| source,paper_id,year,frontend_paper_id,title,venue,decision,primary_area,avg_score,avg_presentation,avg_soundness,avg_contribution,avg_confidence,citations_serper,normalized_citations |
| molbook_leaderboad_iclr2025,iclr_LQdaXixB0g,2025,2b25b44f-55cf-49e7-b2c2-6308ee7c82a1,pSAE-chiatry: Utilizing Sparse Autoencoders to Uncover Mental-Health-Related Features in Language Models,Rejected,reject,applications to neuroscience & cognitive science,2.5,2.0,1.25,1.5,4.0,1,0.0526 |
| molbook_leaderboad_iclr2025,iclr_t8ctvylFn7,2025,1df48f20-128e-47df-8180-403898f0c583,Linearly Controlled Language Generation with Performative Guarantees,Rejected,reject,"alignment, fairness, safety, privacy, and societal considerations",5.0,3.0,3.0,2.5,3.5,16,0.8421 |
| molbook_leaderboad_iclr2025,iclr_JHoC430Nxi,2025,b62b8218-477e-4ffc-9c62-fff04ff2ad17,CTNet: A CNN-Transformer Hybrid Network for 6D Object Pose Estimation,Rejected,reject,"applications to computer vision, audio, language, and other modalities",2.5,2.0,1.75,1.75,4.25,0,0.0 |
| molbook_leaderboad_iclr2025,iclr_lOTfiKt4Gc,2025,ad77eb1e-3a17-4243-acbb-d7b54c78051f,GUARD: Guideline Upholding Test through Adaptive Role-play and Jailbreak Diagnostics for LLMs,Rejected,reject,"alignment, fairness, safety, privacy, and societal considerations",5.0,2.25,2.25,2.75,3.5,1,0.0526 |
| molbook_leaderboad_iclr2025,iclr_qTWDpbF47t,2025,e3df424f-70ad-4367-94e6-cfcd86ed9122,Compositional Video Generation as Flow Equalization,Rejected,reject,generative models,6.75,2.25,3.0,2.5,4.0,13,0.6842 |
| iclr_2025_oral_200,iclr_Cjz9Xhm7sI,2025,bd905a52-5873-4935-aeae-c81aaaa19f04,High-Dynamic Radar Sequence Prediction for Weather Nowcasting Using Spatiotemporal Coherent Gaussian Representation,ICLR 2025 Oral,Accept (Oral),"applications to computer vision, audio, language, and other modalities",8.0,3.0,3.0,2.67,3.33,10,0.5357 |
| iclr_2025_oral_200,iclr_KSLkFYHlYg,2025,49e7c3d3-ca20-433b-b5c5-98f8bd64f263,"ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design",ICLR 2025 Oral,Accept (Oral),"applications to physical sciences (physics, chemistry, biology, etc.)",8.0,3.67,3.0,3.67,4.67,14,0.7568 |
| iclr_2025_oral_200,iclr_KIgaAqEFHW,2025,0de7202a-88bf-45d9-8f07-fa154074fb18,miniCTX: Neural Theorem Proving with (Long-)Contexts,ICLR 2025 Oral,Accept (Oral),datasets and benchmarks,8.0,3.33,3.33,3.67,3.67,18,0.8838 |
| iclr_2025_oral_200,iclr_vo9t20wsmd,2025,2c1f60ae-d5ab-4fb9-ac66-c38926576384,Faster Cascades via Speculative Decoding,ICLR 2025 Oral,Accept (Oral),generative models,5.67,3.33,2.67,2.33,3.0,30,1.2839 |
| iclr_2025_oral_200,iclr_NxyfSW6mLK,2025,8cebc6ca-5407-4d19-99f9-b55ba8473df2,REGENT: A Retrieval-Augmented Generalist Agent That Can Act In-Context in New Environments,ICLR 2025 Oral,Accept (Oral),reinforcement learning,6.75,2.5,2.75,2.75,3.75,17,0.8995 |
| hidden_leaderboard_iclr2026,iclr_0wSlFpMsGb,2026,a17016b1-a8aa-42b7-9de7-a18a447297d2,Common Corpus: The Largest Collection of Ethical Data for LLM Pre-Training,ICLR2026,accept (oral),datasets and benchmarks,7.0,2.75,3.0,3.25,4.0,14,2.333333 |
| hidden_leaderboard_iclr2026,iclr_iPAy5VpGQa,2026,14aeeb93-1343-4e59-87de-0670cc5a8618,HiMAE: Hierarchical Masked Autoencoders Discover Resolution-Specific Structure in Wearable Time Series,ICLR2026,accept (poster),"applications to physical sciences (physics, chemistry, biology, etc.)",4.0,2.0,2.6667,3.0,4.0,19,3.166667 |
| hidden_leaderboard_iclr2026,iclr_0FhrtdKLtD,2026,b3c0352f-d176-4a7e-b71d-8720badaa540,Spatial Mental Modeling from Limited Views,ICLR2026,accept (poster),"foundation or frontier models, including LLMs",5.0,2.5,2.5,2.75,3.5,69,9.857143 |
| hidden_leaderboard_iclr2026,iclr_Mf5AleTUVK,2026,eb305acf-d8aa-43b3-988e-24777b4e81e1,In-the-Flow Agentic System Optimization for Effective Planning and Tool Use,ICLR2026,accept (oral),"applications to computer vision, audio, language, and other modalities",7.333333,2.67,3.0,3.0,2.6667,30,5.0 |
| hidden_leaderboard_iclr2026,iclr_vI56m4Iu4e,2026,54e3fdab-046e-40e7-9213-bfbba65f2340,MemGen: Weaving Generative Latent Memory for Self-Evolving Agents,ICLR2026,accept (poster),"foundation or frontier models, including LLMs",7.0,2.75,3.25,3.25,4.25,31,4.428571 |
| hidden_leaderboard_iclr2026,iclr_3c6bnSJwDg,2026,4c75d4c8-aade-47a7-8b1c-7648f699425a,DexMachina: Functional Retargeting for Bimanual Dexterous Manipulation,ICLR2026,reject,"applications to robotics, autonomy, planning",5.5,3.25,2.5,2.5,3.75,21,3.5 |
| hidden_leaderboard_iclr2026,iclr_osnAy1yTHu,2026,4db63ed5-d0be-4405-a4fe-d80b134ed39d,OneReward: Unified Mask-Guided Image Generation via Multi-Task Human Preference Learning,ICLR2026,reject,generative models,4.5,2.75,2.25,3.0,3.5,12,1.714286 |
| hidden_leaderboard_iclr2026,iclr_1sNhPjqGub,2026,cc5f842d-1002-451c-8d60-506b8ffc311f,Training-free Guidance in Text-to-Video Generation via Multimodal Planning and Structured Noise Initialization,ICLR2026,reject,"applications to computer vision, audio, language, and other modalities",4.5,3.0,2.5,2.25,4.0,11,1.833333 |
| hidden_leaderboard_iclr2026,iclr_CFF6zXErgS,2026,28e42b62-34bb-4923-af10-7148b44b7e63,GTPO AND GRPO-S: TOKEN AND SEQUENCE-LEVEL REWARD SHAPING WITH POLICY ENTROPY,ICLR2026,reject,reinforcement learning,2.5,2.25,2.25,2.0,3.75,27,3.857143 |
| hidden_leaderboard_iclr2026,iclr_SnEywLKodN,2026,30dcd161-e9f1-40ea-ae9b-1694ea337dc7,VeriGuard: Enhancing LLM Agent Safety via Verified Code Generation,ICLR2026,reject,"alignment, fairness, safety, privacy, and societal considerations",3.5,1.75,2.25,2.5,3.25,11,1.833333 |
| flaws_iclr2026_leaderboard,flaws_z2zFk9jYpw,2026,434fda84-5b86-4efd-a807-d6af3a1367b9,Erase or Hide? Suppressing Spurious Unlearning Neurons for Robust Unlearning,ICLR2026,accept (poster),"alignment, fairness, safety, privacy, and societal considerations",0.0,0.0,0.0,0.0,0.0,0,0.0 |
| flaws_iclr2026_leaderboard,flaws_CEJl0gN2gj,2026,6185ab2c-209c-4d7e-ba6d-9fd807f8aacf,"Robustness in Text-Attributed Graph Learning: Insights, Trade-offs, and New Defenses",ICLR2026,accept (poster),learning on graphs and other geometries & topologies,0.0,0.0,0.0,0.0,0.0,0,0.0 |
| flaws_iclr2026_leaderboard,flaws_RebPBMrMmk,2026,ad6a35ae-a936-4cac-ad78-fb887c60848b,"RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo",ICLR2026,accept (poster),datasets and benchmarks,0.0,0.0,0.0,0.0,0.0,0,0.0 |
| flaws_iclr2026_leaderboard,flaws_cEXEmyW77N,2026,9e4c8fd4-8e52-4b26-b466-ed017bfa20a9,"Structurally Human, Semantically Biased: Detecting LLM-Generated References with Embeddings and GNNs",ICLR2026,accept (poster),"foundation or frontier models, including LLMs",0.0,0.0,0.0,0.0,0.0,0,0.0 |
| flaws_iclr2026_leaderboard,flaws_NCN8oUsiNf,2026,3e196547-12c0-406b-8f61-cca73c183cdb,Attention as a Compass: Efficient Exploration for Process-Supervised RL in Reasoning Models,ICLR2026,accept (poster),reinforcement learning,0.0,0.0,0.0,0.0,0.0,0,0.0 |
| flaws_iclr2026_leaderboard,flaws_JlwYkFm91F,2026,95e68002-1c07-4626-947a-84f792b50198,Denoising Neural Reranker for Recommender Systems,ICLR2026,accept (poster),"other topics in machine learning (i.e., none of the above)",0.0,0.0,0.0,0.0,0.0,0,0.0 |
| flaws_iclr2026_leaderboard,flaws_kpLRYtPGt3,2026,0828e010-5e94-4522-8cd6-ad0f7a2541ee,Neon: Negative Extrapolation From Self-Training Improves Image Generation,ICLR2026,accept (oral),generative models,0.0,0.0,0.0,0.0,0.0,0,0.0 |
| flaws_iclr2026_leaderboard,flaws_E7ZZRnBQU7,2026,49665cc8-0ffc-422f-a3b1-d10a4ab03f04,Sharing State Between Prompts and Programs,ICLR2026,accept (poster),"neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)",0.0,0.0,0.0,0.0,0.0,0,0.0 |
| flaws_iclr2026_leaderboard,flaws_aYDpQUcJTn,2026,0d01a044-8645-46c4-bb23-4579b73511ec,Single Index Bandits: Generalized Linear Contextual Bandits with Unknown Reward Functions,ICLR2026,accept (poster),learning theory,0.0,0.0,0.0,0.0,0.0,0,0.0 |
| flaws_iclr2026_leaderboard,flaws_ka82fvJ5f1,2026,92fd5c0c-dbf7-4bbc-bf7f-40eefce37109,Universal Model Routing for Efficient LLM Inference,ICLR2026,accept (poster),"other topics in machine learning (i.e., none of the above)",0.0,0.0,0.0,0.0,0.0,0,0.0 |
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