.0526
iclr_4jzjexvjI7,2025,Regret measure in continuous time limit for a stochastic Multi-armed bandit problem,"Sabrine Chebbi, Sofien Dhouib, Setareh Maghsudi",reinforcement learning,reject,Rejected,"[1, 3, 3]",2.33,"[1, 1, 1]",1.0,"[1, 2, 2]",1.67,"[1, 1, 2]",1.33,"[1, 3, 4]",2.67,"[""Stochastic multi-armed bandit"", ""Risk-sensitive regret"", ""Hamilton-Jacobi-Bellman equation"", ""Continuous time-limit""]",0,016a0d1e-668a-46a8-b157-bdd2635fab09,2024-09-25,0.0
iclr_4UXIGATUTj,2025,Forecasting Whole-Brain Neural Activity from Volumetric Video,"Alexander Immer, Jan-Matthis Lueckmann, Alex Bo-Yuan Chen, Peter H. Li, Mariela D Petkova, Nirmala A Iyer, Aparna Dev, Gudrun Ihrke, Woohyun Park, Alyson Petruncio, Aubrey Weigel, Wyatt Korff, Florian Engert, Jeff Lichtman, Misha Ahrens, Viren Jain, Michal Januszewski",applications to neuroscience & cognitive science,reject,Rejected,"[8, 6, 5]",6.33,"[3, 3, 2]",2.67,"[4, 2, 2]",2.67,"[3, 3, 3]",3.0,"[3, 4, 4]",3.67,"[""neuroscience"", ""forecasting"", ""video"", ""lightsheet microscopy"", ""zebrafish"", ""calcium imaging"", ""neuron activity""]",6,ce839c23-91da-4984-9e67-95af8e696e92,2024-09-25,0.3158
iclr_4LiegvCeQD,2025,IEL: Intra-Model Ensemble Learning For Single Sample Test-Time Adaptation,"Aidan Remington, Yash Gondkar, Wei Ding, Ping Chen","transfer learning, meta learning, and lifelong learning",reject,Rejected,"[1, 3, 3, 3]",2.5,"[2, 2, 2, 2]",2.0,"[1, 1, 2, 1]",1.25,"[2, 3, 2, 2]",2.25,"[4, 5, 4, 3]",4.0,"[""Test-Time Adaptation"", ""Ensemble Learning"", ""Entropy-Regularization"", ""Knowledge Distillation""]",0,4d8768a9-b0c9-4124-9bea-9b6d49929ae9,2024-09-25,0.0
iclr_49qqV4NTdy,2025,Understanding Alignment in Multimodal LLMs: A Comprehensive Study,"Elmira Amirloo, Jean-Philippe Fauconnier, Christoph Roesmann, Christian Kerl, Rinu Boney, Yusu Qian, Zirui Wang, Afshin Dehghan, Yinfei Yang, Zhe Gan, Peter Grasch","foundation or frontier models, including LLMs",reject,Rejected,"[8, 6, 6]",6.67,"[3, 3, 3]",3.0,"[3, 3, 3]",3.0,"[3, 2, 2]",2.33,"[3, 3, 3]",3.0,"[""foundation models"", ""multimodal llm"", ""alignment"", ""image understanding""]",25,fda66fe4-118c-4d4d-b99e-75583d6629b7,2024-09-23,1.3158
iclr_473sH8qki8,2025,Reward as Observation: Learning Reward-based Policies for Rapid Adaptation,"Morgan Byrd, Maks Sorokin, Robert Wright, Sehoon Ha",reinforcement learning,reject,Rejected,"[1, 1, 3, 3]",2.0,"[3, 4, 3, 2]",3.0,"[1, 2, 1, 2]",1.5,"[1, 1, 1, 2]",1.25,"[4, 5, 4, 4]",4.25,"[""Reinforcement learning"", ""transfer learning""]",0,b4876603-f1fe-49c5-936c-2f20315e5c16,2024-09-26,0.0
iclr_46tjvA75h6,2025,No MCMC Teaching For me: Learning Energy-Based Models via Diffusion Synergy,"Shanchao Yang, WU Yanrui, Yidong Ouyang, Baoxiang Wang, Hongyuan Zha",generative models,reject,Rejected,"[3, 3, 3]",3.0,"[3, 2, 2]",2.33,"[3, 2, 2]",2.33,"[2, 2, 2]",2.0,"[4, 4, 5]",4.33,"[""energy-based models"", ""generative modeling"", ""sampling"", ""diffusion models""]",0,5384ec9b-23e9-4c51-8b70-07876e17066c,2024-09-26,0.0
iclr_44WiKy8THW,2025,Integrating Geodesic Interpolation and Flow Matching for Non-Autoregressive Text Generation in Logit Space,"Egor Sevriugov, Ivan Oseledets",generative models,reject,Rejected,"[3, 5, 1]",3.0,"[1, 3, 1]",1.67,"[3, 2, 1]",2.0,"[2, 2, 1]",1.67,"[3, 3, 2]",2.67,"[""Flow Matching"", ""Non-autoregressive text generation""]",0,4ec200b7-b414-42aa-b7ba-22750482920e,2024-09-27,0.0
iclr_44IKUSdbUD,2025,Weighted Diversified Sampling for Efficient Data-Driven Single-Cell Gene-Gene Interaction Discovery,"Yifan Wu, Yuntao Yang, Zirui Liu, Zhao Li, Khushbu Pahwa, Rongbin Li, Wenjin Zheng, Xia Hu, Zhaozhuo Xu","other topics in machine learning (i.e., none of the above)",reject,Rejected,"[5, 3, 1]",3.0,"[3, 1, 2]",2.0,"[3, 1, 2]",2.0,"[2, 1, 1]",1.33,"[4, 2, 4]",3.33,"[""Gene-gene interaction"", ""sampling""]",3,28c10b67-5ea8-4716-be83-a025a2715d5c,2024-09-25,0.1579
iclr_3zEKTw9fSB,2025,Generative Parameter Efficient Fine-Tuning,"Chinmay Savadikar, Xi Song, Tianfu Wu","transfer learning, meta learning, and lifelong learning",reject,Rejected,"[5, 5, 5]",5.0,"[2, 2, 1]",1.67,"[2, 3, 3]",2.67,"[2, 2, 3]",2.33,"[3, 3, 4]",3.33,"[""Parameter Efficient Fine-Tuning"", ""Transfer Learning""]",35,e5025074-bd0b-4c3c-9a70-9c0f4fcf4c21,2024-09-26,1.8421
iclr_3vE4B61VSw,2025,Accurate Split Learning on Noisy Signals,"Hang Xu, Aritra Dutta, Xin Li, Panos Kalnis",optimization,reject,Rejected,"[6, 6, 3]",5.0,"[4, 3, 1]",2.67,"[3, 3, 2]",2.67,"[3, 3, 2]",2.67,"[2, 2, 4]",2.67,"[""Split Learning"", ""Denoising techniques""]",0,37b8d8d5-772b-4861-a9c5-ba6d5bc7064d,2024-09-27,0.0
iclr_2x1U8a3s7G,2025,Prompt Diffusion Robustifies Any-Modality Prompt Learning,"Yingjun Du, Gaowen Liu, Yuzhang Shang, Yuguang Yao, Ramana Rao Kompella, Cees G. M. Snoek",generative models,reject,Rejected,"[6, 6, 3]",5.0,"[3, 3, 3]",3.0,"[3, 3, 2]",2.67,"[3, 3, 2]",2.67,"[3, 4, 4]",3.67,"[""Prompt learning"", ""Diffusion model"", ""Vision-language models""]",2,0e1adc69-907c-4b1f-aa4e-54946f5b2eca,2024-09-23,0.1053
iclr_2wDXNF0Gv4,2025,Prompt-Agnostic Erasure for Diffusion Models Using Task Vectors,"Minh Pham, Kelly O. Marshall, Chinmay Hegde, Niv Cohen",generative models,reject,Rejected,"[5, 6, 8, 6]",6.25,"[2, 2, 4, 1]",2.25,"[2, 2, 3, 2]",2.25,"[2, 2, 3, 2]",2.25,"[4, 3, 4, 4]",3.75,"[""Concept Erasure""]",0,770f04bc-6ba7-4bb4-a7f6-e1d98cae1bcd,2024-09-22,0.0
iclr_2RNGX3iTr6,2025,Tabby: Tabular Adaptation for Language Models,"Sonia Cromp, Satya Sai Srinath Namburi GNVV, Catherine Cao, Mohammed Alkhudhayri, Samuel Guo, Nicholas Roberts, Frederic Sala","foundation or frontier models, including LLMs",reject,Rejected,"[1, 3, 5]",3.0,"[2, 3, 2]",2.33,"[1, 3, 2]",2.0,"[1, 2, 2]",1.67,"[3, 5, 3]",3.67,"[""tabular"", ""generative"", ""llm"", ""mixture-of-experts"", ""synthesis"", ""transformer""]",1,2a527a38-eba8-4489-8d67-adc02f59445f,2024-09-21,0.0526
iclr_2MqyCIxLSi,2025,TopoTune: A Framework for Generalized Combinatorial Complex Neural Networks,"Mathilde Papillon, Guillermo Bernardez, Claudio Battiloro, Nina Miolane",learning on graphs and other geometries & topologies,reject,Rejected,"[6, 8, 6, 5]",6.25,"[3, 4, 3, 2]",3.0,"[3, 4, 3, 2]",3.0,"[3, 2, 2, 2]",2.25,"[3, 3, 4, 3]",3.25,"[""Topological Deep Learning"", ""Graph Neural Network"", ""Graph Expansion"", ""Combinatorial Complex"", ""Cellular Complex""]",14,e67b05d5-a19d-4c1d-96b8-b58fe93e58b0,2024-09-27,0.7368
iclr_2DD4AXOAZ8,2025,Inference-Friendly Models With MixAttention,"Shashank Rajput, Ying Sheng, Sean Owen, Vitaliy Chiley","foundation or frontier models, including LLMs",reject,Rejected,"[1, 3, 3, 1]",2.0,"[1, 3, 2, 2]",2.0,"[2, 2, 2, 3]",2.25,"[1, 2, 2, 1]",1.5,"[4, 5, 4, 5]",4.5,"[""language models"", ""inference"", ""transformers"", ""architecture""]",5,0fee92ff-e7ce-4e36-b7ec-02e803887a56,2024-09-26,0.2632
iclr_1nHQRsb3Ze,2025,Auxiliary Classifiers Improve Stability and Efficiency in Continual Learning,"Filip Szatkowski, Fei Yang, Tomasz Trzcinski, Bartłomiej Twardowski, Joost van de Weijer","transfer learning, meta learning, and lifelong learning",reject,Rejected,"[5, 5, 5]",5.0,"[3, 3, 3]",3.0,"[2, 3, 2]",2.33,"[2, 2, 3]",2.33,"[4, 3, 5]",4.0,"[""continual learning"", ""class incremental learning"", ""auxiliary classifiers""]",0,4b66d4c1-0efc-4fa0-9d82-128ef36ded30,2024-09-13,0.0
iclr_1NYhrZynvC,2025,Exact linear-rate gradient descent: optimal adaptive stepsize theory and practical use,Yifan Ran,optimization,reject,Rejected,"[3, 1, 5, 1]",2.5,"[2, 2, 2, 1]",1.75,"[2, 2, 3, 1]",2.0,"[2, 2, 2, 1]",1.75,"[4, 4, 3, 5]",4.0,"[""gradient descent"", ""adaptive stepsize/learning rate"", ""universal optimal choice"", ""exact convergence rate""]",0,0d35c607-62ad-44e3-b26f-c1d7720ad6f0,2024-09-23,0.0
iclr_1MjOlHwCE6,2025,Reducing Complexity of Force-Directed Graph Embedding,"Hamidreza Lotfalizadeh, Omar Yaqub, Mohammad Al Hasan","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[3, 3, 3, 1]",2.5,"[1, 1, 1, 2]",1.25,"[2, 1, 2, 3]",2.0,"[2, 1, 2, 2]",1.75,"[4, 4, 5, 4]",4.25,"[""Graph embedding"", ""Force-directed"", ""representation learning"", ""Spring model"", ""Reduced complexity""]",0,55c24ad0-abd7-4e48-a5ce-95a2bf2f9a65,2024-09-27,0.0
iclr_1Iq1qIsc2s,2025,Revisiting Positional Information in Transformers in the era of Fused Attention,"Aditya Kane, Ali Hassani, Humphrey Shi","applications to computer vision, audio, language, and other modalities",reject,Rejected,"[8, 5, 6]",6.33,"[3, 3, 4]",3.33,"[3, 2, 4]",3.0,"[3, 2, 2]",2.33,"[2, 4, 2]",2.67,"[""Efficient Vision Transformers"", ""Position Embeddings"", ""CUDA""]",0,a308a01e-8c43-455f-9c19-ac444bd4fbab,2024-09-27,0.0
iclr_1IeCqgULIM,2025,Abstracting and Refining Provably Sufficient Explanations of Neural Network Predictions,"Shahaf Bassan, Yizhak Yisrael Elboher, Tobias Ladner, Matthias Althoff, Guy Katz",interpretability and explainable AI,reject,Rejected,"[8, 8, 5, 8]",7.25,"[3, 3, 3, 4]",3.25,"[3, 4, 3, 3]",3.25,"[3, 3, 2, 3]",2.75,"[3, 3, 3, 1]",2.5,"[""explainability"", ""XAI"", ""explainable AI""]",0,7a493b4b-8db2-44dc-9e93-c9ef9066623f,2024-09-23,0.0
iclr_10kBEqYKKN,2025,Impact of Prompt on Latent Representations in LLMs,"Iskandar Boucharenc, Thomas Gerald, Sahar Ghannay, Christophe Servan, Sophie Rosset",interpretability and explainable AI,reject,Rejected,"[3, 3, 3]",3.0,"[2, 2, 1]",1.67,"[1, 1, 2]",1.33,"[1, 1, 1]",1.0,"[4, 4, 4]",4.0,"[""Explainability"", ""Representation analysis"", ""LLM"", ""prompting"", ""zero-shot""]",0,7ef6dae1-cb08-4f4f-bf35-bf003d46ceab,2024-09-27,0.0
iclr_0tIiMNNmdm,2025,Limitations of measure-first protocols in quantum machine learning,"Casper Gyurik, Riccardo Molteni, Vedran Dunjko",learning theory,reject,Rejected,"[3, 6, 6]",5.0,"[2, 3, 3]",2.67,"[1, 3, 4]",2.67,"[1, 3, 4]",2.67,"[3, 4, 5]",4.0,"[""quantum machine learning"", ""machine learning"", ""learning separation""]",12,591fe7d7-7531-4107-9a3c-b27eacdefd67,2024-09-25,0.6316
iclr_0rS9o1uKqu,2025,Training-Like Data Reconstruction,"Pirzada Suhail, Amit Sethi","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[3, 3, 3, 1]",2.5,"[2, 2, 2, 2]",2.0,"[1, 2, 2, 3]",2.0,"[2, 2, 2, 1]",1.75,"[4, 3, 3, 5]",3.75,"[""Network Inversion"", ""Interpretability"", ""Privacy"", ""Training Data Reconstruction""]",3,a3573f85-3824-4c43-8c22-25ef0500ae52,2024-09-28,0.1579
iclr_0je4SA7Jjg,2025,Spatiotemporal Learning on Cell-embedded Graphs,"Yuan Mi, Qi Wang, Hao Sun",learning on time series and dynamical systems,reject,Rejected,"[10, 6, 5, 5]",6.5,"[3, 3, 3, 3]",3.0,"[4, 2, 3, 1]",2.5,"[4, 2, 2, 3]",2.75,"[5, 5, 3, 4]",4.25,"[""Spatiotemporal Dynamics"", ""Graph Learning"", ""Physics-embeded Learning""]",3,857cbde4-c6ca-4218-a69d-b0ad6cc08222,2024-09-25,0.1579
iclr_0iAZYF9hrl,2025,Disentangled representations of microscopy images,"Jacopo Dapueto, Vito Paolo Pastore, Nicoletta Noceti, Francesca Odone","applications to physical sciences (physics, chemistry, biology, etc.)",reject,Rejected,"[3, 3, 1, 3]",2.5,"[2, 2, 1, 2]",1.75,"[2, 2, 1, 1]",1.5,"[1, 1, 2, 1]",1.25,"[5, 3, 5, 4]",4.25,"[""Microscopy images"", ""Disentangled representations"", ""Transfer learning"", ""Interpretability""]",0,a31e8e05-53b6-40b8-a640-2e6ca436b6cd,2024-09-27,0.0
iclr_0VP3LuzZ8K,2025,Generalization of noisy SGD under isoperimetry,"Leello Tadesse Dadi, Volkan Cevher",learning theory,reject,Rejected,"[6, 6, 8, 5]",6.25,"[2, 3, 3, 2]",2.5,"[3, 4, 3, 2]",3.0,"[3, 2, 3, 2]",2.5,"[2, 4, 3, 4]",3.25,"[""generalization"", ""langevin"", ""non-convex"", ""information theory""]",0,0b328c61-55a0-4cfa-a275-b0915d412161,2024-09-27,0.0
iclr_0QZcoGdmtJ,2025,Auditing $f$-Differential Privacy in One Run,"Saeed Mahloujifar, Luca Melis, Kamalika Chaudhuri","alignment, fairness, safety, privacy, and societal considerations",reject,Rejected,"[6, 3, 8, 8]",6.25,"[3, 1, 2, 2]",2.0,"[3, 3, 3, 3]",3.0,"[3, 3, 3, 3]",3.0,"[4, 5, 3, 3]",3.75,"[""Differential privacy"", ""Auditing privacy""]",32,39d9d4f8-d699-4c00-8903-bb7911f2ce14,2024-09-27,1.6842
iclr_0Fi3u4RCyU,2025,Evolve: Evaluating and Optimizing LLMs For Exploration,"Allen Nie, Yi Su, Bo Chang, Jonathan Lee, Ed H. Chi, Quoc V Le, Minmin Chen","foundation or frontier models, including LLMs",reject,Rejected,"[5, 8, 5, 8]",6.5,"[2, 4, 4, 3]",3.25,"[2, 4, 4, 3]",3.25,"[2, 3, 2, 3]",2.5,"[4, 4, 4, 4]",4.0,"[""Large Language Model"", ""Exploration""]",39,f10d389e-1b93-44e3-bead-8f4b94466887,2024-09-27,2.0526
iclr_0ApkwFlCxq,2025,ComputAgeBench: Epigenetic Aging Clocks Benchmark,"Dmitrii Kriukov, Evgeniy Efimov, Kuzmina Ekaterina, Ekaterina Khrameeva, Dmitry V. Dylov",datasets and benchmarks,reject,Rejected,"[8, 6, 5, 6]",6.25,"[3, 4, 3, 3]",3.25,"[3, 3, 2, 3]",2.75,"[3, 2, 2, 2]",2.25,"[3, 4, 4, 5]",4.0,"[""biological age"", ""epigenetic aging clocks"", ""DNA methylation"", ""aging biomarkers"", ""longevity""]",10,5b5d120a-d302-427f-8999-9d55fbebbc67,2024-09-26,0.5263
iclr_09LEjbLcZW,2025,AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions,"Ziming Li, Qianbo Zang, David Ma, Jiawei Guo, Tianyu Zheng, minghao liu, Xinyao Niu, Xiang Yue, Yue Wang, Jian Yang, Jiaheng Liu, Wanjun Zhong, Wangchunshu Zhou, Wenhao Huang, Ge Zhang","foundation or frontier models, including LLMs",reject,Rejected,"[5, 5, 5]",5.0,"[3, 3, 3]",3.0,"[3, 2, 2]",2.33,"[2, 2, 3]",2.33,"[3, 4, 4]",3.67,"[""large language models"", ""language agents"", ""multi-agent""]",67,84ef3933-e578-44df-a6bf-80c9cf938a23,2024-09-28,3.5263
iclr_06ZvHHBR0i,2025,Adversarial Multi-Agent Evaluation of Large Language Models through Iterative Debate,"Chaithanya Bandi, Hari Bandi, Abir HARRASSE","foundation or frontier models, including LLMs",reject,Rejected,"[3, 3, 1, 3]",2.5,"[1, 3, 1, 2]",1.75,"[2, 1, 1, 1]",1.25,"[2, 1, 1, 1]",1.25,"[4, 5, 5, 4]",4.5,"[""LLM Evals"", ""Adversarial analysis"", ""Mechanism Design""]",18,c07be7fe-612d-41a2-b2ba-cde984bf588b,2024-09-28,0.9474
iclr_02Od16GFRW,2025,Ensembles provably learn equivariance through data augmentation,"Oskar Nordenfors, Axel Flinth","unsupervised, self-supervised, semi-supervised, and supervised representation learning",reject,Rejected,"[3, 6, 6]",5.0,"[3, 2, 3]",2.67,"[3, 3, 3]",3.0,"[2, 2, 3]",2.33,"[4, 3, 3]",3.33,"[""equivariance"", ""invariance"", ""ensemble models"", ""data augmentation"", ""SGD""]",4,a086647b-ecae-4795-9446-20092f04fc9e,2024-09-25,0.2105
flaws_2502_06814v2,2025,Diffusion Instruction Tuning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,9b131ba7-7126-410a-aa82-5e78fc8ce15c,,
flaws_2505_23027v1,2025,Diverse Prototypical Ensembles Improve Robustness to Subpopulation Shift,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,8a0e97a6-83e7-458c-b418-0c6fb0406e0c,,
flaws_2506_22696v1,2025,Residual Matrix Transformers: Scaling the Size of the Residual Stream,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,b92b5e71-d646-4dbd-99ca-84243e4ac37e,,
flaws_2502_12920v3,2025,Lightweight Online Adaption for Time Series Foundation Model Forecasts,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,d913eba8-4b14-4e68-929b-69ae656fa46f,,
flaws_2506_02712v1,2025,Theoretical Performance Guarantees for Partial Domain Adaptation via Partial Optimal Transport,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,56510d2c-1f77-42d9-8946-de181924e956,,
flaws_2501_14372v2,2025,Reinforcement Learning for Quantum Control under Physical Constraints,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,7cd52061-dce7-4597-93d6-de4cc9c2db97,,
flaws_2509_05137v1,2025,On the Learnability of Distribution Classes with Adaptive Adversaries,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,1bfd51e5-bb46-4340-9b47-04145e9a3cff,,
flaws_2502_01951v4,2025,On the Emergence of Position Bias in Transformers,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,b3258c9d-54b6-49fa-8e58-f818334736a1,,
flaws_2503_01328v2,2025,PipeOffload: Improving Scalability of Pipeline Parallelism with Memory Optimization,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,1d4e9128-cc98-4e24-8099-3f7eef4fd056,,
flaws_2501_18527v3,2025,Neural Discovery in Mathematics: Do Machines Dream of Colored Planes?,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,4c178e96-a68b-4082-b0fb-8f3f172dc6d3,,
flaws_2503_18731v2,2025,Thermalizer: Stable autoregressive neural emulation of spatiotemporal chaos,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,a276f14d-75d9-4922-a651-4811eeb9ebd8,,
flaws_2505_20840v1,2025,Aggregation Buffer: Revisiting DropEdge with a New Parameter Block,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,6c1c3ce9-1485-47d2-b0b3-9c321fc434a5,,
flaws_2505_01763v1,2025,Quantum Speedup for Hypergraph Sparsification,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,bd67d89a-b481-4631-9c7c-45d82baf4419,,
flaws_2505_08092v2,2025,Doubly Robust Fusion of Many Treatments for Policy Learning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,22fa4423-49cb-417b-8f61-315bc7e1b85e,,
flaws_2503_21592v2,2025,Simple and Critical Iterative Denoising: A Recasting of Discrete Diffusion in Graph Generation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,3c42b7ba-a9ec-4ba0-95f6-1fc929d04763,,
flaws_2502_13228v2,2025,Conformal Prediction as Bayesian Quadrature,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,f858dc29-b280-47e0-a7f5-55a5b7f9e624,,
flaws_2502_12150v2,2025,Idiosyncrasies in Large Language Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,f185c8e2-9cca-4080-9701-4f9ad0af1680,,
flaws_2502_15280v2,2025,Hyperspherical Normalization for Scalable Deep Reinforcement Learning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,ef9d8300-6b40-41e9-9a11-78796cffad49,,
flaws_2505_17552v2,2025,Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,cefd7e27-fd9c-4056-9e54-407128ab686d,,
flaws_2505_22449v1,2025,Private Lossless Multiple Release,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,5a3819e1-5e28-4d5b-a5d2-7e8a4948ef01,,
flaws_2501_14082v2,2025,Communicating Activations Between Language Model Agents,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,82e7006c-9908-41a1-aa83-6d6ccce34181,,
flaws_2505_03561v1,2025,Ergodic Generative Flows,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,f659f5f8-e075-4792-a4d6-35a6b45663a1,,
flaws_2506_18340v3,2025,Controlled Generation with Equivariant Variational Flow Matching,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,210191b4-7d12-4e67-b430-d74c3e04eeb8,,
flaws_2503_15200v2,2025,Partially Observable Reinforcement Learning with Memory Traces,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,c1d573ba-d8f4-403c-b7e9-98a7a338e166,,
flaws_2506_02923v1,2025,The Limits of Predicting Agents from Behaviour,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,926c42e6-2e69-4fb6-91ba-d9f7605ea9fc,,
flaws_2505_06948v1,2025,Unsupervised Learning for Class Distribution Mismatch,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,dc8e6aab-4134-4936-9eb0-2cf59da9ce77,,
flaws_2508_11697v1,2025,Separating Knowledge and Perception with Procedural Data,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,93f1c714-773a-447b-8871-0c43a0466c87,,
flaws_2506_13234v1,2025,The Butterfly Effect: Neural Network Training Trajectories Are Highly Sensitive to Initial Conditions,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,84bceb1b-947e-4d9d-8f88-a9178f545d31,,
flaws_2502_01633v2,2025,Adversarial Reasoning at Jailbreaking Time,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,48a96aaf-9f20-476a-a4cf-01fda643c2c8,,
flaws_2507_04075v1,2025,Accurate and Efficient World Modeling with Masked Latent Transformers,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,4f9edd93-ee94-4eb7-bb7b-5797e4b76a16,,
flaws_2502_05759v4,2025,Reinforced Lifelong Editing for Language Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,2205f838-5642-48d8-86c8-03e6a74bae4f,,
flaws_2505_22438v1,2025,Synonymous Variational Inference for Perceptual Image Compression,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,c0633aed-0ef2-42f9-afc3-f7d9ee0fdb52,,
flaws_2502_16056v1,2025,Since Faithfulness Fails: The Performance Limits of Neural Causal Discovery,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,4f64f60a-8537-4fba-b6c1-9c8d3b55e56a,,
flaws_2505_20697v3,2025,Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,49fb021b-f63e-4d90-b072-fe6be7f8f426,,
flaws_2506_22769v1,2025,Learning Efficient Robotic Garment Manipulation with Standardization,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,a247a17e-1112-41f1-937f-f4e1c90e9154,,
flaws_2502_01512v4,2025,Wrapped Gaussian on the manifold of Symmetric Positive Definite Matrices,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,657baaf1-ac75-475d-8b1b-0442b6cf8e1d,,
flaws_2505_13740v2,2025,Improving Compositional Generation with Diffusion Models Using Lift Scores,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,a0e94355-d294-4c48-b64e-9c6539aa98e4,,
flaws_2502_01189v4,2025,Compressed Image Generation with Denoising Diffusion Codebook Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,,,
flaws_2504_11393v2,2025,DataDecide: How to Predict Best Pretraining Data with Small Experiments,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,2f9bc2e0-7963-451a-8ee3-7882001637cb,,
flaws_2505_19097v2,2025,Towards Robust Influence Functions with Flat Validation Minima,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,2e84b315-2584-4b6b-9b89-962ea2f714b9,,
flaws_2505_08735v1,2025,Preference Optimization for Combinatorial Optimization Problems,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,fb3e68be-2d17-4e65-a17e-dfd61e4d2510,,
flaws_2504_14783v2,2025,How Effective Can Dropout Be in Multiple Instance Learning ?,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,41ce2961-037e-4aed-82ba-a72f3c5107b0,,
flaws_2503_05662v1,2025,On Mitigating Affinity Bias through Bandits with Evolving Biased Feedback,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,51c5ef0e-14ef-4298-a0ab-18ba77af426e,,
flaws_2505_11478v2,2025,Automatic Reward Shaping from Confounded Offline Data,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,20b10aa7-9275-4ee5-acf5-984583774ee8,,
flaws_2502_18605v2,2025,Expected Variational Inequalities,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,0f47ec75-832b-4f05-8000-5d090f77a27e,,
flaws_2502_08007v1,2025,The Role of Randomness in Stability,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,8488955a-70a8-4482-9d66-46c842ced2e3,,
flaws_2502_12089v3,2025,How Compositional Generalization and Creativity Improve as Diffusion Models are Trained,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,2bdf42a6-2fb1-4344-9b8f-048a57c60904,,
flaws_2502_11229v2,2025,Provable and Practical Online Learning Rate Adaptation with Hypergradient Descent,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,7f73a728-8ff4-42dc-a705-cf8d548c7353,,
flaws_2501_18836v1,2025,Transfer Learning for Nonparametric Contextual Dynamic Pricing,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,87bbb2c9-4d1e-4b17-9dc6-1e2520291b13,,
flaws_2503_10537v2,2025,Structured Preconditioners in Adaptive Optimization: A Unified Analysis,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,d0a48502-8901-4324-986c-56f27ca93263,,
flaws_2505_19313v1,2025,Concept Reachability in Diffusion Models: Beyond Dataset Constraints,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,575cf4e4-7adc-4f66-b9b3-ac7193c4205e,,
flaws_2502_10581v2,2025,Do We Need to Verify Step by Step? Rethinking Process Supervision from a Theoretical Perspective,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,,,
flaws_2505_14138v1,2025,Sample Complexity of Correlation Detection in the Gaussian Wigner Model,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,9e534aa7-d1dd-47f8-856a-9fcea1eba3be,,
flaws_2506_07952v2,2025,Discrete and Continuous Difference of Submodular Minimization,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,7a275799-3cd6-42d8-ab4e-0da827b77a76,,
flaws_2502_00816v2,2025,Sundial: A Family of Highly Capable Time Series Foundation Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,25e6e77b-979a-456e-b95d-ffeb89949aeb,,
flaws_2508_09654v1,2025,"Improving Diversity in Language Models: When Temperature Fails, Change the Loss",,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,47b9fc27-77cd-4f44-925f-7eb91e0d8b48,,
flaws_2502_00298v2,2025,The Price of Linear Time: Error Analysis of Structured Kernel Interpolation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,b57cfffb-e753-4696-be68-d8226ee4085d,,
flaws_2506_12781v1,2025,Unconstrained Robust Online Convex Optimization,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,46c00f82-0d70-49ca-bc81-d5c1cbb9926c,,
flaws_2502_03492v1,2025,Teaching Language Models to Critique via Reinforcement Learning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,ff2b92c9-453e-4c40-8f36-5f6e190ca838,,
flaws_2506_10952v1,2025,Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,2a9c9110-eb17-4401-9076-de3c7995787e,,
flaws_2503_01496v2,2025,Liger: Linearizing Large Language Models to Gated Recurrent Structures,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,5edd5ac7-fc1c-4136-94d1-edd12922d0e3,,
flaws_2505_01212v2,2025,High Dynamic Range Novel View Synthesis with Single Exposure,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,9a3fdec7-4196-4979-8193-7869d5017e8a,,
flaws_2502_12542v1,2025,Computing Voting Rules with Improvement Feedback,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,9f917e3f-6846-4ff7-8ccc-9694bd5d46f7,,
flaws_2506_03839v1,2025,Revisiting Unbiased Implicit Variational Inference,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,2ada3986-d13a-4022-904c-00b7a5b56133,,
flaws_2507_07418v1,2025,Optimal Auction Design in the Joint Advertising,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,a1794eea-55c0-4ce5-9da8-0edf1c82001d,,
flaws_2502_12528v2,2025,Contextual Linear Bandits with Delay as Payoff,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,aaae84d3-0bb4-463a-bd7a-72cf909ee446,,
flaws_2505_24445v1,2025,Learning Safety Constraints for Large Language Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,147d4c6f-9ffd-48e6-b2b3-3021ace0a795,,
flaws_2506_17927v1,2025,Safety Certificate against Latent Variables with Partially Unidentifiable Dynamics,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,eff57196-6de9-4505-b3a1-d564de21f120,,
flaws_2502_03618v2,2025,The Logical Implication Steering Method for Conditional Interventions on Transformer Generation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,63dd351c-7f65-4de0-ac9c-40413b8fa69f,,
flaws_2505_18909v1,2025,On the Role of Label Noise in the Feature Learning Process,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,38b9e631-fce7-4d77-90a1-b6984cd60f8c,,
flaws_2502_04909v2,2025,Benchmarking Quantum Reinforcement Learning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,eed1173d-e174-44d9-8edf-bef0f13a3ffe,,
flaws_2510_00136v1,2025,Nonparametric Identification of Latent Concepts,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,aa9cc3ae-661d-406f-bdeb-c4d2bd0ecf08,,
flaws_2506_12087v1,2025,Efficient Parallel Training Methods for Spiking Neural Networks with Constant Time Complexity,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,01565f4b-de8e-46bf-b0c8-59ec2580ba86,,
flaws_2501_18283v4,2025,Random Feature Representation Boosting,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,9fe1c5db-f512-4a2f-83e2-3dd8c90d3176,,
flaws_2502_01425v1,2025,The Batch Complexity of Bandit Pure Exploration,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,636e9910-95dc-46a6-9e2e-35ece1fa6d64,,
flaws_2504_16968v3,2025,BackSlash: Rate Constrained Optimized Training of Large Language Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,6277ed64-30e1-492c-a4a4-b6e2d3b90bcd,,
flaws_2502_07778v2,2025,Stay-Positive: A Case for Ignoring Real Image Features in Fake Image Detection,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,5da5a998-3eb1-4525-a35c-17e7519c0e92,,
flaws_2507_09177v1,2025,Continual Reinforcement Learning by Planning with Online World Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,643ab467-56f2-4230-bfd8-87cafcb58f17,,
flaws_2508_00264v1,2025,Calibrated Language Models and How to Find Them with Label Smoothing,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,62350b41-bc67-46e0-ae54-68b75624f118,,
flaws_2502_04397v3,2025,Multimodal Medical Code Tokenizer,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,aa70723c-1f8b-4a4b-8b7d-b4d4e293f4fe,,
flaws_2506_09215v1,2025,Robust Noise Attenuation via Adaptive Pooling of Transformer Outputs,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,c62e879a-bdb7-4be6-b76e-06c339ce327c,,
flaws_2505_02435v2,2025,A New Approach to Backtracking Counterfactual Explanations: A Unified Causal Framework for Efficient Model Interpretability,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,07e616b0-846a-4565-bfe7-d7d899c5efbe,,
flaws_2505_14903v1,2025,When to retrain a machine learning model,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,,,
flaws_2503_00089v2,2025,{Protein Structure Tokenization: Benchmarking and New Recipe,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,075f71ac-d6a1-4b5b-b5fd-983af7195a21,,
flaws_2506_07947v1,2025,Statistical Hypothesis Testing for Auditing Robustness in Language Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,388fa647-2883-4181-9839-61613dd99652,,
flaws_2507_08438v2,2025,Optimal and Practical Batched Linear Bandit Algorithm,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,c89319f6-e4b1-49aa-b123-25efb9f1655b,,
flaws_2505_22152v1,2025,Uncertainty Estimation for Heterophilic Graphs Through the Lens of Information Theory,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,bf98fa60-e9e1-48a0-905f-c481deab06c8,,
flaws_2502_04850v2,2025,Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,64340a9a-6126-4283-840b-8d9946b000bd,,
flaws_2503_02169v2,2025,"One Stone, Two Birds: Enhancing Adversarial Defense Through the Lens of Distributional Discrepancy",,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,45d5997c-4303-4ce9-8934-34e3af8290ec,,
flaws_2505_17524v1,2025,Latent Imputation before Prediction: \linebreak A New Computational Paradigm for \emph{De Novo,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,f3388c16-0c64-4e53-a1ed-9cb397a765e6,,
flaws_2506_02053v1,2025,Generalization Performance of Ensemble Clustering: From Theory to Algorithm,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,0ec2139e-7d1e-4a8a-bdea-02943b14a795,,
flaws_2501_09755v1,2025,Learnings from Scaling Visual Tokenizers for Reconstruction and Generation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,6aab916e-f659-4d5b-b21f-ed5a42d5311d,,
flaws_2505_19105v2,2025,Latent Mamba Operator for Partial Differential Equations,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,fa427c19-6997-4e1e-8403-e3fc9320fc64,,
flaws_2505_15141v1,2025,BanditSpec: Adaptive Speculative Decoding via Bandit Algorithms,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,494daba9-9dde-4f5f-88a8-23908a6f5f26,,
flaws_2506_09940v1,2025,The Sample Complexity of Online Strategic Decision Making with Information Asymmetry and Knowledge Transportability,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,c28d5213-9ee0-4b82-bad7-800bd2582344,,
flaws_2502_18890v2,2025,TokenSwift: Lossless Acceleration of Ultra Long Sequence Generation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,507f5262-0730-42ef-a1ca-414b4c9832ee,,
flaws_2503_08908v1,2025,Interpreting the Repeated Token Phenomenon in Large Language Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,40bb6c7a-fad6-474e-a724-ea23792a2cd6,,
flaws_2507_07621v1,2025,Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,24b85e58-9b4d-4802-a3b5-41e72b7b7a13,,
flaws_2503_04957v1,2025,SafeArena: Evaluating the Safety of Autonomous Web Agents,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,,,
flaws_2505_17542v2,2025,Graph Inverse Style Transfer for Counterfactual Explainability,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,ea121431-96b8-45da-b759-5ef091da0b9a,,
flaws_2506_09724v1,2025,The Four Color Theorem for Cell Instance Segmentation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,d1299b85-7192-47a1-b8cd-e4f1fea2b885,,
flaws_2506_06853v1,2025,Curvature Enhanced Data Augmentation for Regression,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,c2c7dfc1-a18c-46ae-9966-c8954ef40996,,
flaws_2501_01045v4,2025,ZeroFlow,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,0384ba26-dc43-41dd-8118-33a14d4cfdfd,,
flaws_2505_23609v1,2025,The Generalized Skew Spectrum of Graphs,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,93a22830-f565-45a7-bdec-9d3a2ef641a5,,
flaws_2506_15926v1,2025,Competing Bandits in Matching Markets via Super Stability,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,d71f279b-f43d-4cee-ab7f-f9814b881868,,
flaws_2506_17248v1,2025,Efficient Quantification of Multimodal Interaction at Sample Level,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,c1894e89-4a33-4d3d-ab78-63dc0ff1a94a,,
flaws_2501_15602v3,2025,Rethinking External Slow-Thinking: From Snowball Errors to Probability of Correct Reasoning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,ec8949fd-a0d8-465e-9976-64e66c1e4432,,
flaws_2505_21576v1,2025,Concentration Distribution Learning from Label Distributions,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,fec35d03-563e-403f-bc7d-ce3f2c061149,,
flaws_2503_12314v2,2025,Empirical Privacy Variance,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,73ac591b-0a74-414f-bc27-22b88ae70b70,,
flaws_2506_21521v2,2025,Potemkin Understanding in Large Language Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,a17ad7c0-9923-43dd-9222-f86e9b8d5e7f,,
flaws_2504_07165v1,2025,Perception in Reflection,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,54e1b416-364a-4779-a4c0-b2ab0994ae19,,
flaws_2502_03358v2,2025,Minerva: A Programmable \textit{Memory Test,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,94dd1160-bfbe-4069-aa3e-aa5b9e577a01,,
flaws_2502_09591v2,2025,Censor Dependent Variational Inference,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,e2675281-2f1b-4490-9644-e27cb9d8d09b,,
flaws_2507_11372v1,2025,Attributes Shape the Embedding Space of Face Recognition Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,c9ca08da-e894-4710-8aa6-c3d20c5a729f,,
flaws_2506_01054v1,2025,No Soundness in the Real World: On the Challenges of the Verification of Deployed Neural Networks,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,0a443d8c-93ae-44f3-9a2f-4367ea59830d,,
flaws_2502_02483v3,2025,Distributional Diffusion Models with Scoring Rules,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,9b620b5d-8660-403c-9dcf-cbe4f3356c8b,,
flaws_2502_02921v3,2025,Robust Reward Alignment via Hypothesis Space Batch Cutting,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,96132867-e077-4b9e-b6d3-1cc28f2d17cf,,
flaws_2507_02782v1,2025,Understanding and Improving Length Generalization in Recurrent Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,4f39a83f-d57c-4618-8256-c5b6cf6bb553,,
flaws_2505_09586v1,2025,Rhomboid Tiling for Geometric Graph Deep Learning \thanks{\textit{\underline{Citation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,34add534-219f-4834-a11b-955d34de7f2e,,
flaws_2502_00277v2,2025,Regularized Langevin Dynamics for Combinatorial Optimization,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,2ab12f68-542d-4bf3-89d4-f05e877aeb8f,,
flaws_2506_08505v1,2025,"Explaining, Fast and Slow: Abstraction and Refinement of Provable Explanations",,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,fb4e038f-c2de-4811-8be2-440429f356b7,,
flaws_2505_19024v1,2025,Learn Beneficial Noise as Graph Augmentation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,2316fc24-b83f-4c72-a23a-6220f7fe5f16,,
flaws_2502_02129v1,2025,Deep Neural Cellular Potts Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,a063a3bb-9c08-4534-8a02-51dc17770d3c,,
flaws_2502_08020v2,2025,"Speculate, then Collaborate: Fusing Knowledge of Language Models during Decoding",,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,24faf82a-c1f1-4002-a594-e29bf2ca2e74,,
flaws_2502_03032v3,2025,Analyze Feature Flow to Enhance Interpretation and Steering in Language Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,d3ceb48a-692d-451c-af82-d3970ce199f2,,
flaws_2505_22048v1,2025,Learning Curves of Stochastic Gradient Descent in Kernel Regression,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,47fca722-4be1-4ac6-a6b9-37047b22de57,,
flaws_2506_06985v2,2025,Certified Unlearning for Neural Networks,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,b1dbabbf-440e-4396-87bc-dfe66d7df904,,
flaws_2502_04495v2,2025,Discovering Physics Laws of Dynamical Systems via Invariant Function Learning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,e51179ac-9b09-40cb-afba-9b22539c1402,,
flaws_2505_06699v3,2025,Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,e015cb70-760f-4547-861b-356e05966701,,
flaws_2503_07197v2,2025,Effective and Efficient Masked Image Generation Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,295dccc3-d0f8-496c-a3a6-20aa9f35c01c,,
flaws_2506_17868v1,2025,Geometric Contact Flows: Contactomorphisms for Dynamics and Control,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,6eb3c92b-7aed-41e0-bebe-123d8958558c,,
flaws_2506_03919v1,2025,Weisfeiler and Leman Go Gambling: Why Expressive Lottery Tickets Win,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,029f4513-6a00-4143-bc25-864e1acc5bbd,,
flaws_2501_18914v1,2025,Scaling Laws for Differentially Private Language Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,0ceeaa54-874b-4a43-a43c-7e31ca875d09,,
flaws_2505_24688v4,2025,Soft Reasoning: Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,0aefc16e-97f8-44b2-9d49-b842110cad9b,,
flaws_2506_05196v1,2025,Locality Preserving Markovian Transition for Instance Retrieval,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,5eee5d6a-e7e1-430e-81b8-4ee3259fc59c,,
flaws_2503_08501v2,2025,Learning to Match Unpaired Modalities with Minimum Entropy Coupling,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,72335f40-9016-4fae-ba7a-6046109b9c21,,
flaws_2502_04720v3,2025,Fluctuations of the largest eigenvalues of transformed spiked Wigner matrices,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,857f439f-c0ca-4a2f-be67-43237466cdc5,,
flaws_2505_22483v2,2025,A Closer Look at Multimodal Representation Collapse,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,41c63a89-bd85-4dc9-89b2-036c4ea533a2,,
flaws_2503_14378v1,2025,Impossible Videos,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,29604893-ddd1-483a-ae80-c2ff2e6d2ae0,,
flaws_2505_21819v1,2025,Representative Language Generation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,9a47f917-ce62-488a-adca-8c81159c1c76,,
flaws_2507_00191v1,2025,Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,247587ce-301d-42be-b0fb-3e4d2d1b9602,,
flaws_2501_17079v1,2025,Learning Mean Field Control on Sparse Graphs,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,67cfb5f6-ddc8-4217-960b-b5646f8da361,,
flaws_2506_10632v1,2025,Hessian Geometry of Latent Space in Generative Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,570eff56-9f80-4215-87f5-af0e8556b9c4,,
flaws_2505_12378v1,2025,Efficient Optimization with Orthogonality Constraint: a Randomized Riemannian Submanifold Method,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,56c968fb-8c7b-49c5-94f9-3e341e9426ef,,
flaws_2502_20332v2,2025,Emergent Symbolic Mechanisms Support Abstract Reasoning in Large Language Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,67d1806c-c3ce-4b15-9d8c-0e243218764c,,
flaws_2502_06785v2,2025,DeepCrossAttention: Supercharging Transformer Residual Connections,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,bf2df11a-ffb5-45b8-be15-7afe5d27aaee,,
flaws_2505_18023v2,2025,Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,5d73eb34-9578-4f5c-aa86-a4019e36c241,,
flaws_2506_13672v1,2025,The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,43bc5081-575c-41e4-9c0f-44e34669f257,,
flaws_2508_01957v3,2025,Stochastic Encodings for Active Feature Acquisition,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,c1dccd1b-dc11-4ab6-a75b-b390465864a8,,
flaws_2503_04429v4,2025,Activation Space Interventions Can Be Transferred Between Large Language Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,5dcf6364-0a22-4fe3-b732-14aa24e9c948,,
flaws_2502_17874v2,2025,Neural Graph Matching Improves Retrieval Augmented Generation in Molecular Machine Learning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,34ec4268-3c1f-4715-b036-5a39e38def75,,
flaws_2506_12352v2,2025,Efficient Network Automatic Relevance Determination,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,9465d251-e1ec-4488-beaa-1e2511e2f6c4,,
flaws_2502_08075v2,2025,Knowledge Swapping via Learning and Unlearning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,4259d8de-ee5d-4d6b-a3c3-d189b25682e2,,
flaws_2505_05143v2,2025,Sparse Training from Random Initialization: Aligning Lottery Ticket Masks using Weight Symmetry,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,486838fb-ffc2-4501-8712-403f85369e84,,
flaws_2506_13320v1,2025,Action Dubber: Timing Audible Actions via Inflectional Flow,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,c5461b9c-3428-468b-b47a-8b57f43f4a00,,
flaws_2502_04686v3,2025,Learning Strategic Language Agents in the Werewolf Game with Iterative Latent Space Policy Optimization,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,b84aeb91-02dc-44d2-9d1f-c7cc0bc7a8a2,,
flaws_2505_21363v3,2025,Subgroups Matter for Robust Bias Mitigation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,14c068c2-c3d3-411e-96a3-b3e69dc1e98b,,
flaws_2502_05397v2,2025,Imitation Learning from a Single Temporally Misaligned Video,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,0460c924-4907-4e1a-ac73-3561f337a5c2,,
flaws_2502_10505v2,2025,Preference learning made easy: Everything should be understood through win rate,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,6425a81e-ed68-41f6-9c62-45c7ef7a6ad6,,
flaws_2505_22899v2,2025,On the Dynamic Regret of Following the Regularized Leader: Optimism with History Pruning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,7a6df0b1-6903-46d4-b517-93b154f52bfc,,
flaws_2503_03043v2,2025,Leveraging Randomness in Model and Data Partitioning for Privacy Amplification,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,23c41d0b-4d06-43ab-874e-247d0c79e798,,
flaws_2502_06806v4,2025,Logits are All We Need to Adapt Closed Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,0264ec93-b2d5-470d-9798-fd7e4ce1990b,,
flaws_2503_13925v1,2025,Reconstructing Cell Lineage Trees from Phenotypic Features with Metric Learning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,d3711b6c-2ac7-43d8-a7bd-24aa3ad9ffad,,
flaws_2505_02288v1,2025,Universal Approximation Theorem of Deep Q-Networks,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,ee5832f9-de7f-41fc-8c2e-2bc1d0b9e3b0,,
flaws_2504_12264v1,2025,Towards Learning to Complete Anything in Lidar,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,4681bf2e-12b9-4a2c-83e1-55f135cd5949,,
flaws_2502_09775v3,2025,CellFlux,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,49b2929e-dd7a-45f2-8635-606a0961ed7b,,
flaws_2504_17921v3,2025,Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,679d2b9f-dd89-4fa8-8ee7-1347897a584b,,
flaws_2502_16870v3,2025,Distributionally Robust Active Learning for Gaussian Process Regression,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,a79e1f54-0c35-425d-8aa6-90152c4991ea,,
flaws_2503_10489v2,2025,Beyond Atoms: Enhancing Molecular Pretrained Representations with 3D Space Modeling,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,c1d869ee-1e77-48e6-8f13-938860509509,,
flaws_2508_10800v1,2025,Competitively Consistent Clustering,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,8396f16c-4502-478d-ae97-5a29de65f715,,
flaws_2502_11909v3,2025,Neural Guided Diffusion Bridges,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,f6340d1d-201b-47c0-9b54-677feef81b09,,
flaws_2505_21790v1,2025,Faster Rates for Private Adversarial Bandits,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,4dcd77fa-a10b-477e-bb05-a1a6479a391a,,
flaws_2505_08266v3,2025,Open Your Eyes: Vision Enhances Message Passing Neural Networks in Link Prediction,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,3d6adc7b-947e-4e91-8d64-1582c6b24658,,
flaws_2505_18442v1,2025,Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,aea82a2c-7210-47d0-b3a1-4892718932fa,,
flaws_2502_16658v1,2025,Volume Optimality in Conformal Prediction with Structured Prediction Sets,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,f569a1fe-5d30-446f-8040-7a4badd6914b,,
flaws_2502_10433v2,2025,Neural Genetic Search in Discrete Spaces,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,d22527c1-ad6b-4cee-9587-11d749ede6fb,,
flaws_2506_00432v1,2025,Channel Normalization for Time Series Channel Identification,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,33ad0309-5510-44a4-bdc8-963e0fdf4569,,
flaws_2505_01134v1,2025,Aggregation of Dependent Expert Distributions in Multimodal Variational Autoencoders,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,4a490b23-6902-45ea-9396-46fd8a5edb54,,
flaws_2505_00209v1,2025,Direct Motion Models for Assessing Generated Videos,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,aacea9ac-0066-4f23-960d-667f942631a3,,
flaws_2503_18938v4,2025,AdaWorld: Learning Adaptable World Models with Latent Actions,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,df74f94a-6dbd-4439-a524-b40985c602c5,,
flaws_2504_10777v2,2025,AtlasD: Automatic Local Symmetry Discovery,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,481b02fc-fe3f-43db-b217-37b7ee096afe,,
flaws_2503_19300v3,2025,UniMoMo: Unified Generative Modeling of 3D Molecules for \textit{De Novo,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,c4df9989-5580-41e9-801c-cbd7fdb26669,,
flaws_2504_18394v2,2025,Maximum Coverage in Turnstile Streams with Applications to Fingerprinting Measures,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,9b5b1479-7dc6-493f-b67e-311fd5220baa,,
flaws_2505_07688v1,2025,Heterogeneous Data Game: Characterizing the Model Competition Across Multiple Data Sources,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,1036b900-a9b6-491a-abb4-253a826ae5b8,,
flaws_2502_11673v2,2025,Best of Both Worlds: Regret Minimization versus Minimax Play,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,81588bd2-f8e4-49aa-ab5b-18587027d83a,,
flaws_2502_06768v3,2025,"Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions",,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,b2cffb2e-a25c-4d88-8e57-34da9b476887,,
flaws_2508_12576v1,2025,Widening the Network Mitigates the Impact of Data Heterogeneity on FedAvg,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,11cbe0c6-5f81-417f-ae4a-da6d3d3203fe,,
flaws_2502_20033v2,2025,Recommendations with Sparse Comparison Data: Provably Fast Convergence for Nonconvex Matrix Factorization,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,86e98527-e435-416d-ac20-bcca03c4022e,,
flaws_2502_00379v5,2025,Latent Action Learning Requires Supervision in the Presence of Distractors,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,14a1d45a-e3b7-4be0-b4a2-9b9d9cb0405b,,
flaws_2502_00338v3,2025,OneForecast: A Universal Framework for Global and Regional Weather Forecasting,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,7afb6639-c8c8-4ac3-996c-2cd3a4928cdc,,
flaws_2504_11454v3,2025,Elucidating the Design Space of Multimodal Protein Language Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,c43b774a-587e-4872-a02c-3fd4f966a966,,
flaws_2507_13386v1,2025,Minimalist Concept Erasure in Generative Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,f699c152-a3cf-446a-ba99-a3b32671056b,,
flaws_2505_15174v2,2025,Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,86ab8d3e-05df-4511-82de-7b8e20e6ad37,,
flaws_2502_20770v2,2025,Learning to Steer Learners in Games,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,3c18e772-b726-45a9-8be1-7c797a3fd6da,,
flaws_2505_18568v1,2025,Learning without Isolation: Pathway Protection for Continual Learning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,d44c24f8-162c-49b8-9dc5-4d466bd36d25,,
flaws_2503_00808v4,2025,Predictive Data Selection: The Data That Predicts Is the Data That Teaches,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,bbb27e51-15d8-4474-a544-e196b94a1cc3,,
flaws_2506_13095v1,2025,Learning Event Completeness for Weakly Supervised Video Anomaly Detection,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,84bdfe0a-a29a-4ba9-a7f9-83862ac09f81,,
flaws_2505_23760v1,2025,Model Immunization from a Condition Number Perspective,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,e571e9fd-af5e-4d4c-bcea-597b17c48088,,
flaws_2505_15803v1,2025,Adaptive Estimation and Learning under Temporal Distribution Shift,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,019a280f-8ecc-41b8-8a68-91661463870d,,
flaws_2502_21278v1,2025,Does Generation Require Memorization? Creative Diffusion Models using Ambient Diffusion,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,50b9e656-3c89-4964-a2b4-aa69e85161bd,,
flaws_2502_11612v3,2025,Maximum Entropy Reinforcement Learning with Diffusion Policy,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,069ed819-490c-45aa-b54b-3eb09b2ef84f,,
flaws_2506_07534v1,2025,Flowing Datasets with Wasserstein over Wasserstein Gradient Flows,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,f2b38c08-23ab-43a4-bf1d-1095b490a400,,
flaws_2505_20853v2,2025,Cooperation of Experts: Fusing Heterogeneous Information with Large Margin,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,57c86c7c-09a2-4c03-861c-b7b086cd5141,,
flaws_2505_23152v1,2025,Provable Benefit of Random Permutations over Uniform Sampling in Stochastic Coordinate Descent,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,1b1537bc-9999-48d3-ab7d-2e48e176540e,,
flaws_2502_03444v2,2025,Masked Autoencoders Are Effective Tokenizers for Diffusion Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,280d45c0-d3a3-4b76-8983-e407cf232361,,
flaws_2505_23967v1,2025,Improved Approximations for Hard Graph Problems using Predictions,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,577ddcc4-56c1-4c17-b838-dbca2479af57,,
flaws_2502_01662v2,2025,Fast Large Language Model Collaborative Decoding via Speculation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,e15277f5-512f-414e-8d85-65407282f0cb,,
flaws_2501_14544v2,2025,Distributed Conformal Prediction via Message Passing,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,569bcdde-9a37-439c-94d5-9b35d387cbdf,,
flaws_2502_12292v2,2025,Independence Tests for Language Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,2ccd08fa-9f89-489f-8d99-3239fb1cd4d1,,
flaws_2505_02118v5,2025,Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,b076cbec-799a-467b-9f88-d839fbf6eaf1,,
flaws_2505_11586v1,2025,The Ripple Effect: On Unforeseen Complications of Backdoor Attacks,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,30f35559-e81c-48de-b482-6411f9f423fb,,
flaws_2503_16057v3,2025,Expert Race: A Flexible Routing Strategy for Scaling Diffusion Transformer with Mixture of Experts,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,9e7c0fb4-30fd-4b1d-b515-bd4b7ff18cc5,,
flaws_2501_18901v2,2025,Lightspeed Geometric Dataset Distance via Sliced Optimal Transport,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,7b507944-3aaa-4359-bc91-dd0756b99264,,
flaws_2506_09781v2,2025,On the Similarities of Embeddings in Contrastive Learning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,9dd325a0-6827-4903-bf75-4c2f1a7563de,,
flaws_2505_21114v1,2025,Differentiable Solver Search for Fast Diffusion Sampling,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,,,
flaws_2506_08747v1,2025,A Sample-Efficient Conditional Independence Test in the Presence of Discretization,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,59362f69-8e89-4f91-9d1d-404597e93776,,
flaws_2502_13967v2,2025,FlexTok: Resampling Images into 1D Token Sequences of Flexible Length,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,925ecc0f-fda9-40a2-8e49-f4dc478b7bc9,,
flaws_2502_00488v3,2025,Learn Singularly Perturbed Solutions via Homotopy Dynamics,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,58324015-655f-4c36-86d6-57e1d9a8bac3,,
flaws_2502_16681v1,2025,Are Sparse Autoencoders Useful? A Case Study in Sparse Probing,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,640d98e2-72e8-4b91-bb72-f4fe2723a7df,,
flaws_2506_06895v2,2025,Scalable Gaussian Processes with Latent Kronecker Structure,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,251d1710-17d8-47b1-b0fe-8a3a1f2404ba,,
flaws_2502_20260v1,2025,Understanding the Limits of Deep Tabular Methods with Temporal Shift,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,3c694ca1-3a80-43a0-b467-789b8a7a69a7,,
flaws_2506_05503v1,2025,On Differential Privacy for Adaptively Solving Search Problems via Sketching,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,35d7484b-7e1e-49bd-8f88-424af586ed24,,
flaws_2502_10927v2,2025,"The underlying structures of self-attention: symmetry, directionality, and emergent dynamics in Transformer training",,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,46fec557-8f8e-4534-babc-52f5587d6506,,
flaws_2503_02870v3,2025,{\bf Multiaccuracy and Multicalibration via Proxy Groups,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,be0eaf1d-ef14-4575-97c5-66a7b2dc3207,,
flaws_2506_04870v1,2025,Aligning Multimodal Representations through an Information Bottleneck,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,faf459d6-0de0-4189-8cb9-35106d20f936,,
flaws_2502_08878v2,2025,Scalable Private Partition Selection via Adaptive Weighting,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,4838c046-4cca-4dd5-ae25-038b8240486a,,
flaws_2505_00917v1,2025,Multivariate Conformal Selection,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,7dd5e4ff-85d5-4e0f-ad8c-009f07bf2f09,,
flaws_2507_02606v1,2025,De-AntiFake: Rethinking the Protective Perturbations Against Voice Cloning Attacks,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,236562ec-8d36-4829-9542-6745ff259655,,
flaws_2505_00926v3,2025,How Transformers Learn Regular Language Recognition: A Theoretical Study on Training Dynamics and Implicit Bias,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,a69cc53e-3e51-4ff3-bcb8-9c2c5919ae19,,
flaws_2503_01776v5,2025,% Beyond Matryoshka: Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,88196f13-21a4-4351-bfb5-3ca907ea444c,,
flaws_2504_08201v4,2025,Neural Encoding and Decoding at Scale,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,bad7dc33-cd2a-4e88-9974-17ef187b830d,,
flaws_2506_08070v2,2025,Info-Coevolution: An Efficient Framework for Data Model Coevolution,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,c53cd477-1d55-4f1a-9808-844c4ea70a2a,,
flaws_2506_07903v2,2025,Diffuse Everything: Multimodal Diffusion Models on Arbitrary State Spaces,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,fa7f7cf1-1674-4671-90b6-32c85711e495,,
flaws_2501_16243v3,2025,Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,f911a4f4-9a09-427a-8e4b-98d292e4a14f,,
flaws_2502_02531v3,2025,"Deep Linear Network Training Dynamics from Random Initialization: Data, Width, Depth, and Hyperparameter Transfer",,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,0945f802-4d7b-40af-b4cd-707dfcdb52cd,,
flaws_2505_04775v2,2025,Prediction via Shapley Value Regression,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,559acd5e-76ec-4834-8986-72072d2fbdf6,,
flaws_2505_23681v1,2025,Understanding Mode Connectivity via Parameter Space Symmetry,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,817130ad-ca32-47ad-977a-114260b74a37,,
flaws_2506_14143v2,2025,Leveraging Predictive Equivalence in Decision Trees,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,2c2da7a8-e8e6-42f7-8513-8ac6e06f511e,,
flaws_2502_10020v5,2025,Improved Online Confidence Bounds for Multinomial Logistic Bandits,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,1cbcb93d-d0b6-4630-8c0b-05fc4261ec3b,,
flaws_2506_07806v1,2025,Identifiable Object Representations under Spatial Ambiguities,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,1e80533b-9a0b-4b73-9090-34fedad8abfb,,
flaws_2506_16884v2,2025,The Importance of Being Lazy: Scaling Limits of Continual Learning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,d4b5d1d4-ef0e-416a-be77-8b04f9865c9b,,
flaws_2506_00961v1,2025,Enhancing Parallelism in Decentralized Stochastic Convex Optimization,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,6f6749b6-7df4-4b5a-affe-7c75abdea959,,
flaws_2503_04424v2,2025,Determinant Estimation under Memory Constraints and Neural Scaling Laws,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,,,
flaws_2505_04163v1,2025,Retrieval Augmented Time Series Forecasting,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,69565df6-761d-490d-9c8d-e866958c02bb,,
flaws_2502_14831v3,2025,Improving the Diffusability of Autoencoders,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,,,
flaws_2505_07004v4,2025,GuidedQuant: Large Language Model Quantization via Exploiting End Loss Guidance,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,a9a4afdd-6999-47ec-b6d5-be43f5296576,,
flaws_2501_17974v2,2025,Think Smarter not Harder: Adaptive Reasoning with Inference Aware Optimization,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,abf4d926-0aca-4038-81cd-fd8ab60c2223,,
flaws_2503_05004v1,2025,Faster Global Minimum Cut with Predictions,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,fad756e7-543f-4249-9732-1d37de165410,,
flaws_2502_00382v1,2025,Masked Generative Nested Transformers with Decode Time Scaling,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,7993c2d6-3344-401f-bc95-e980b3f3b7dd,,
flaws_2501_15781v2,2025,Large Language Models to Diffusion Finetuning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,12349ee8-d58c-4d9f-b747-06f01ee7aab4,,
flaws_2502_06231v2,2025,Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,b0ce5afa-5c37-47e9-8f2f-0172de3ef8d8,,
flaws_2503_09492v3,2025,Learning Cascade Ranking as One Network,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,54a29107-fbc7-44de-8275-b68f4b5e781f,,
flaws_2506_06990v2,2025,Modified K-means Algorithm with Local Optimality Guarantees,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,e9168000-379d-405a-b9f0-59a9fa1b937b,,
flaws_2504_04204v2,2025,Adaptive Elicitation of Latent Information Using Natural Language,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,77cb7b4d-96fc-426e-8598-23fa15b4dbb9,,
flaws_2502_04879v3,2025,Statistical Collusion by Collectives on Learning Platforms,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,1685fd29-708a-4148-b03f-e2a6ab200086,,
flaws_2502_02514v4,2025,Privacy Attacks on Image AutoRegressive Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,,,
flaws_2502_01419v2,2025,Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,fcedc7bc-4f00-4f20-9cf6-3eb1da693adf,,
flaws_2506_13485v1,2025,Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,9ff27957-d2e1-40af-994b-2d3b1b110696,,
flaws_2502_09534v2,2025,Fast Tensor Completion via Approximate Richardson Iteration,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,72f3e039-636c-4461-8ef5-5d50cf4f63a6,,
flaws_2505_05355v2,2025,Nearly Optimal Sample Complexity for Learning with Label Proportions,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,b6182d36-2362-4fea-8f37-bd9528faa8f1,,
flaws_2501_18739v2,2025,Beyond Message Passing: Neural Graph Pattern Machine,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,b689de77-49e7-4832-9df9-099d4851fe47,,
flaws_2502_11333v1,2025,Inverse Flow and Consistency Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,1f9ada75-931a-4edb-86b5-df153bfc5907,,
flaws_2501_17077v2,2025,"Inducing, Detecting and Characterising Neural Modules: A Pipeline for Functional Interpretability in Reinforcement Learning",,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,cba23dc1-8eb6-4889-a59c-24d3f3db3d27,,
flaws_2505_05242v1,2025,Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,af31b104-2a60-4296-a637-f847d7e87030,,
flaws_2502_18147v2,2025,"Jacobian Sparse Autoencoders: Sparsify Computations, Not Just Activations",,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,0507ee4a-72a6-4479-883a-fb60afaa8a33,,
flaws_2502_15523v2,2025,Contract Design Under Approximate Best Responses,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,bb22d8fa-bb4f-4d60-b7b8-f35b8164a349,,
flaws_2506_06242v1,2025,Visual Graph Arena: Evaluating Visual Conceptualization of Vision and Multimodal Large Language Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,652600a1-7832-4c7b-8b8b-7ec315f37872,,
flaws_2502_17709v2,2025,Contrastive Visual Data Augmentation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,0f4d5fd8-e3d0-4317-b53b-6f142a986430,,
flaws_2504_04505v1,2025,A Classification View on Meta Learning Bandits,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,178e6c5d-6dd9-420e-80ba-84931c625147,,
flaws_2505_21135v1,2025,Learning Single Index Models with Diffusion Priors,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,6d7be5dd-bd4e-445e-8b56-9d4dbb720ec5,,
flaws_2502_14583v2,2025,A Theory for Conditional Generative Modeling on Multiple Data Sources,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,cbded8ec-bf62-41cb-84e8-e892ae02ec6f,,
flaws_2501_18756v2,2025,A Unified Framework for Entropy Search and Expected Improvement in Bayesian Optimization,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,7318c6c1-756a-46c4-aa69-ba2c6b44f3f6,,
flaws_2505_19247v1,2025,Improving Value Estimation Critically Enhances Vanilla Policy Gradient,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,e62d2e98-8999-49f9-9a82-ce4a141a5ba0,,
flaws_2505_01888v1,2025,Rethinking Score Distilling Sampling for 3D Edit and Generation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,fa07602a-257f-4d07-80c8-19485de74d0d,,
flaws_2505_10147v1,2025,Near Optimal Best Arm Identification for Clustered Bandits,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,de191982-d7aa-4b71-a6fe-1fa78cbe829a,,
flaws_2502_11413v1,2025,Statistical Query Hardness of Multiclass Linear Classification with Random Classification Noise,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,79a5d0bd-ded6-4302-8ba8-e435472e83bc,,
flaws_2505_01557v1,2025,Contextures: Representations from Contexts,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,53789250-4acf-4474-81fe-d31578997bb7,,
flaws_2502_01171v2,2025,Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,047a092a-07cd-4b9a-b016-5fa98ab36d11,,
flaws_2505_00887v2,2025,Rethinking Time Encoding via Learnable Transformation Functions,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,d2f2ef21-94f9-40d2-b76f-63ba37e974cc,,
flaws_2502_04248v1,2025,Adapting to Evolving Adversaries with Regularized Continual Robust Training,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,514dc145-ff1a-4c11-beb5-070281553307,,
flaws_2506_05101v1,2025,Privacy Amplification Through Synthetic Data: Insights from Linear Regression,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,3980e39d-78a9-4fbf-b4b5-c90eca14d82d,,
flaws_2505_22364v1,2025,Computing Optimal Transport Maps and Wasserstein Barycenters Using Conditional Normalizing Flows,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,a0a60cd0-421d-4a44-8116-7ad49859e026,,
flaws_2506_05584v1,2025,TabFlex: Scaling Tabular Learning to Millions with Linear Attention,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,,,
flaws_2505_00546v2,2025,Directly Forecasting Belief for Reinforcement Learning with Delays,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,9296e0c3-30a1-45c6-9e3c-0694bb0a548b,,
flaws_2506_03363v1,2025,Probabilistic Factorial Experimental Design for Combinatorial Interventions,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,45d94f26-e608-4bb8-a01d-edc409f01289,,
flaws_2502_17543v3,2025,Training a Generally Curious Agent,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,29c9078a-9ce3-47f0-81d9-f5017ebcf69c,,
flaws_2506_08257v1,2025,Highly Compressed Tokenizer Can Generate Without Training,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,63290fb5-b2da-4fdc-aecb-b54921b08a69,,
flaws_2503_18962v2,2025,Representative Ranking for Deliberation in the Public Sphere,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,b6851ad7-f23e-4ef2-834f-b2a0daa88a5d,,
flaws_2506_01901v1,2025,Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,2b9e03be-e2fa-44aa-aa25-5f018a9c8140,,
flaws_2502_06655v2,2025,Unbiased Evaluation of Large Language Models from a Causal Perspective,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,d14c8631-ca1f-4afd-b463-c95fd022bdfc,,
flaws_2501_12633v3,2025,Inverse Reinforcement Learning with Switching Rewards and History Dependency for Characterizing Animal Behaviors,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,da09b33f-90a0-4522-b9ac-b21036b7bdc5,,
flaws_2506_19755v1,2025,Cross-regularization: Adaptive Model Complexity through Validation Gradients,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,5b44fbf0-17ea-454c-9bae-efa061d317dd,,
flaws_2502_10510v2,2025,MixMin: Finding Data Mixtures via Convex Minimization,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,ecf1ff25-e846-49ff-9a7c-57893743311b,,
flaws_2502_06905v3,2025,Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,d70c5f74-7064-484e-bf31-9aa2f1144d40,,
flaws_2502_08532v2,2025,Nonlinearly Preconditioned Gradient Methods \protect under Generalized Smoothness,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,63b1a404-d6a4-4e0d-b87f-e0f470957cfb,,
flaws_2503_22738v1,2025,ShieldAgent: Shielding Agents via Verifiable Safety Policy Reasoning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,b01b47bd-1dc6-48d3-ab5e-7c097052814e,,
flaws_2507_08761v2,2025,Penalizing Infeasible Actions and Reward Scaling in Reinforcement Learning with Offline Data,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,39ff3f2a-c1a3-495a-9847-0ce3d03753db,,
flaws_2501_06158v3,2025,GenMol: A Drug Discovery Generalist with Discrete Diffusion,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,fef33366-e25b-47d8-b83b-930d569e997a,,
flaws_2503_04482v2,2025,Generalized Interpolating Discrete Diffusion,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,28b655e1-390a-451f-a1ca-5d4f8a15600a,,
flaws_2506_05774v1,2025,Evaluating Neuron Explanations: A Unified Framework with Sanity Checks,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,d00c1554-dad8-470b-aaa1-867b1baf9b70,,
flaws_2502_02582v2,2025,Open Materials Generation with Stochastic Interpolants,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,a0ab3d1f-e7bd-4f29-a5f9-2f7d2dfb17a3,,
flaws_2507_05508v1,2025,Beyond Communication Overhead: A Multilevel Monte Carlo Approach for Mitigating Compression Bias in Distributed Learning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,5c73af0e-d31c-4013-8b1f-93c2c736284c,,
flaws_2503_03025v3,2025,Hierarchical Refinement: Optimal Transport to Infinity and Beyond,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,b68276a9-6eb1-4d06-9799-99141be31736,,
flaws_2506_05968v2,2025,Gradual Transition from Bellman Optimality Operator to Bellman Operator in Online Reinforcement Learning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,849add66-9135-4860-812f-0a7096daf738,,
flaws_2506_06486v2,2025,A Certified Unlearning Approach without Access to Source Data,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,1229bad0-4e35-44f7-89e7-bbf4340ff085,,
flaws_2504_15251v1,2025,On Learning Parallel Pancakes with Mostly Uniform Weights,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,91d591d2-cfe6-42c8-8dcd-73c777b4ed76,,
flaws_2507_03146v1,2025,Set Valued Predictions For Robust Domain Generalization,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,92a76f68-da65-47ae-8115-e76624b20856,,
flaws_2505_18545v1,2025,B-score: Detecting biases in large language models using response history,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,,,
flaws_2507_07102v1,2025,Does Data Scaling Lead to Visual Compositional Generalization?,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,93a07cee-63da-4e9a-b77b-b6ef44e4455b,,
flaws_2502_10365v2,2025,AffinityFlow: Guided Flows for Antibody Affinity Maturation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,02bae3b9-36f1-4578-b2c2-7f08057b7bb7,,
flaws_2502_05172v2,2025,Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,3166a8da-2b27-4591-90a8-c3fc24f84cf1,,
flaws_2502_17427v1,2025,Stronger Neyman Regret Guarantees for Adaptive Experimental Design,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,680d1f4a-0825-4692-8e9e-80d02289bea9,,
flaws_2506_15722v1,2025,"UniMate: A Unified Model for Mechanical Metamaterial Generation, Property Prediction, and Condition Confirmation",,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,989ff7eb-bafb-4df9-a905-e5bc83c6e38a,,
flaws_2502_20012v3,2025,Learning Classifiers That Induce Markets,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,0aab9624-f061-4661-b8a8-39c14b6c303c,,
flaws_2502_00264v2,2025,Beyond the Permutation Symmetry of Transformers: The Role of Rotation for Model Fusion,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,a50f9abb-a33f-43a0-a4e5-5736956198c7,,
flaws_2504_08859v2,2025,PolyConf: Unlocking Polymer Conformation Generation through Hierarchical Generative Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,26575c1b-ae24-4ecd-971a-aaa803923147,,
flaws_2503_07639v1,2025,Mixture of Experts Made Intrinsically Interpretable,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,4aafe2b1-137c-41ec-bce5-26fa38c491e3,,
flaws_2501_16825v2,2025,Can Transformers Learn Full Bayesian Inference In Context?,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,a5c67ff1-7eb9-46b7-82c9-d85faadbf026,,
flaws_2502_04320v2,2025,ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,bacebe22-1025-4416-b433-817fb3e050f3,,
flaws_2503_06337v4,2025,Pretraining Generative Flow Networks with Inexpensive Rewards for Molecular Graph Generation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,6023152f-a353-4dbc-b64f-dccf2a7f7dc7,,
flaws_2502_07202v6,2025,Monte Carlo Tree Diffusion for System 2 Planning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,a4aa04f2-1e8b-4f94-b6bf-42beb297c4dd,,
flaws_2502_09858v1,2025,Automated Hypothesis Validation with Agentic Sequential Falsifications,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,31953986-edcc-4bfd-baaf-84d4e5a4aa5b,,
flaws_2505_02865v2,2025,Accelerating Large Language Model Reasoning via Speculative Search,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,74018d42-ef6c-4732-8109-eaf005dd77e3,,
flaws_2503_15798v2,2025,Mixture of Lookup Experts,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,de54ee31-eeb7-4325-ae77-886975d559c9,,
flaws_2502_13574v3,2025,RestoreGrad: Signal Restoration Using Conditional Denoising Diffusion Models with Jointly Learned Prior,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,2a989610-244a-4279-8191-0622b81c50cf,,
flaws_2506_19598v2,2025,Training Flexible Models of Genetic Variant Effects from Functional Annotations using Accelerated Linear Algebra,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,625c0d99-2a7a-4c5d-b51f-765c25c8a45e,,
flaws_2502_04549v3,2025,Mechanisms of Projective Composition of Diffusion Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,5971e484-c678-4694-9637-c457c38fb61e,,
flaws_2501_04694v2,2025,EpiCoder: Encompassing Diversity and Complexity in Code Generation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,3faeee95-5547-444a-a1f9-24b2c8c6f761,,
flaws_2505_12917v2,2025,Temporal Query Network for Efficient Multivariate Time Series Forecasting,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,7a7b66bb-965a-4fa0-9997-82bea23dc829,,
flaws_2501_17345v1,2025,Testing Conditional Mean Independence Using Generative Neural Networks,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,8b52a92e-609d-4aeb-b21a-11c375e9ed37,,
flaws_2505_13573v1,2025,FreeMesh: Boosting Mesh Generation with Coordinates Merging,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,17d6aa8c-e66f-4243-9824-0f864bd19416,,
flaws_2506_00205v1,2025,Unlocking the Power of Rehearsal in Continual Learning: A Theoretical Perspective,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,e8a2c51e-7f4f-4547-af95-38fea621c37f,,
flaws_2506_11039v1,2025,Angle Domain Guidance: Latent Diffusion Requires Rotation Rather Than Extrapolation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,63eb68f8-1729-4033-9cfa-ad963b26a17d,,
flaws_2502_04375v2,2025,An Analysis for Reasoning Bias of Language Models with Small Initialization,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,66ac6479-0619-4473-886f-53827e112de4,,
flaws_2506_05615v1,2025,When Maximum Entropy Misleads Policy Optimization,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,2115a839-5728-4c04-b4bd-5135d309711a,,
flaws_2502_13339v2,2025,How Expressive are Knowledge Graph Foundation Models?,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,24243367-5170-4dc8-bc76-566c2b5b98c2,,
flaws_2506_05039v1,2025,iN2V: Bringing Transductive Node Embeddings to Inductive Graphs,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,c559317e-2185-4ce0-920f-31d1b974c9ea,,
flaws_2507_17382v1,2025,Continual Generalized Category Discovery: Learning and Forgetting from a Bayesian Perspective,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,f01c4dcd-fa5f-4bb5-81ac-3452a0a18f94,,
flaws_2502_06761v2,2025,"When, Where and Why to Average Weights?",,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,75e931d8-eb79-459f-89a6-cce8f3797930,,
flaws_2506_07883v1,2025,Unifying Diffusion Models for Counterfactual Inference,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,2531395c-200c-47a0-97e4-6a9234f8cbdd,,
flaws_2505_09131v2,2025,Fair Clustering via Alignment,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,7fa2a814-4694-40b8-ad01-19ea55e71470,,
flaws_2502_07222v1,2025,A Memory Efficient Randomized Subspace Optimization Method for Training Large Language Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,a7166891-40b8-4ef0-b497-3fd56ef8fe62,,
flaws_2505_04741v1,2025,When Bad Data Leads to Good Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,7cbf7010-cb74-4d9c-b446-6749dd6eeb54,,
flaws_2502_03738v1,2025,"Scaling Laws in Patchification: An Image Is Worth 50,176 Tokens And More",,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,ab8e3968-b140-4c85-824b-16dee6c16fe8,,
flaws_2502_18679v3,2025,Discriminative Finetuning of Generative Large Language Models without Reward Models and Human Preference Data,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,3b565eff-0123-4fab-9cdb-40a50e87d1b0,,
flaws_2505_17072v2,2025,Safety Alignment Can Be Not Superficial With Explicit Safety Signals,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,756ed113-da33-41ca-b9f0-9bdcd51dbcdc,,
flaws_2504_16925v1,2025,Latent Diffusion Planning for Imitation Learning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,d825c06d-f3d7-468d-a650-56279bb0d30a,,
flaws_2506_07595v1,2025,Exploiting Curvature in Online Convex Optimization with Delayed Feedback,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,9771a4a3-67b2-454c-a4dd-724545493321,,
flaws_2505_17649v1,2025,Instruct2See: Learning to Remove Any Obstructions Across Distributions,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,d609458e-4529-4571-ad77-a179b1544359,,
flaws_2501_13925v1,2025,GeoPixel\texorpdfstring{\includegraphics[width=0.04\textwidth]{fig/logo3.png,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,1851d8bf-59ae-4bcd-944b-41b2003df8e3,,
flaws_2502_21075v2,2025,Spatial Reasoning with Denoising Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,d4098883-66e1-452f-b09d-532ae042c308,,
flaws_2503_10996v2,2025,Steering the Oracle: Exploring and Shaping Knowledge Conflicts in LLMs,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,b3d2cd2c-5af3-4664-9ae8-d143265c4721,,
flaws_2504_10694v1,2025,The Jailbreak Tax: How Useful are Your Jailbreak Outputs?,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,27287769-4fd3-4cf5-9cbd-efa137f1a77d,,
flaws_2506_00165v1,2025,Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,c39cdf21-06e2-40d0-8289-4401e3132827,,
flaws_2502_07616v2,2025,Tractable Transformers for Flexible Conditional Generation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,831fe4c5-953a-41d9-8d09-bc93d72cccce,,
flaws_2501_00701v4,2025,ResKoopNet: Learning Koopman Representations for Complex Dynamics with Spectral Residuals,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,52adc93b-f7fb-49eb-ad42-338b5c59379f,,
flaws_2501_19200v2,2025,A Variational Perspective on Generative Protein Fitness Optimization,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,356bf218-927f-4bd9-b8cf-04bc35fdfa48,,
flaws_2506_18729v2,2025,MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,c4531202-64ea-456d-b91f-5e0a5ee77f6f,,
flaws_2503_01580v1,2025,A Selective Learning Method for Temporal Graph Continual Learning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,06de15b8-48f2-4801-9042-7d92129b8b85,,
flaws_2505_20251v1,2025,Learning Extrapolative Sequence Transformations from Markov Chains,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,04584dab-182f-479c-90d7-254e46da1161,,
flaws_2502_02367v3,2025,Field Matching: an Electrostatic Paradigm to Generate and Transfer Data,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,03fbb3d6-7de8-418c-ba4a-72e591fcaa7d,,
flaws_2502_04040v2,2025,Safety Reasoning with Guidelines,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,4ee1a0d9-d15d-4453-88e7-80a454624d90,,
flaws_2502_17607v2,2025,Synthetic Text Generation for Training Large Language Models via Gradient Matching,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,0063a844-17d3-4936-8347-0d3f13934e5c,,
flaws_2503_07565v7,2025,Inductive Moment Matching,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,d65ebb12-e14f-4c7d-8666-4ee2971ea731,,
flaws_2506_00866v1,2025,Projection Pursuit Density Ratio Estimation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,4bbda17f-3a00-4e9f-b5fe-f36ec338a7f7,,
flaws_2506_08127v1,2025,Constrained Pareto Set Identification with Bandit Feedback,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,483926d2-5e92-462e-b4c7-8545bd8fbb30,,
flaws_2506_06194v1,2025,Transformative or Conservative? Conservation laws for ResNets and Transformers,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,3985a37d-a264-444e-9edb-4512a2b5ee48,,
flaws_2502_01362v2,2025,Inverse Bridge Matching Distillation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,4358bebe-4823-494a-8de3-d8650082e330,,
flaws_2503_17514v2,2025,Language Models May Verbatim Complete Text They Were Not Explicitly Trained On,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,04f37129-c050-4567-a9ad-4a0186f05e60,,
flaws_2502_13112v2,2025,Constrained Online Convex Optimization with Polyak Feasibility Steps,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,834a828a-f2a2-4b08-9f92-d24ef5a1821e,,
flaws_2506_12091v2,2025,Continuously Updating Digital Twins using Large Language Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,c40cea7b-1732-430e-b147-82a518b539b5,,
flaws_2509_07983v2,2025,Steering Protein Language Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,3fca688c-b2b8-4de1-88ff-52345cbc5ec9,,
flaws_2506_05718v2,2025,Grokking Beyond the Euclidean Norm of Model Parameters,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,5a6080ad-051c-43b3-8e47-1bafc17be7cb,,
flaws_2509_26085v1,2025,A Chaotic Dynamics Framework Inspired by Dorsal Stream for Event Signal Processing,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,f73aca1a-7eb3-469f-a380-43f5ee924209,,
flaws_2505_01874v2,2025,Towards Trustworthy Federated Learning with Untrusted Participants,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,15c78f75-6101-4934-9348-60523406d03d,,
flaws_2506_08216v1,2025,What makes an Ensemble (Un) Interpretable?,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,73bac467-63db-4f26-93c3-6efffb35fb27,,
flaws_2506_09101v1,2025,Feature Shift Localization Network,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,7b15858e-ae23-4379-b821-cabcefa1b80b,,
flaws_2506_08436v1,2025,Olica: Efficient Structured Pruning of Large Language Models without Retraining,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,a26a802b-c31f-46c1-bd24-eb24ecdf1c5f,,
flaws_2502_03275v2,2025,Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,5164bfbd-a124-4af2-8b44-c955b7d591b4,,
flaws_2502_02013v2,2025,Layer by Layer: Uncovering Hidden Representations in Language Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,bf09eb4a-f0dc-488b-a25a-acb860db1d98,,
flaws_2502_06485v3,2025,WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,d492840e-1e64-4190-be81-de28a7e10267,,
flaws_2502_05908v3,2025,Inverse Problem Sampling in Latent Space Using Sequential Monte Carlo,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,1dff5a09-a5d7-4235-a7d9-6be2bd6602b7,,
flaws_2502_02079v1,2025,Online Clustering of Dueling Bandits,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,d6ff6f0f-bdcc-4289-832f-31e28277942b,,
flaws_2502_16008v1,2025,Exact Recovery of Sparse Binary Vectors from Generalized Linear Measurements,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,7f35168d-c653-4606-9262-3890a93e5c37,,
flaws_2506_05940v4,2025,Exponential Family Variational Flow Matching for Tabular Data Generation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,fb086ff8-0abe-42f5-9b00-c36ff351fb7a,,
flaws_2510_01855v1,2025,Explicit Discovery of Nonlinear Symmetries from Dynamic Data,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,e4d631ea-74f0-46fc-a55e-b21f1847ae72,,
flaws_2502_02853v5,2025,Rethinking Latent Redundancy in Behavior Cloning: An Information Bottleneck Approach for Robot Manipulation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,7a7df1b6-c21c-4129-a595-fccf45beb0a0,,
flaws_2506_21362v1,2025,Counterfactual Voting Adjustment for Quality Assessment and Fairer Voting in Online Platforms with Helpfulness Evaluation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,6c962418-5aaa-4daa-9eec-a52ea142adab,,
flaws_2504_14730v2,2025,Optimizing Noise Distributions for Differential Privacy,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,4b6c0794-9677-4b99-9e0c-ac7120e2a020,,
flaws_2502_00557v1,2025,Sampling Binary Data by Denoising through Score Functions,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,14206153-ad93-4e82-b4f5-87373463f8b7,,
flaws_2505_01726v2,2025,Probabilistic Interactive 3D Segmentation with Hierarchical Neural Processes,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,8a18337b-a178-4306-aa3c-2e8b6834f851,,
flaws_2502_18462v2,2025,Scalable Equilibrium Sampling with Sequential Boltzmann Generators,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,16208d77-59ec-40ec-b947-185ef79f8776,,
flaws_2506_15397v1,2025,Learn to Vaccinate: Combining Structure Learning and Effective Vaccination for Epidemic and Outbreak Control,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,1f9da132-244b-4f75-86a9-01dee81a1315,,
flaws_2502_06854v2,2025,Can Large Language Models Understand Intermediate Representations in Compilers?,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,1becc1e0-1390-4cd5-86cf-93f34f14fc5b,,
flaws_2501_09976v2,2025,Dendritic Localized Learning: Toward Biologically Plausible Algorithm,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,cc8d86db-c730-43d8-9e8c-271350b757c0,,
flaws_2501_16815v3,2025,Best Subset Selection: Optimal Pursuit for Feature Selection and Elimination,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,5780c528-f3b5-4152-ab11-2d0168791783,,
flaws_2501_18373v2,2025,Function Encoders: A Principled Approach to Transfer Learning in Hilbert Spaces,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,aefc9c31-9536-4e40-887d-200db4d254f1,,
flaws_2502_17578v1,2025,How Do Large Language Monkeys Get Their Power (Laws)?,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,092fd498-4e6d-4766-bd78-b86dec86b2c7,,
flaws_2502_08512v3,2025,Measuring Diversity in Synthetic Datasets,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,5118d018-b1e8-475d-a1e1-3d8df3a8f6e1,,
flaws_2502_16025v2,2025,"FeatSharp: Your Vision Model Features, Sharper",,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,4d84e3c6-75bc-423a-9aab-d61ef42bb1de,,
flaws_2502_02671v1,2025,On Teacher Hacking in Language Model Distillation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,380c337a-6247-43d7-a043-1eff049a3f1b,,
flaws_2505_06744v1,2025,LineFlow: A Framework to Learn Active Control of Production Lines,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,d236501b-c953-499d-84eb-fbf682d36f45,,
flaws_2502_07004v2,2025,Demystifying Singular Defects in Large Language Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,cc03c7bf-2690-41f9-92ba-d77c91f7b051,,
flaws_2508_08252v1,2025,ReferSplat: Referring Segmentation in 3D Gaussian Splatting,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,39b43028-28af-4e94-ad53-57ed76708450,,
flaws_2505_02406v2,2025,Token Coordinated Prompt Attention is Needed for Visual Prompting,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,82989a02-71a3-49ab-9a20-0c88c59cd488,,
flaws_2501_04179v2,2025,Generation from Noisy Examples,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,2145f015-3323-4831-9052-d5bbb14976fb,,
flaws_2503_03576v1,2025,Optimal Decision Tree Pruning Revisited: Algorithms and Complexity,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,226597bb-b9c8-497e-94d5-3112ab4b7bf5,,
flaws_2505_17765v1,2025,Joker: Joint Optimization Framework for Lightweight Kernel Machines,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,,,
flaws_2504_03461v2,2025,Conditioning Diffusions Using Malliavin Calculus,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,fcc32810-57e6-4486-91fa-b0a79f4dbb63,,
flaws_2508_14338v1,2025,On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,a4c8bd69-1697-4223-a2fb-bb35d45feead,,
flaws_2502_04673v1,2025,Optimistic Algorithms for Adaptive Estimation of the Average Treatment Effect,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,4fe23e9c-792a-4649-9aec-4ee3cb468e06,,
flaws_2502_09443v2,2025,Relational Conformal Prediction for Correlated Time Series,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,156ad6ce-037f-4260-bd50-08280b844333,,
flaws_2502_03023v2,2025,Parametric Scaling Law of Tuning Bias in Conformal Prediction,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,4fe4bf20-bde9-45f3-b9db-633ee32653c1,,
flaws_2505_21877v1,2025,Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,707af838-01d7-40f1-a304-a4b710f3697e,,
flaws_2506_11550v2,2025,Improving Multimodal Learning Balance and Sufficiency through Data Remixing,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,5ab39e07-9579-4bf8-8927-6331da129b3d,,
flaws_2502_02331v1,2025,On the Impact of Performative Risk Minimization for Binary Random Variables,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,a8f16628-efeb-49df-b9ac-a4678f0de6fe,,
flaws_2505_19438v1,2025,Sampling from Binary Quadratic Distributions via Stochastic Localization,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,7695d11e-17b1-403f-8f1d-cccbc90ff627,,
flaws_2504_11713v3,2025,Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,93a39e4e-79a3-438d-9513-283bc5596f28,,
flaws_2502_06786v3,2025,Matryoshka Quantization,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,43ba7f19-8176-4148-854b-71b064242e99,,
flaws_2502_00846v3,2025,Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,492f5c88-00be-488d-9bb2-43d17b107007,,
flaws_2502_10158v3,2025,Combinatorial Reinforcement Learning with Preference Feedback,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,c5a27f54-1514-44ba-b795-7b6f04bc4052,,
flaws_2502_01236v1,2025,Eliciting Language Model Behaviors with Investigator Agents,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,3c89a943-ebd6-450a-ba14-e79255de4204,,
flaws_2503_15704v5,2025,Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,c7384c49-34e5-4bd2-b31b-312df94cdc82,,
flaws_2505_19996v1,2025,Learning Optimal Multimodal Information Bottleneck Representations,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,70f4ff97-b303-41a1-92d7-a620557d4313,,
flaws_2502_00954v3,2025,Hypo3D: Exploring Hypothetical Reasoning in 3D,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,a373f1ca-d2ba-49c0-a5a7-cb92bcb533d4,,
flaws_2502_13757v3,2025,Identifying Metric Structures of Deep Latent Variable Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,7c9ba033-8890-431d-adef-76f8e5411315,,
flaws_2502_13129v1,2025,Is Noise Conditioning Necessary for Denoising Generative Models?,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,28fc42ee-6b24-4ea7-82f5-0328feb94ccd,,
flaws_2503_19595v2,2025,Optimizing Language Models for Inference Time Objectives using Reinforcement Learning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,320ce721-d187-4cec-8eb8-b13c4182ca1c,,
flaws_2505_16321v1,2025,Efficient Motion Prompt Learning for Robust Visual Tracking,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,0f86c292-deae-423b-87dc-17ef20c075e0,,
flaws_2505_20465v1,2025,Learning with Expected Signatures: Theory and Applications,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,acd7abd6-f227-4369-b2d5-436fd31c30a4,,
flaws_2503_19136v2,2025,Stochastic Poisson Surface Reconstruction with One Solve\texorpdfstring{,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,ca7aa978-15fa-4add-8c0a-174ee117e1ae,,
flaws_2507_11161v2,2025,How does Labeling Error Impact Contrastive Learning? A Perspective from Data Dimensionality Reduction,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,3fe89afe-be18-42bd-8a31-de50362e5fe5,,
flaws_2505_22632v1,2025,Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,e237a3fa-c06a-4058-980a-12b535466257,,
flaws_2507_04373v1,2025,Hierarchical Reinforcement Learning with Targeted Causal Interventions,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,0a1bf28c-2fc0-49d2-995e-0a7bca768c26,,
flaws_2506_11449v1,2025,Dynamic Sparse Training of Diagonally Sparse Networks,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,95ff7fe2-d810-4f9f-994d-baecde78bc0e,,
flaws_2503_11713v2,2025,"Revisiting the Predictability of Performative, Social Events",,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,1e7befdd-aa12-4118-b6c0-d3a3d0463f98,,
flaws_2501_18006v1,2025,Topological Signatures of Adversaries in Multimodal Alignments,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,7c0bb0bb-d0fb-49bc-983c-3a2ecfba3a8b,,
flaws_2506_14767v1,2025,A Variational Framework for Improving Naturalness in Generative Spoken Language Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,c0211811-3cef-422e-8bf5-ec234c542cda,,
flaws_2505_12353v1,2025,Importance Sampling for Nonlinear Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,adec3352-0925-4884-8a92-11db521b63f0,,
flaws_2505_07715v1,2025,Hybrid Spiking Vision Transformer for Object Detection with Event Cameras (ICML 2025),,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,26c3c0d3-6f22-40aa-b396-b3d5b6d455e9,,
flaws_2501_15420v2,2025,Visual Generation Without Guidance,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,378c950b-d3a0-4c58-97cd-341abcfbe64e,,
flaws_2502_13581v3,2025,ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,aa89c635-da20-4072-90d7-6373b9d9bd22,,
flaws_2504_15812v1,2025,Fusing Reward and Dueling Feedback in Stochastic Bandits,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,f35ac784-1102-4cb2-b3b4-21ea5c275498,,
flaws_2505_07812v1,2025,Continuous Visual Autoregressive Generation via Score Maximization,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,db0dce43-3804-4d1f-a356-e9e16963719e,,
flaws_2503_09817v1,2025,Temporal Difference Flows,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,f645e8e9-6ca7-4802-b7f8-e216f2fcd035,,
flaws_2502_04382v3,2025,Sparse Autoencoders for Hypothesis Generation,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,,,
flaws_2503_00339v2,2025,Falcon: Fast Visuomotor Policies via Partial Denoising,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,bdcac886-2933-4543-9386-d9b31ab670ba,,
flaws_2506_06599v1,2025,Direct Prediction Set Minimization via Bilevel Conformal Classifier Training,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,3103df8f-01b4-4724-8225-cceaaf2fd20a,,
flaws_2505_15025v1,2025,Inverse Optimization via Learning Feasible Regions,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,47218d55-d64d-4f98-88d9-8bb1b1c51934,,
flaws_2505_20130v3,2025,Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,4414f472-155e-428b-98b7-79b6a31593f5,,
flaws_2507_18807v1,2025,Fishers for Free? Approximating the Fisher Information Matrix by Recycling the Squared Gradient Accumulator,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,8410f635-5068-4cdb-b44e-a95d9b676112,,
flaws_2506_05814v1,2025,Positional Encoding meets Persistent Homology on Graphs,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,2c4147b1-5253-4434-ab2d-9889eadded73,,
flaws_2505_19820v1,2025,InfoCons: Identifying Interpretable Critical Concepts in Point Clouds via Information Theory,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,15fce5ce-e1cb-4073-844f-3c89afc10a3f,,
flaws_2506_23596v1,2025,When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,bbf4639f-72f9-4b1a-a1ff-063e629d0f27,,
flaws_2502_12147v2,2025,Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,11e84c4e-f5b2-4c58-9738-138bb9561a70,,
flaws_2502_02861v3,2025,Algorithms with Calibrated Machine Learning Predictions,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,8cdfa765-e8b0-498d-a838-9d3d84ca3ef7,,
flaws_2507_09897v2,2025,Algorithm Development in Neural Networks: Insights from the Streaming Parity Task,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,0dbfb6cf-dc7f-4738-9bdf-add86bf89e17,,
flaws_2502_01876v2,2025,Reinforcement Learning with Segment Feedback,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,0343a501-ef87-4a90-849d-232e140b0a5c,,
flaws_2505_16734v1,2025,Maximum Total Correlation Reinforcement Learning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,8b9e3df1-1116-4fd9-b42e-422742bc7818,,
flaws_2505_23833v1,2025,Benchmarking Abstract and Reasoning Abilities Through A Theoretical Perspective,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,862dacda-088a-4527-a4e9-fbb6530c5783,,
flaws_2505_07271v1,2025,On the Robustness of Reward Models for Language Model Alignment,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,5f446eb9-f86f-40ca-b5ad-7cf2525d4984,,
flaws_2504_12883v2,2025,"Mirror, Mirror of the Flow: How Does Regularization Shape Implicit Bias?",,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,e0e4e34a-b006-4422-bde5-90e2f676b18a,,
flaws_2502_09650v2,2025,Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,b767ae81-d7ce-44b3-b85c-9273faae0f3e,,
flaws_2506_12383v1,2025,Scaling Probabilistic Circuits via Monarch Matrices,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,1393506b-44ef-4da8-9161-52f87a1b6038,,
flaws_2501_18797v2,2025,Compositional Generalization via Forced Rendering of Disentangled Latents,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,c9e9b5e9-fdc2-4882-9614-bf2792c1b6bc,,
flaws_2502_11672v2,2025,Exact Upper and Lower Bounds for the Output Distribution of Neural Networks with Random Inputs,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,c5a39e56-4758-48c0-a748-55786943c099,,
flaws_2506_17204v1,2025,Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,774b2b60-5b24-4aa5-9254-7901e6d419e7,,
flaws_2502_16282v2,2025,Understanding the Emergence of Multimodal Representation Alignment,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,e4110ac5-c3eb-42c7-a57c-abd48b0d0b8a,,
flaws_2503_10135v2,2025,Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,25785c74-b077-4468-bd7e-6e598e396c8b,,
flaws_2502_15588v1,2025,Improving the Scaling Laws of Synthetic Data with Deliberate Practice,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,b36d35cd-bda2-45ed-b0df-8ed09e3a93c3,,
flaws_2505_07558v2,2025,Direct Density Ratio Optimization: A Statistically Consistent Approach to Aligning Large Language Models,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,f26a5c89-6e9f-45d5-853d-c8fe4707c7c0,,
flaws_2501_11651v2,2025,T1: Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling,,,reject,Unknown,,0.0,,0.0,,0.0,,0.0,,0.0,[],0,fa8be410-d36f-4141-ab63-49a4a223ae88,,