Sync v0.9.0: 601 resources and Loopie research

Adds Loop the Loopies and 21 related works, refreshes publication/source metadata, updates the 50-field Parquet dataset, and aligns the site and social assets with v0.9.0.

CITATION.bib

@@ -3,5 +3,5 @@
   title        = {Awesome Loop Engineering},
   year         = {2026},
   howpublished = {\url{https://github.com/ChaoYue0307/awesome-loop-engineering}},
-  note         = {Field guide and implementation kit for Loop Engineering, version 0.8.0}
+  note         = {Field guide and implementation kit for Loop Engineering, version 0.9.0}
 }

CITATION.cff

@@ -2,8 +2,8 @@ cff-version: 1.2.0
 message: "If this repository is useful in your work, please cite it as below."
 title: "Awesome Loop Engineering"
 type: software
-version: 0.8.0
-date-released: 2026-07-18
+version: 0.9.0
+date-released: 2026-07-20
 authors:
   - family-names: "He"
     given-names: "Chaoyue"

README.de.md

@@ -1,6 +1,6 @@
 # Awesome Loop Engineering
 
-<!-- last-synced: 2026-07-18 -->
+<!-- last-synced: 2026-07-20 -->
 
 <p align="center">
   <img src="assets/awesome-loop-engineering-cover.png" alt="Awesome Loop Engineering cover" width="100%">
@@ -28,7 +28,7 @@ Ein Loop entdeckt Arbeit, übergibt sie an einen oder mehrere Agenten, prüft da
 
 Loop Engineering umfasst wiederkehrende KI-Agenten- und Coding-Agent-Systeme mit expliziten Auslösern, Verifikation und dauerhaftem Zustand. Software-Event-Loops, Regelungstechnik, Growth Loops, generische Automatisierung und nicht-KI-bezogene Feedback-Loops gehören nicht dazu.
 
-Starte mit 579 Ressourcen, 20 operativen Loop-Patterns, 20 schemavalidierten Loop-Contracts und 8 Runtime-Startern (3 ausführbare Programme und 5 anpassbare Vorlagen), ergänzt durch eine Community-Galerie und 8 Sprachen.
+Starte mit 601 Ressourcen, 20 operativen Loop-Patterns, 20 schemavalidierten Loop-Contracts und 8 Runtime-Startern (3 ausführbare Programme und 5 anpassbare Vorlagen), ergänzt durch eine Community-Galerie und 8 Sprachen.
 
 ## Mentales Modell
 

README.es.md

@@ -1,6 +1,6 @@
 # Awesome Loop Engineering
 
-<!-- last-synced: 2026-07-18 -->
+<!-- last-synced: 2026-07-20 -->
 
 <p align="center">
   <img src="assets/awesome-loop-engineering-cover.png" alt="Awesome Loop Engineering cover" width="100%">
@@ -28,7 +28,7 @@ Un loop descubre trabajo, lo delega a uno o más agentes, verifica el resultado,
 
 Loop Engineering abarca sistemas recurrentes de agentes de IA y coding agents con disparadores explícitos, verificación y estado duradero. No incluye event loops de software, teoría de control, growth loops, automatización genérica ni feedback loops no relacionados con IA.
 
-Empieza con 579 recursos, 20 patrones operativos, 20 loop contracts validados por esquema y 8 puntos de partida de runtime (3 ejecutables y 5 plantillas listas para adaptar), además de una galería comunitaria y 8 idiomas.
+Empieza con 601 recursos, 20 patrones operativos, 20 loop contracts validados por esquema y 8 puntos de partida de runtime (3 ejecutables y 5 plantillas listas para adaptar), además de una galería comunitaria y 8 idiomas.
 
 ## Modelo Mental
 

README.fr.md

@@ -1,6 +1,6 @@
 # Awesome Loop Engineering
 
-<!-- last-synced: 2026-07-18 -->
+<!-- last-synced: 2026-07-20 -->
 
 <p align="center">
   <img src="assets/awesome-loop-engineering-cover.png" alt="Awesome Loop Engineering cover" width="100%">
@@ -28,7 +28,7 @@ Un loop découvre du travail, le délègue à un ou plusieurs agents, vérifie l
 
 Loop Engineering couvre les systèmes récurrents d'agents IA et de coding agents avec des déclencheurs explicites, une vérification et un état durable. Il n'inclut ni les event loops logiciels, ni la théorie du contrôle, ni les growth loops, ni l'automatisation générique, ni les feedback loops sans rapport avec l'IA.
 
-Commencez avec 579 ressources, 20 patterns opérationnels, 20 loop contracts validés par schéma et 8 points de départ d'exécution (3 exécutables et 5 modèles prêts à adapter), ainsi qu'une galerie communautaire et 8 langues.
+Commencez avec 601 ressources, 20 patterns opérationnels, 20 loop contracts validés par schéma et 8 points de départ d'exécution (3 exécutables et 5 modèles prêts à adapter), ainsi qu'une galerie communautaire et 8 langues.
 
 ## Modèle Mental
 

README.ja.md

@@ -1,6 +1,6 @@
 # Awesome Loop Engineering
 
-<!-- last-synced: 2026-07-18 -->
+<!-- last-synced: 2026-07-20 -->
 
 <p align="center">
   <img src="assets/awesome-loop-engineering-cover.png" alt="Awesome Loop Engineering cover" width="100%">
@@ -28,7 +28,7 @@ Loop は作業を発見し、1 つ以上の agents に委任し、結果を確
 
 Loop Engineering は、明示的なトリガー、検証、永続状態を備えた反復型 AI agent / coding agent システムを対象とします。software event loop、制御理論、growth loop、一般的な workflow automation、非 AI の feedback loop は含みません。
 
-579 件の監査済みリソース、20 個の運用パターン、20 個の schema 検証済み loop contract、8 個の runtime starter（3 個の実行ファイルと 5 個の適応可能なテンプレート）から始められます。コミュニティ gallery と 8 言語の導入も利用できます。
+601 件の監査済みリソース、20 個の運用パターン、20 個の schema 検証済み loop contract、8 個の runtime starter（3 個の実行ファイルと 5 個の適応可能なテンプレート）から始められます。コミュニティ gallery と 8 言語の導入も利用できます。
 
 ## メンタルモデル
 

README.ko.md

@@ -1,6 +1,6 @@
 # Awesome Loop Engineering
 
-<!-- last-synced: 2026-07-18 -->
+<!-- last-synced: 2026-07-20 -->
 
 <p align="center">
   <img src="assets/awesome-loop-engineering-cover.png" alt="Awesome Loop Engineering cover" width="100%">
@@ -28,7 +28,7 @@ Loop 는 작업을 발견하고, 하나 이상의 agents 에게 위임하고,
 
 Loop Engineering 은 명시적 트리거, 검증, 지속 상태를 갖춘 반복 AI agent / coding agent 시스템을 다룹니다. software event loop, control theory, growth loop, 일반적인 workflow automation, 비 AI feedback loop 는 포함하지 않습니다.
 
-579개의 감사된 리소스, 20개의 운영 loop 패턴, 20개의 스키마 검증 loop contract, 8개의 runtime starter(실행 파일 3개와 적용 가능한 템플릿 5개)로 바로 시작할 수 있습니다. 커뮤니티 gallery 와 8개 언어 안내도 제공합니다.
+601개의 감사된 리소스, 20개의 운영 loop 패턴, 20개의 스키마 검증 loop contract, 8개의 runtime starter(실행 파일 3개와 적용 가능한 템플릿 5개)로 바로 시작할 수 있습니다. 커뮤니티 gallery 와 8개 언어 안내도 제공합니다.
 
 ## 멘탈 모델
 

README.md

@@ -38,7 +38,7 @@ configs:
 <h1 align="center">Awesome Loop Engineering Dataset</h1>
 
 <p align="center">
-  A structured dataset of 579 papers, official docs, tools, benchmarks, patterns, critiques, and implementation guides for recurring AI-agent systems.
+  A structured dataset of 601 papers, official docs, tools, benchmarks, patterns, critiques, and implementation guides for recurring AI-agent systems.
 </p>
 
 <p align="center">
@@ -52,16 +52,16 @@ configs:
 
 Each row connects an original source to its contribution, novelty, impact, publication details, lifecycle stages, audience, evidence type, link status, and repository context when applicable.
 
-Current release: **v0.8.0**
+Current release: **v0.9.0**
 
 | Surface | Count |
 | --- | ---: |
-| Resources | 579 |
+| Resources | 601 |
 | Operational patterns | 20 |
 | Adaptable loop contracts | 20 |
 | Runtime starters | 8 |
 
-At the **2026-07-18** source check, **528 public links opened successfully**, **3 required access**, **48 pointed to repository files**, and **0 were broken or unreachable**.
+At the **2026-07-20** source check, **547 public links opened successfully**, **6 required access**, **48 pointed to repository files**, and **0 were broken or unreachable**.
 
 ## What A Loop Contract Is
 
@@ -189,6 +189,6 @@ GitHub Releases define versioned snapshots; cite a release or commit for reprodu
   title        = {Awesome Loop Engineering},
   year         = {2026},
   howpublished = {\url{https://github.com/ChaoYue0307/awesome-loop-engineering}},
-  note         = {Version 0.8.0}
+  note         = {Version 0.9.0}
 }
 ```

README.pt-BR.md

@@ -1,6 +1,6 @@
 # Awesome Loop Engineering
 
-<!-- last-synced: 2026-07-18 -->
+<!-- last-synced: 2026-07-20 -->
 
 <p align="center">
   <img src="assets/awesome-loop-engineering-cover.png" alt="Awesome Loop Engineering cover" width="100%">
@@ -28,7 +28,7 @@ Um loop descobre trabalho, delega para um ou mais agentes, verifica o resultado,
 
 Loop Engineering abrange sistemas recorrentes de AI agents e coding agents com gatilhos explícitos, verificação e estado durável. Não inclui event loops de software, teoria de controle, growth loops, automação genérica nem feedback loops sem relação com IA.
 
-Comece com 579 recursos, 20 patterns operacionais, 20 loop contracts validados por schema e 8 starters de runtime (3 executáveis e 5 templates prontos para adaptação), além de uma galeria comunitária e 8 idiomas.
+Comece com 601 recursos, 20 patterns operacionais, 20 loop contracts validados por schema e 8 starters de runtime (3 executáveis e 5 templates prontos para adaptação), além de uma galeria comunitária e 8 idiomas.
 
 ## Modelo Mental
 

README.zh-CN.md

@@ -1,6 +1,6 @@
 # Awesome Loop Engineering
 
-<!-- last-synced: 2026-07-18 -->
+<!-- last-synced: 2026-07-20 -->
 
 <p align="center">
   <img src="assets/awesome-loop-engineering-cover.png" alt="Awesome Loop Engineering cover" width="100%">
@@ -32,7 +32,7 @@ Prompt engineering 改进你对模型说什么。Context engineering 改进模
 
 Loop Engineering 专指具备明确触发、外部验证和持久状态的可重复 AI-agent 与 coding-agent 系统，不包括软件事件循环、控制论、增长循环、通用 workflow automation 或非 AI feedback loop。
 
-可直接从 579 篇论文、官方文档、工具、benchmark 与实践指南，20 个操作模式、20 个经 schema 校验的 loop contracts 和 8 个 runtime starters（3 个可直接执行，5 个可复制改造）开始，并通过社区 gallery 和 8 种语言入口继续探索。
+可直接从 601 篇论文、官方文档、工具、benchmark 与实践指南，20 个操作模式、20 个经 schema 校验的 loop contracts 和 8 个 runtime starters（3 个可直接执行，5 个可复制改造）开始，并通过社区 gallery 和 8 种语言入口继续探索。
 
 ## 一句话定位
 

assets/MEDIA_KIT.md

@@ -14,11 +14,11 @@ The original 1200x630 cover presents Loop Engineering as the operating layer abo
 
 ### One Sentence
 
-Awesome Loop Engineering is a field guide and implementation kit for recurring AI-agent systems, connecting 579 resources to 20 operational patterns, 20 loop contracts, 8 runtime starters, an interactive atlas, and a structured dataset.
+Awesome Loop Engineering is a field guide and implementation kit for recurring AI-agent systems, connecting 601 resources to 20 operational patterns, 20 loop contracts, 8 runtime starters, an interactive atlas, and a structured dataset.
 
 ### Short Paragraph
 
-Awesome Loop Engineering maps how recurring AI agents discover work, act with scoped tools, pass independent verification, preserve state, and decide whether to retry, hand off, or stop. Readers can explore 579 papers, official docs, tools, benchmarks, and guides, then build from 20 patterns, 20 schema-checked contracts, and 8 runtime starters.
+Awesome Loop Engineering maps how recurring AI agents discover work, act with scoped tools, pass independent verification, preserve state, and decide whether to retry, hand off, or stop. Readers can explore 601 papers, official docs, tools, benchmarks, and guides, then build from 20 patterns, 20 schema-checked contracts, and 8 runtime starters.
 
 ### What Distinguishes It
 
@@ -33,7 +33,7 @@ Awesome Loop Engineering maps how recurring AI agents discover work, act with sc
 | --- | --- | --- |
 | GitHub repository | <https://github.com/ChaoYue0307/awesome-loop-engineering> | Stars, forks, issues, pull requests, and source browsing |
 | Interactive field guide | <https://chaoyue0307.github.io/awesome-loop-engineering/> | Visual explanation and Resource Atlas |
-| Current release | <https://github.com/ChaoYue0307/awesome-loop-engineering/releases/tag/v0.8.0> | Versioned summary and low-noise release subscription |
+| Current release | <https://github.com/ChaoYue0307/awesome-loop-engineering/releases/tag/v0.9.0> | Versioned summary and low-noise release subscription |
 | Hugging Face dataset | <https://huggingface.co/datasets/cy0307/awesome-loop-engineering> | Tabular exploration and programmatic loading |
 | Update digest | <https://github.com/ChaoYue0307/awesome-loop-engineering/discussions/9> | New resources, corrections, artifacts, and contributor tasks |
 

data/README.md

@@ -1,6 +1,6 @@
 # Dataset Exports
 
-Download all 579 resources as deterministic tabular exports.
+Download all 601 resources as deterministic tabular exports.
 
 - `resources.csv` - Tabular export for spreadsheets and ad hoc analysis.
 - `resources.jsonl` - JSON Lines export and source for the Hugging Face Parquet build.

data/arxiv_publication_audit.csv

@@ -33,7 +33,14 @@ ale-0141,2607.11288,Mako: A Self-Evolving Agentic Operating System for Autonomou
 ale-0142,2607.10856,How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study,How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
 ale-0143,2607.10113,Dynamic Agent Skills: A Lifecycle Survey and Taxonomy of Evolving Skill Libraries,Dynamic Agent Skills: A Lifecycle Survey and Taxonomy of Evolving Skill Libraries,2026,accepted,Dynamic Agent Skills: A Lifecycle Survey and Taxonomy of Evolving Skill Libraries,2026,2026,Transactions on Machine Learning Research (TMLR),OpenReview,,https://openreview.net/forum?id=cjU3YbcRr8,https://openreview.net/forum?id=cjU3YbcRr8,Current arXiv acceptance note and OpenReview record,1.000,2026-07-17
 ale-0144,2605.27276,SIA: Self Improving AI with Harness and Weight Updates,SIA: Self Improving AI with Harness & Weight Updates,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-18
+ale-0150,2502.05171,Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach,Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach,2025,published,Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach,2025,2025,Advances in Neural Information Processing Systems 38 (NeurIPS 2025),Neural Information Processing Systems Foundation,,https://openreview.net/forum?id=D6o6Bwtq7h,https://openreview.net/forum?id=D6o6Bwtq7h,OpenReview conference record,1.000,2026-07-20
+ale-0151,2507.10524,Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation,Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation,2025,published,Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation,2025,2025,Advances in Neural Information Processing Systems 38 (NeurIPS 2025),Neural Information Processing Systems Foundation,,https://openreview.net/forum?id=QuqsEIVWIG,https://openreview.net/forum?id=QuqsEIVWIG,OpenReview conference record,1.000,2026-07-20
 ale-0152,2510.25741,Scaling Latent Reasoning via Looped Language Models,Scaling Latent Reasoning via Looped Language Models,2025,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-18
+ale-0157,2606.18206,Fixed-Point Reasoners: Stable and Adaptive Deep Looped Transformers,Fixed-Point Reasoners: Stable and Adaptive Deep Looped Transformers,2026,published,Fixed-Point Reasoners: Stable and Adaptive Deep Looped Transformers,2026,2026,"Proceedings of the 43rd International Conference on Machine Learning (ICML), PMLR 306",PMLR,,https://openreview.net/pdf/51350b6e425ed0500ac9eb9cec78ba15d9f5d1ba.pdf,https://openreview.net/pdf/51350b6e425ed0500ac9eb9cec78ba15d9f5d1ba.pdf,PMLR camera-ready record,1.000,2026-07-20
+ale-0158,2607.16051,Loop the Loopies!,Loop the Loopies!,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-20
+ale-0159,2606.04438,LoopMoE: Unifying Iterative Computation with Mixture-of-Experts for Language Modeling,LoopMoE: Unifying Iterative Computation with Mixture-of-Experts for Language Modeling,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-20
+ale-0160,2605.09165,Sparse Layers are Critical to Scaling Looped Language Models,Sparse Layers are Critical to Scaling Looped Language Models,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-20
+ale-0161,2604.11791,A Mechanistic Analysis of Looped Reasoning Language Models,A Mechanistic Analysis of Looped Reasoning Language Models,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-20
 ale-0158,2602.11698,SpiralFormer: Looped Transformers Can Learn Hierarchical Dependencies via Multi-Resolution Recursion,SpiralFormer: Looped Transformers Can Learn Hierarchical Dependencies via Multi-Resolution Recursion,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-18
 ale-0159,2605.23872,Training-Free Looped Transformers,Training-Free Looped Transformers,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-18
 ale-0160,2604.07822,"Loop, Think, & Generalize: Implicit Reasoning in Recurrent-Depth Transformers","Loop, Think, & Generalize: Implicit Reasoning in Recurrent-Depth Transformers",2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-18
@@ -48,6 +55,11 @@ ale-0155,2605.20456,Agentic Agile-V: From Vibe Coding to Verified Engineering,Ag
 ale-0156,2509.06216,Agentic Software Engineering: Foundational Pillars and a Research Roadmap,Agentic Software Engineering: Foundational Pillars and a Research Roadmap,2025,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
 ale-0163,2607.13285,"Harness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable","Harness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable",2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
 ale-0164,2607.14159,MemoHarness: Agent Harnesses That Learn from Experience,MemoHarness: Agent Harnesses That Learn from Experience,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
+ale-0194,2607.15524,Recursive Harness Self-Improvement,Recursive Harness Self-Improvement,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-20
+ale-0195,2607.15557,SkillCorpus: Consolidating and Evaluating the Open Skill Ecosystem for Real-World LLM Agents,SkillCorpus: Consolidating and Evaluating the Open Skill Ecosystem for Real-World LLM Agents,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-20
+ale-0196,2607.15715,Behavioral Controllability of Agentic Models for Information Extraction: From Fixed Workflows to Reflective Agents,Behavioral Controllability of Agentic Models for Information Extraction: From Fixed Workflows to Reflective Agents,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-20
+ale-0197,2607.15684,Understanding Agent-Reactive Bugs at the Model-Harness Boundary: An Empirical Study of LLM Agent Issue Reports,Understanding Agent-Reactive Bugs at the Model-Harness Boundary: An Empirical Study of LLM Agent Issue Reports,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-20
+ale-0198,2607.16038,"SciForge: An AI-Native, Multimodal Workbench for Scientific Discovery","SciForge: An AI-Native, Multimodal Workbench for Scientific Discovery",2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-20
 ale-0166,2405.15793,SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering,SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering,2024,published,SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering,2024,2024,Advances in Neural Information Processing Systems 37 (NeurIPS),Neural Information Processing Systems Foundation,10.52202/079017-1601,https://proceedings.neurips.cc/paper_files/paper/2024/hash/5a7c947568c1b1328ccc5230172e1e7c-Abstract-Conference.html,https://doi.org/10.52202/079017-1601,NeurIPS proceedings and DOI records,1.000,2026-07-17
 ale-0169,2407.16741,OpenHands: An Open Platform for AI Software Developers as Generalist Agents,OpenHands: An Open Platform for AI Software Developers as Generalist Agents,2025,published,OpenHands: An Open Platform for AI Software Developers as Generalist Agents,2025,2025,International Conference on Learning Representations (ICLR),International Conference on Learning Representations,,https://proceedings.iclr.cc/paper_files/paper/2025/hash/a4b6ad6b48850c0c331d1259fc66a69c-Abstract-Conference.html,https://proceedings.iclr.cc/paper_files/paper/2025/hash/a4b6ad6b48850c0c331d1259fc66a69c-Abstract-Conference.html,ICLR proceedings record,1.000,2026-07-17
 ale-0171,2407.01489,Agentless: Demystifying LLM-based Software Engineering Agents,Agentless: Demystifying LLM-based Software Engineering Agents,2024,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
@@ -59,6 +71,8 @@ ale-0178,2504.15228,A Self-Improving Coding Agent,A Self-Improving Coding Agent,
 ale-0179,2607.03691,Don't Blame the Large Language Model: How Scaffolding Evolution Shapes Coding Agent Quality,Don't Blame the Large Language Model: How Scaffolding Evolution Shapes Coding Agent Quality,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
 ale-0180,2607.11423,"ToFu: A White-Box, Token-Efficient Agent Harness for Researchers","ToFu: A White-Box, Token-Efficient Agent Harness for Researchers",2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
 ale-0181,2607.10569,When Does Restricting a Coding Agent to execute_code Help?,When Does Restricting a Coding Agent to execute_code Help? A Regime $\times$ Agent-Design Ablation,2026,accepted,When Does Restricting a Coding Agent to execute_code Help? A Regime $\times$ Agent-Design Ablation,2026,2026,KDD Workshop on Agentic Software Engineering (SE 3.0),ACM SIGKDD,,https://arxiv.org/abs/2607.10569,https://agent-se.github.io/,Current arXiv acceptance note and official non-archival workshop page,1.000,2026-07-17
+ale-0216,2607.15854,Agentic Synthesis against Counterexample-Supplemented Sketches,Agentic Synthesis against Counterexample-Supplemented Sketches,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-20
+ale-0217,2607.15769,Making Agent-Mediated Contributions Governable: A Project-Level Governance Manifest for Open-Source AI Collaboration,Making Agent-Mediated Contributions Governable: A Project-Level Governance Manifest for Open-Source AI Collaboration,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-20
 ale-0224,2607.13091,Self-Improving AI Coding Agents Through Accumulated Behavioral Rules: A Closed-Loop Framework,Self-Improving AI Coding Agents Through Accumulated Behavioral Rules: A Closed-Loop Framework,2026,accepted,Self-Improving AI Coding Agents Through Accumulated Behavioral Rules: A Closed-Loop Framework,2026-06-22,2026,32nd IEEE International Conference on Engineering Technology and Innovation (ICE/ITMC),IEEE,,https://arxiv.org/abs/2607.13091,https://ice-conference.org/,Current arXiv acceptance note and official conference page,1.000,2026-07-17
 ale-0238,2604.25850,Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses,Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
 ale-0239,2603.28052,Meta-Harness: End-to-End Optimization of Model Harnesses,Meta-Harness: End-to-End Optimization of Model Harnesses,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
@@ -92,6 +106,8 @@ ale-0286,2607.05188,Latent Programming Horizons in Coding Agents,Latent Programm
 ale-0288,2607.14167,Structured Feedback Improves Repair in an LLM Agent Loop,Structured Feedback Improves Repair in an LLM Agent Loop,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
 ale-0289,2607.14336,Copy-on-Write Scoring: Application-Specific Agent Evaluations,Copy-on-Write Scoring: Application-Specific Agent Evaluations,2026,accepted,Copy-on-Write Scoring: Application-Specific Agent Evaluations,2026,2026,ICML Workshop on Agents in the Wild: Safety Security and Beyond,International Conference on Machine Learning,,https://arxiv.org/abs/2607.14336,https://agentwild-workshop.github.io/icml2026/,Current arXiv acceptance note and official workshop page,1.000,2026-07-17
 ale-0290,2607.14340,The Prover Is the Judge: Verified Security Software from AI Coding Agents in Ada/SPARK,The Prover Is the Judge: Verified Security Software from AI Coding Agents in Ada/SPARK,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
+ale-0328,2607.15718,Verified LLM-Driven Synthesis for Concept Design,Verified LLM-Driven Synthesis for Concept Design,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-20
+ale-0329,2607.15620,AEGIS: Assay-Aware Protocol Validation and Runtime Monitoring for Open-Source Liquid Handling Robots,AEGIS: Assay-Aware Protocol Validation and Runtime Monitoring for Open-Source Liquid Handling Robots,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-20
 ale-0293,2603.02798,GLEAN: Guideline-Grounded Evidence Accumulation for High-Stakes Agent Verification,Guideline-Grounded Evidence Accumulation for High-Stakes Agent Verification,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-18
 ale-0300,2607.05189,When Claws Remember but Do Not Tell: Stealthy Memory Injection in Persistent Personal Agents,When Claws Remember but Do Not Tell: Stealthy Memory Injection in Persistent Personal Agents,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
 ale-0301,2607.05029,Your Agent's Memories Are Not Its Own: Forged Reasoning Attacks on LLM Agent Memory and Defenses,Your Agent's Memories Are Not Its Own: Forged Reasoning Attacks on LLM Agent Memory and Defenses,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
@@ -118,6 +134,9 @@ ale-0329,2607.13718,"How Agents Ask for Permission: User Permissions for AI Agen
 ale-0330,2607.14166,Stop Means Stop: Measuring and Repairing the Enforcement Gap in Agent-Framework Control Primitives,Stop Means Stop: Measuring and Repairing the Enforcement Gap in Agent-Framework Control Primitives,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
 ale-0331,2607.14611,Bad Memory: Evaluating Prompt Injection Risks from Memory in Agentic Systems,Bad Memory: Evaluating Prompt Injection Risks from Memory in Agentic Systems,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
 ale-0332,2607.15143,"Setup Complete, Now You Are Compromised: Weaponizing Setup Instructions Against AI Coding Agents","Setup Complete, Now You Are Compromised: Weaponizing Setup Instructions Against AI Coding Agents",2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
+ale-0374,2607.15550,SeerGuard: A Safety Framework for Mobile GUI Agents via World Model Prediction,SeerGuard: A Safety Framework for Mobile GUI Agents via World Model Prediction,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-20
+ale-0375,2607.15657,Do Agents Dream of False Memories? Black-box Visual Attacks on Long-term Memory in Multimodal AI Agents,Do Agents Dream of False Memories? Black-box Visual Attacks on Long-term Memory in Multimodal AI Agents,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-20
+ale-0376,2607.15589,MemoGuard: An Adaptive Runtime for Guarding Against Memory Traps in Communication-Limited Robot Navigation,MemoGuard: An Adaptive Runtime for Guarding Against Memory Traps in Communication-Limited Robot Navigation,2026,accepted,MemoGuard: An Adaptive Runtime for Guarding Against Memory Traps in Communication-Limited Robot Navigation,2026,2026,IEEE/ACM ESWEEK (CODES) 2026,IEEE/ACM,,https://arxiv.org/abs/2607.15589,https://arxiv.org/abs/2607.15589,Current arXiv acceptance note,1.000,2026-07-20
 ale-0345,2601.12030,ARC: Active and Reflection-driven Context Management for Long-Horizon Agents,ARC: Active and Reflection-driven Context Management for Long-Horizon Information Seeking Agents,2026,published,ARC: Active and Reflection-driven Context Management for Long-Horizon Information Seeking Agents,2026,2026,Findings of the Association for Computational Linguistics: ACL,Association for Computational Linguistics,10.18653/v1/2026.findings-acl.930,https://aclanthology.org/2026.findings-acl.930/,https://doi.org/10.18653/v1/2026.findings-acl.930,ACL Anthology and DOI records,1.000,2026-07-17
 ale-0346,2603.07670,"Memory for Autonomous LLM Agents: Mechanisms, Evaluation, and Emerging Frontiers","Memory for Autonomous LLM Agents:Mechanisms, Evaluation, and Emerging Frontiers",2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
 ale-0347,2604.08224,"Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering","Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering",2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
@@ -142,6 +161,8 @@ ale-0374,2607.11388,StructAgent: Harness Long-Horizon Digital Agents with Unifie
 ale-0375,2607.10608,The Compliance Trap: Diagnosing How AI Agents Consume Conflicting Memory,The Compliance Trap: Diagnosing How AI Agents Consume Conflicting Memory,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
 ale-0380,2607.13591,Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents,Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
 ale-0381,2607.14390,Why Git Is the Memory Solution for the Agentic Development Lifecycle,Why Git Is the Memory Solution for the Agentic Development Lifecycle,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
+ale-0427,2607.15899,ContinuityBench: A Benchmark and Systems Study of Stateful Failover in Multi-Provider LLM Routing,ContinuityBench: A Benchmark and Systems Study of Stateful Failover in Multi-Provider LLM Routing,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-20
+ale-0428,2607.16019,"Presentation, Not Mechanism: A Render Confound in Deprecation-Aware Memory Evaluation","Presentation, Not Mechanism: A Render Confound in Deprecation-Aware Memory Evaluation",2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-20
 ale-0387,2509.25140,ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory,ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory,2025,published,ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory,2026,2026,International Conference on Learning Representations (ICLR),International Conference on Learning Representations,,https://openreview.net/forum?id=jL7fwchScm,https://openreview.net/forum?id=jL7fwchScm,OpenReview proceedings record,,2026-07-18
 ale-0388,2510.11967,Scaling Long-Horizon LLM Agent via Context-Folding,Scaling Long-Horizon LLM Agent via Context-Folding,2025,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-18
 ale-0396,2604.11378,From Agent Loops to Structured Graphs: A Scheduler-Theoretic Framework for LLM Agent Execution,From Agent Loops to Structured Graphs:A Scheduler-Theoretic Framework for LLM Agent Execution,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
@@ -150,6 +171,7 @@ ale-0399,2603.22386,From Static Templates to Dynamic Runtime Graphs: A Survey of
 ale-0406,2607.02807,SwarmResearch: Orchestrating Coding Agents for Open-Ended Discovery,SwarmResearch: Orchestrating Coding Agents for Open-Ended Discovery,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
 ale-0417,2607.11399,Agentic Routing: The Harness-Native Data Flywheel,Agentic Routing: The Harness-Native Data Flywheel,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
 ale-0418,2607.11138,A Formal Hierarchical Architecture for Agentic Orchestration with Stack-Based Execution,A Formal Hierarchical Architecture for Agentic Orchestration with Stack-Based Execution and Lazy Discovery,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
+ale-0468,2607.16109,The Honest Quorum Problem: Epistemic Byzantine Fault Tolerance for Agentic Infrastructure,The Honest Quorum Problem: Epistemic Byzantine Fault Tolerance for Agentic Infrastructure,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-20
 ale-0429,2512.08296,Towards a Science of Scaling Agent Systems,Towards a Science of Scaling Agent Systems,2025,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-18
 ale-0423,2310.06770,SWE-bench: Can Language Models Resolve Real-World GitHub Issues?,SWE-bench: Can Language Models Resolve Real-World GitHub Issues?,2024,published,SWE-bench: Can Language Models Resolve Real-World GitHub Issues?,2024,2024,International Conference on Learning Representations (ICLR),International Conference on Learning Representations,,https://proceedings.iclr.cc/paper_files/paper/2024/hash/edac78c3e300629acfe6cbe9ca88fb84-Abstract-Conference.html,https://proceedings.iclr.cc/paper_files/paper/2024/hash/edac78c3e300629acfe6cbe9ca88fb84-Abstract-Conference.html,ICLR proceedings record,1.000,2026-07-17
 ale-0424,2505.23419,SWE-bench Goes Live,SWE-bench Goes Live!,2025,published,SWE-bench Goes Live!,2025,2025,Advances in Neural Information Processing Systems 38: Datasets and Benchmarks Track (NeurIPS),Neural Information Processing Systems Foundation,,https://proceedings.neurips.cc/paper_files/paper/2025/hash/d83c4a745789690f82e86d0ef752ae7c-Abstract-Datasets_and_Benchmarks_Track.html,https://proceedings.neurips.cc/paper_files/paper/2025/hash/d83c4a745789690f82e86d0ef752ae7c-Abstract-Datasets_and_Benchmarks_Track.html,NeurIPS proceedings record,1.000,2026-07-17
@@ -198,9 +220,11 @@ ale-0470,2607.10526,"Agents Don't Just Agree, They Remember: Benchmarking Persis
 ale-0471,2607.13396,Set-shifting Behavioral Test for Harnessed Agents,Set-shifting Behavioral Test for Harnessed Agents,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
 ale-0472,2607.14642,MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers,MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
 ale-0473,2607.15205,MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization,MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
+ale-0525,2607.15660,ToolVerse: Unlocking Massive Environments and Long-Horizon Tasks for Agentic Reinforcement Learning,ToolVerse: Unlocking Massive Environments and Long-Horizon Tasks for Agentic Reinforcement Learning,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-20
 ale-0482,2607.07052,"Progressive Crystallization: Turning Agent Exploration into Deterministic, Lower-Cost Workflows","Progressive Crystallization: Turning Agent Exploration into Deterministic, Lower-Cost Workflows in Production",2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
 ale-0483,2607.08010,Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems,Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
 ale-0485,2607.07689,Agent Delivery Engineering Predictive Reliability Framework,Agent Delivery Engineering Predictive Reliability Framework,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
+ale-0540,2607.16127,CADAQUES: A Cost-Aware Dual Architecture for Query-Efficient Autonomous Discovery,CADAQUES: A Cost-Aware Dual Architecture for Query-Efficient Autonomous Discovery,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-20
 ale-0488,2607.02134,Coding-agents can replicate scientific machine learning papers,Coding-agents can replicate scientific machine learning papers,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
 ale-0509,2606.17799,Position: Coding Benchmarks Are Misaligned with Agentic Software Engineering,Position: Coding Benchmarks Are Misaligned with Agentic Software Engineering,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17
 ale-0510,2603.23994,Understanding the Challenges in Iterative Generative Optimization with LLMs,Understanding the Challenges in Iterative Generative Optimization with LLMs,2026,preprint-only,,,,,,,,,DBLP and Crossref exact-title audit,,2026-07-17

data/arxiv_publication_overrides.csv

@@ -41,3 +41,7 @@ arxiv_id,status,publication_date,publication_year,publication_venue,publisher,do
 2601.21008,published,2026,2026,"Proceedings of the 43rd International Conference on Machine Learning (ICML), PMLR 306",PMLR,,https://openreview.net/pdf/16a0193aa4e71ffe6c921ac0081a66b525eea017.pdf,https://openreview.net/pdf/16a0193aa4e71ffe6c921ac0081a66b525eea017.pdf,PMLR camera-ready record
 2603.13428,accepted,2026,2026,Proceedings of the 43rd International Conference on Machine Learning (ICML),PMLR,,https://arxiv.org/abs/2603.13428,https://swe-milestone.com/,Current arXiv acceptance note and official project record
 2509.25140,published,2026,2026,International Conference on Learning Representations (ICLR),International Conference on Learning Representations,,https://openreview.net/forum?id=jL7fwchScm,https://openreview.net/forum?id=jL7fwchScm,OpenReview proceedings record
+2502.05171,published,2025,2025,Advances in Neural Information Processing Systems 38 (NeurIPS 2025),Neural Information Processing Systems Foundation,,https://openreview.net/forum?id=D6o6Bwtq7h,https://openreview.net/forum?id=D6o6Bwtq7h,OpenReview conference record
+2507.10524,published,2025,2025,Advances in Neural Information Processing Systems 38 (NeurIPS 2025),Neural Information Processing Systems Foundation,,https://openreview.net/forum?id=QuqsEIVWIG,https://openreview.net/forum?id=QuqsEIVWIG,OpenReview conference record
+2606.18206,published,2026,2026,"Proceedings of the 43rd International Conference on Machine Learning (ICML), PMLR 306",PMLR,,https://openreview.net/pdf/51350b6e425ed0500ac9eb9cec78ba15d9f5d1ba.pdf,https://openreview.net/pdf/51350b6e425ed0500ac9eb9cec78ba15d9f5d1ba.pdf,PMLR camera-ready record
+2607.15589,accepted,2026,2026,IEEE/ACM ESWEEK (CODES) 2026,IEEE/ACM,,https://arxiv.org/abs/2607.15589,https://arxiv.org/abs/2607.15589,Current arXiv acceptance note

data/first_seen.json

@@ -89,5 +89,29 @@
   "https://arxiv.org/abs/2606.31779": "2026-07-18",
   "https://arxiv.org/abs/2605.16048": "2026-07-18",
   "https://arxiv.org/abs/2605.26106": "2026-07-18",
-  "https://github.com/huskydoge/Awesome-Loop-Models": "2026-07-18"
+  "https://github.com/huskydoge/Awesome-Loop-Models": "2026-07-18",
+  "https://arxiv.org/abs/2502.05171": "2026-07-18",
+  "https://arxiv.org/abs/2507.10524": "2026-07-18",
+  "https://arxiv.org/abs/2607.16051": "2026-07-20",
+  "https://arxiv.org/abs/2606.04438": "2026-07-20",
+  "https://arxiv.org/abs/2605.09165": "2026-07-20",
+  "https://arxiv.org/abs/2606.18206": "2026-07-20",
+  "https://arxiv.org/abs/2604.11791": "2026-07-20",
+  "https://arxiv.org/abs/2607.15524": "2026-07-20",
+  "https://arxiv.org/abs/2607.15557": "2026-07-20",
+  "https://arxiv.org/abs/2607.15715": "2026-07-20",
+  "https://arxiv.org/abs/2607.15684": "2026-07-20",
+  "https://arxiv.org/abs/2607.16038": "2026-07-20",
+  "https://arxiv.org/abs/2607.15854": "2026-07-20",
+  "https://arxiv.org/abs/2607.15769": "2026-07-20",
+  "https://arxiv.org/abs/2607.15718": "2026-07-20",
+  "https://arxiv.org/abs/2607.15620": "2026-07-20",
+  "https://arxiv.org/abs/2607.15550": "2026-07-20",
+  "https://arxiv.org/abs/2607.15657": "2026-07-20",
+  "https://arxiv.org/abs/2607.15589": "2026-07-20",
+  "https://arxiv.org/abs/2607.15899": "2026-07-20",
+  "https://arxiv.org/abs/2607.16019": "2026-07-20",
+  "https://arxiv.org/abs/2607.16109": "2026-07-20",
+  "https://arxiv.org/abs/2607.15660": "2026-07-20",
+  "https://arxiv.org/abs/2607.16127": "2026-07-20"
 }

data/resources.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5f37704c40b407707ffab344d96d904cfe1dae25a624abb695bc3948c1356bed
-size 466731
+oid sha256:2014d940a1076cb34c9403ca3aea038654eb3d49d0b1bbb111db15186ea03a31
+size 494773

design-qa.md

@@ -19,7 +19,7 @@
 - Contract and lifecycle: purpose-built mobile summaries replace desktop-wide figures at 560 px and below.
 - Runtime map: desktop figure plus eight labeled starters in a balanced 4 x 2 grid; mobile uses the explanatory copy and one-column starter sequence instead of shrinking the wide figure.
 - Maturity model: desktop figure plus semantic levels 00-06; mobile keeps the readable level rows and hides the wide supporting figure.
-- Social preview: 1280 x 640, exact 579-resource, 20-pattern, 20-contract, and 8-starter counts, with the model-to-operations scope line.
+- Social preview: 1280 x 640, exact 601-resource, 20-pattern, 20-contract, and 8-starter counts, with the model-to-operations scope line.
 - Interactive hero, desktop: 1440 x 1000, one ready WebGL canvas, no horizontal overflow, and meaningful agent-system labels.
 - Interactive hero, mobile: 390 x 844, one ready WebGL canvas, no horizontal overflow, and all six stations inside the frame.
 - Agent-loop semantics: the bounded agent workspace is the visual anchor; context and three scoped tools feed the agent, verification remains external, memory remains durable, and decision outcomes branch to retry, human handoff, or verified exit.
@@ -70,15 +70,15 @@
 - Semantic engineering stack, desktop: `/tmp/awesome-loop-stack-desktop.png`
 - Semantic agent loop, mobile: `/tmp/awesome-loop-agent-mobile.png`
 
-## 2026-07-18 Repository Audit
+## 2026-07-20 Repository Audit
 
 - Desktop: 1440 x 1000, no horizontal overflow, no hero text/scene overlap, and all nine section headings render without requiring scroll-triggered opacity changes.
 - Tablet: 768 x 900. The first pass clipped the intake and decision stations. The responsive camera now fits an 11.2-unit horizontal view between 720 px and 900 px, keeping all six stations, the retry path, and the outcome node inside the canvas.
 - Phone: 390 x 844, no horizontal overflow. The compact loop keeps all six stations, the moving work packet, legend, outcome key, and stage selector visible without crossing the hero copy.
 - Mobile navigation: opens all nine project links, reports `aria-expanded="true"`, closes on Escape, and restores focus to the menu button.
 - Pattern filter: `accessibility` returns exactly one of the 20 patterns.
-- Resource Atlas: remains idle above the fold, loads on approach or a direct resource anchor, and reports `579 of 579 resources | showing 8` on a fresh mobile load.
-- Resource interactions: the Model loop-layer filter returns 24 of 579 resources (23 papers plus Awesome Loop Models), paginates eight at a time on mobile, and renders LoopWM with its 2026 arXiv record and adjacent-scope label.
+- Resource Atlas: remains idle above the fold, loads on approach or a direct resource anchor, and reports `601 of 601 resources | showing 8` on a fresh mobile load.
+- Resource interactions: the Model loop-layer filter returns 24 of 601 resources (23 papers plus Awesome Loop Models), paginates eight at a time on mobile, and renders LoopWM with its 2026 arXiv record and adjacent-scope label.
 - Resource row schema: desktop exposes Work, Year, Published at, and Evidence; mobile stacks the same labels. A `ReasoningBank` query returns one row with its ICLR venue, arXiv identifier, Tier A evidence, task fit, and source record without horizontal overflow.
 - Scene motion: the canvas frame counter advanced from 640 to 662 over 900 ms. Two canvas crops taken 1.2 seconds apart changed 17.79% of sampled RGB channels.
 - Scene pixels: the current desktop canvas has 7,062 colors with RGB standard deviations of 35.93, 29.76, and 22.29; mobile has 5,755 colors with deviations of 40.73, 30.54, and 19.70. Both renders are nonblank and well contrasted.
@@ -86,8 +86,8 @@
 - Future directions: direct-anchor restoration waits for the Resource Atlas reflow, all three audience tracks remain readable, and the agenda CTA is fully visible at desktop and mobile widths.
 - Console: no page-origin errors or warnings during mobile navigation, pattern filtering, and Resource Atlas interactions.
 - Narrow phone: 320 x 844, document width equals viewport width; the headline stays within 16 px gutters and the Three.js canvas remains fully inside the 288 px content width.
-- Source audit: 579 rows checked; 528 public sources reachable, 3 access-restricted, 48 repository-native, and 0 broken or unreachable as of 2026-07-18 UTC.
-- Data and copy: the website, README, translations, social preview source, release copy, and Hugging Face card all report 579 resources, 20 patterns, 20 contracts, 8 runtime starters, and version 0.8.0. The dataset exposes 50 fields, including `loop_layer` and `scope_fit`.
+- Source audit: 601 rows checked; 547 public sources reachable, 6 access-restricted, 48 repository-native, and 0 broken or unreachable as of 2026-07-20 UTC.
+- Data and copy: the website, README, translations, social preview source, release copy, and Hugging Face card all report 601 resources, 20 patterns, 20 contracts, 8 runtime starters, and version 0.9.0. The dataset exposes 50 fields, including `loop_layer` and `scope_fit`.
 - Semantic scene pass: the desktop hero crop paints 14.61% of pixels beyond the paper background with a 254.71 luminance range; the desktop stack paints 27.83% with a 249.98 range; the mobile hero paints 20.84% with a 229.45 range.
 - Stage interaction: selecting Verify updates `aria-pressed` across all six controls and replaces the live detail with the independent evidence-gate explanation.
 - Progressive enhancement: desktop loads the agent-loop and engineering-stack canvases; mobile loads only the agent-loop canvas and exposes the four-layer text stack. Both target viewports have zero horizontal overflow and no console messages.

docs/index.html

@@ -4,7 +4,7 @@
     <meta charset="utf-8">
     <meta name="viewport" content="width=device-width, initial-scale=1">
     <title>Awesome Loop Engineering</title>
-    <meta name="description" content="Explore 579 resources from model recurrence to governed agent operations, then build with 20 patterns, 20 contracts, and 8 runtime starters.">
+    <meta name="description" content="Explore 601 resources from model recurrence to governed agent operations, then build with 20 patterns, 20 contracts, and 8 runtime starters.">
     <meta name="robots" content="index,follow">
     <meta name="author" content="Chaoyue He">
     <link rel="canonical" href="https://chaoyue0307.github.io/awesome-loop-engineering/">
@@ -15,16 +15,16 @@
     <link href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@500;600&amp;family=Inter:wght@400;500;600;700;800&amp;display=swap" rel="stylesheet">
     <meta property="og:type" content="website">
     <meta property="og:title" content="Awesome Loop Engineering">
-    <meta property="og:description" content="Explore 579 papers, docs, tools, benchmarks, and guides, then build recurring agent systems with 20 patterns, 20 contracts, and 8 starters.">
+    <meta property="og:description" content="Explore 601 papers, docs, tools, benchmarks, and guides, then build recurring agent systems with 20 patterns, 20 contracts, and 8 starters.">
     <meta property="og:url" content="https://chaoyue0307.github.io/awesome-loop-engineering/">
-    <meta property="og:image" content="https://chaoyue0307.github.io/awesome-loop-engineering/assets/social-preview-579-v08.png">
+    <meta property="og:image" content="https://chaoyue0307.github.io/awesome-loop-engineering/assets/social-preview-601-v09.png">
     <meta property="og:image:width" content="1280">
     <meta property="og:image:height" content="640">
     <meta property="og:image:alt" content="Awesome Loop Engineering: design loops, not just prompts.">
     <meta name="twitter:card" content="summary_large_image">
     <meta name="twitter:title" content="Awesome Loop Engineering">
-    <meta name="twitter:description" content="579 resources from model recurrence to agent operations, plus 20 patterns, 20 contracts, and 8 starters for building reliable loops.">
-    <meta name="twitter:image" content="https://chaoyue0307.github.io/awesome-loop-engineering/assets/social-preview-579-v08.png">
+    <meta name="twitter:description" content="601 resources from model recurrence to agent operations, plus 20 patterns, 20 contracts, and 8 starters for building reliable loops.">
+    <meta name="twitter:image" content="https://chaoyue0307.github.io/awesome-loop-engineering/assets/social-preview-601-v09.png">
     <link rel="alternate" type="application/atom+xml" title="Awesome Loop Engineering releases" href="https://github.com/ChaoYue0307/awesome-loop-engineering/releases.atom">
     <link rel="alternate" type="application/atom+xml" title="Awesome Loop Engineering commits" href="https://github.com/ChaoYue0307/awesome-loop-engineering/commits/main.atom">
     <style>
@@ -1276,13 +1276,13 @@
         "description": "A practical field guide and implementation kit for recurring, stateful, verified AI-agent systems.",
         "keywords": ["Loop Engineering", "AI agents", "Coding agents", "Agent workflows", "Agentic loops"],
         "datePublished": "2026-06-10",
-        "dateModified": "2026-07-18",
+        "dateModified": "2026-07-20",
         "sameAs": [
           "https://github.com/ChaoYue0307/awesome-loop-engineering",
           "https://huggingface.co/datasets/cy0307/awesome-loop-engineering"
         ],
         "license": "https://creativecommons.org/publicdomain/zero/1.0/",
-        "image": "https://chaoyue0307.github.io/awesome-loop-engineering/assets/social-preview-579-v08.png",
+        "image": "https://chaoyue0307.github.io/awesome-loop-engineering/assets/social-preview-601-v09.png",
         "logo": "https://chaoyue0307.github.io/awesome-loop-engineering/assets/awesome-loop-engineering-logo.png",
         "author": { "@type": "Person", "name": "Chaoyue He" }
       }
@@ -1330,19 +1330,19 @@
         "@context": "https://schema.org",
         "@type": "Dataset",
         "name": "Awesome Loop Engineering resources",
-        "description": "Explore 579 resources across model, agent, harness, workflow, operations, and evaluation layers, with publication, contribution, novelty, impact, and evidence fields.",
+        "description": "Explore 601 resources across model, agent, harness, workflow, operations, and evaluation layers, with publication, contribution, novelty, impact, and evidence fields.",
         "url": "https://chaoyue0307.github.io/awesome-loop-engineering/",
         "sameAs": [
           "https://huggingface.co/datasets/cy0307/awesome-loop-engineering",
           "https://github.com/ChaoYue0307/awesome-loop-engineering"
         ],
         "identifier": "https://huggingface.co/datasets/cy0307/awesome-loop-engineering",
-        "version": "0.8.0",
+        "version": "0.9.0",
         "license": "https://creativecommons.org/publicdomain/zero/1.0/",
         "isAccessibleForFree": true,
         "creator": { "@type": "Person", "name": "Chaoyue He" },
         "datePublished": "2026-06-10",
-        "dateModified": "2026-07-18",
+        "dateModified": "2026-07-20",
         "keywords": ["Loop Engineering", "AI agents", "Coding agents", "Agent reliability", "Agent evaluation"],
         "includedInDataCatalog": {
           "@type": "DataCatalog",
@@ -1405,7 +1405,7 @@
             <p class="section-label hero-kicker"><i class="icon" data-lucide="repeat-2" aria-hidden="true"></i><span>Field guide for recurring agent systems</span></p>
             <h1>Awesome Loop Engineering</h1>
             <p class="tagline">Design loops, not just prompts.</p>
-            <p class="lead">Explore 579 resources from model recurrence to agent operations, then build with 20 patterns, 20 adaptable contracts, and 8 runtime starters.</p>
+            <p class="lead">Explore 601 resources from model recurrence to agent operations, then build with 20 patterns, 20 adaptable contracts, and 8 runtime starters.</p>
             <div class="actions">
               <a class="button primary" href="https://github.com/ChaoYue0307/awesome-loop-engineering#readme"><i class="icon" data-lucide="book-open" aria-hidden="true"></i>Explore resources</a>
               <a class="button" href="#choose"><i class="icon" data-lucide="compass" aria-hidden="true"></i>Choose a loop</a>
@@ -1468,7 +1468,7 @@
             </div>
           </figure>
           <div class="stats" role="list" aria-label="Field guide at a glance">
-            <div class="stat" role="listitem"><b><i class="icon" data-lucide="library" aria-hidden="true"></i>579</b><span>resources</span></div>
+            <div class="stat" role="listitem"><b><i class="icon" data-lucide="library" aria-hidden="true"></i>601</b><span>resources</span></div>
             <div class="stat" role="listitem"><b><i class="icon" data-lucide="blocks" aria-hidden="true"></i>20</b><span>operational patterns</span></div>
             <div class="stat" role="listitem"><b><i class="icon" data-lucide="file-check-2" aria-hidden="true"></i>20</b><span>loop contracts</span></div>
             <div class="stat" role="listitem"><b><i class="icon" data-lucide="play" aria-hidden="true"></i>8</b><span>runtime starters</span></div>
@@ -1768,9 +1768,9 @@
               <p class="section-label"><i class="icon" data-lucide="library" aria-hidden="true"></i><span>06 / Resource atlas</span></p>
               <h2>Find evidence, not just links.</h2>
             </div>
-            <p class="section-intro">Filter 579 resources by goal, loop layer, lifecycle, artifact type, and evidence class. Open any result to see its contribution, novelty, impact, scope, publication details, and original source.</p>
+            <p class="section-intro">Filter 601 resources by goal, loop layer, lifecycle, artifact type, and evidence class. Open any result to see its contribution, novelty, impact, scope, publication details, and original source.</p>
           </div>
-          <p class="audit-note"><strong>Last source check, 2026-07-18 UTC:</strong> 528 public links opened successfully, 3 required access, 48 point to files in this repository, and none were broken or unreachable. <a href="https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/data/resource_source_audit.csv">View the results</a> or <a href="https://github.com/ChaoYue0307/awesome-loop-engineering/issues/new?template=annotation-correction.yml">report a correction</a>.</p>
+          <p class="audit-note"><strong>Last source check, 2026-07-20 UTC:</strong> 547 public links opened successfully, 6 required access, 48 point to files in this repository, and none were broken or unreachable. <a href="https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/data/resource_source_audit.csv">View the results</a> or <a href="https://github.com/ChaoYue0307/awesome-loop-engineering/issues/new?template=annotation-correction.yml">report a correction</a>.</p>
           <div class="loop-layer-map" aria-label="Where different loop concepts live">
             <div class="loop-layer-item"><span>Inner loop</span><strong>Model recurrence</strong><p>A learned block refines hidden or latent state inside one inference. Included as an adjacent foundation.</p></div>
             <div class="loop-layer-item"><span>Task loop</span><strong>Agent execution</strong><p>The model reasons, calls tools, observes results, and updates working state while solving one task.</p></div>
@@ -1854,7 +1854,7 @@
               <button class="resource-collection" type="button" data-collection="Apply" aria-pressed="false"><i class="icon" data-lucide="package-open" aria-hidden="true"></i>Apply</button>
             </div>
             <div class="resource-summary">
-              <p class="resource-count" id="resource-count" aria-live="polite">Preparing 579 resources...</p>
+              <p class="resource-count" id="resource-count" aria-live="polite">Preparing 601 resources...</p>
               <label>
                 <span class="sr-only">Sort resources</span>
                 <select class="resource-select resource-sort" id="resource-sort">
@@ -2004,7 +2004,7 @@
   title        = {Awesome Loop Engineering},
   year         = {2026},
   howpublished = {\url{https://github.com/ChaoYue0307/awesome-loop-engineering}},
-  note         = {Version 0.8.0}
+  note         = {Version 0.9.0}
 }</pre>
             </div>
           </div>
@@ -2437,7 +2437,7 @@
           loading = true;
           explorer.dataset.indexState = 'loading';
           list.setAttribute('aria-busy', 'true');
-          count.textContent = 'Loading 579 resources...';
+          count.textContent = 'Loading 601 resources...';
           list.replaceChildren(createText('p', 'resource-loading', 'Loading resources...'));
 
           fetch('assets/resources.json')

docs/x-v09-601.html

@@ -0,0 +1,44 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1">
+    <title>Awesome Loop Engineering: 601 resources from model recurrence to agent operations</title>
+    <meta name="description" content="Explore 601 resources, then build recurring AI-agent systems with 20 patterns, 20 contracts, and 8 runtime starters.">
+    <meta name="robots" content="noindex,follow">
+    <link rel="canonical" href="https://chaoyue0307.github.io/awesome-loop-engineering/x-v09-601.html">
+    <meta property="og:type" content="website">
+    <meta property="og:title" content="Awesome Loop Engineering">
+    <meta property="og:description" content="601 papers, docs, tools, benchmarks, and guides, plus 20 patterns, 20 contracts, and 8 starters for recurring AI-agent systems.">
+    <meta property="og:url" content="https://chaoyue0307.github.io/awesome-loop-engineering/x-v09-601.html">
+    <meta property="og:image" content="https://chaoyue0307.github.io/awesome-loop-engineering/assets/social-preview-601-v09.png">
+    <meta property="og:image:width" content="1280">
+    <meta property="og:image:height" content="640">
+    <meta property="og:image:alt" content="Awesome Loop Engineering: 601 resources, 20 patterns, 20 contracts, and 8 runtime starters.">
+    <meta name="twitter:card" content="summary_large_image">
+    <meta name="twitter:title" content="Awesome Loop Engineering">
+    <meta name="twitter:description" content="From looped models to verified agent work and bounded operations.">
+    <meta name="twitter:image" content="https://chaoyue0307.github.io/awesome-loop-engineering/assets/social-preview-601-v09.png">
+    <meta name="twitter:image:alt" content="Awesome Loop Engineering: design loops, not just prompts.">
+    <link rel="icon" href="assets/favicon-32.png" type="image/png" sizes="32x32">
+    <style>
+      :root { color-scheme: light; font-family: Inter, ui-sans-serif, system-ui, sans-serif; }
+      body { display: grid; min-height: 100vh; margin: 0; place-items: center; background: #f7f9fc; color: #111827; }
+      main { width: min(88vw, 420px); text-align: center; }
+      img { width: 72px; height: 72px; }
+      p { color: #526176; line-height: 1.6; }
+      a { color: #1d4ed8; font-weight: 700; }
+    </style>
+    <script>
+      window.location.replace("https://chaoyue0307.github.io/awesome-loop-engineering/?utm_source=x&utm_medium=social&utm_campaign=v0.9.0");
+    </script>
+  </head>
+  <body>
+    <main>
+      <img src="assets/awesome-loop-engineering-logo.png" alt="Awesome Loop Engineering">
+      <h1>Awesome Loop Engineering</h1>
+      <p>Opening the interactive Resource Atlas.</p>
+      <p><a href="https://chaoyue0307.github.io/awesome-loop-engineering/?utm_source=x&amp;utm_medium=social&amp;utm_campaign=v0.9.0">Continue to the site</a></p>
+    </main>
+  </body>
+</html>

meta/DISTRIBUTION.md

@@ -4,12 +4,12 @@ Use this checklist after major updates or releases. Optimize for qualified disco
 
 ## Current Launch Status
 
-Status as of 2026-07-18:
+Status as of 2026-07-20:
 
 | Surface | Status | Evidence / next action |
 | --- | --- | --- |
-| GitHub release | Complete | [`v0.8.0`](https://github.com/ChaoYue0307/awesome-loop-engineering/releases/tag/v0.8.0) packages 579 resources, 20 patterns and contracts, 8 runtime starters, and explicit model-to-operations scope facets. |
-| Release announcement | Ready | Publish the v0.8.0 summary from [`posts/launch.md`](../posts/launch.md); [Discussion #9](https://github.com/ChaoYue0307/awesome-loop-engineering/discussions/9) is the low-noise release and contributor feed. |
+| GitHub release | Complete | [`v0.9.0`](https://github.com/ChaoYue0307/awesome-loop-engineering/releases/tag/v0.9.0) packages 601 resources, 20 patterns and contracts, 8 runtime starters, and explicit model-to-operations scope facets. |
+| Release announcement | Ready | Publish the v0.9.0 summary from [`posts/launch.md`](../posts/launch.md); [Discussion #9](https://github.com/ChaoYue0307/awesome-loop-engineering/discussions/9) is the low-noise release and contributor feed. |
 | GitHub Explore topic | Complete | [`github/explore#5209`](https://github.com/github/explore/pull/5209) merged the `loop-engineering` topic page. |
 | Canonical Awesome directory | Policy objection | [`sindresorhus/awesome#4339`](https://github.com/sindresorhus/awesome/pull/4339) is open and lint passes, but the owner stated that AI-generated lists are not accepted. Do not treat further visual polish as a path around that policy or misrepresent automated assistance. |
 | Adjacent harness directory | In review | [`ai-boost/awesome-harness-engineering#131`](https://github.com/ai-boost/awesome-harness-engineering/pull/131) transparently proposes the project for the list's Related Awesome Lists section. |
@@ -33,7 +33,7 @@ The Hugging Face dataset [`datasets/cy0307/awesome-loop-engineering`](https://hu
 
 - The mirror tracks the full GitHub tree (docs, patterns, examples, schema, scripts).
 - The dataset mirror includes generated resource sheets at `data/resources.csv` and `data/resources.jsonl`, refreshed from the full English `README.md` by `scripts/export_resource_dataset.py`.
-- The HF copy of `README.md` is a focused dataset card generated from `meta/hf_card_header.yaml` and `meta/hf_card_body.md` by `scripts/build_hf_card.py`. It documents intended uses, evidence fields, limitations, loading examples, and the latest link status without duplicating the 579-row GitHub README.
+- The HF copy of `README.md` is a focused dataset card generated from `meta/hf_card_header.yaml` and `meta/hf_card_body.md` by `scripts/build_hf_card.py`. It documents intended uses, evidence fields, limitations, loading examples, and the latest link status without duplicating the 601-row GitHub README.
 - The YAML header is **HF-only**: it must never be added to the GitHub `README.md`, because the metadata list items break `awesome-lint`.
 - Sync uses `python3 scripts/build_hf_card.py --output <staging>/README.md` followed by `hf upload --type dataset` against a staging copy. The token lives in the local Hugging Face cache; no token is committed.
 
@@ -50,7 +50,7 @@ Use GitHub-native discovery in this order.
   ```
 
 - **Pin the repository** on your profile, and add it to your profile README (profile-level promotion).
-- **About description.** Keep this concise value statement aligned with the README and project site: `🔁 Build reliable recurring AI-agent systems: 579 resources, 20 operational patterns, 20 loop contracts, 8 runtime starters, an interactive atlas, and a structured dataset.`
+- **About description.** Keep this concise value statement aligned with the README and project site: `🔁 Build reliable recurring AI-agent systems: 601 resources, 20 operational patterns, 20 loop contracts, 8 runtime starters, an interactive atlas, and a structured dataset.`
 - **Custom social preview** (repo Settings, Social preview) so shared links lead with the same four durable proof points. This is UI-only; it cannot be set through the API.
 - **Releases and Discussions** provide a lower-noise subscription path. Recommend `Watch -> Custom -> Releases and Discussions`; do not ask readers to watch every repository event.
 
@@ -95,7 +95,7 @@ Use the [English launch article](../posts/launch.md) or [Chinese launch article]
 ```text
 🔁 Awesome Loop Engineering maps how recurring AI agents should act, verify, remember, retry, and stop.
 
-579 resources · 20 patterns · 20 contracts · 8 starters
+601 resources · 20 patterns · 20 contracts · 8 starters
 
 Explore and build:
 https://github.com/ChaoYue0307/awesome-loop-engineering
@@ -105,12 +105,14 @@ https://github.com/ChaoYue0307/awesome-loop-engineering
 
 Attach [`assets/social-preview.png`](../assets/social-preview.png) directly when the platform permits it. The post is 264 characters before platform-specific URL shortening.
 
+For a fresh link card on X or LinkedIn, share <https://chaoyue0307.github.io/awesome-loop-engineering/x-v09-601.html>. The versioned URL points to the current preview image, records the `v0.9.0` campaign, and redirects readers to the interactive site.
+
 ### Chinese Short Post
 
 ```text
 🔁 Awesome Loop Engineering 把反复运行的 AI 智能体变成可设计、可验证、可停止的工程系统。
 
-579 个资源 · 20 个运行模式 · 20 份循环契约 · 8 个启动模板
+601 个资源 · 20 个运行模式 · 20 份循环契约 · 8 个启动模板
 
 浏览并开始构建：
 https://github.com/ChaoYue0307/awesome-loop-engineering
@@ -121,14 +123,14 @@ https://github.com/ChaoYue0307/awesome-loop-engineering
 ### Longer Post
 
 ```text
-Awesome Loop Engineering v0.8.0 is live.
+Awesome Loop Engineering v0.9.0 is live.
 
-"Loop" can mean recurrent computation inside a model, reasoning and tools inside one task, or verified work repeated across time. v0.8.0 maps all three without confusing their guarantees.
+"Loop" can mean recurrent computation inside a model, reasoning and tools inside one task, or verified work repeated across time. v0.9.0 maps all three without confusing their guarantees.
 
 The release includes:
 
-- 579 papers, docs, tools, benchmarks, and guides
-- 23 selected model-recurrence papers labeled as adjacent foundations
+- 601 papers, docs, tools, benchmarks, and guides
+- 29 model-layer resources, including Loopie, labeled as adjacent foundations
 - an interactive Resource Atlas
 - 20 operational patterns and 20 adaptable loop contracts
 - 8 runtime starters: 3 executables and 5 copy/paste templates
@@ -144,11 +146,11 @@ https://github.com/ChaoYue0307/awesome-loop-engineering
 Use a regular submission, not Show HN.
 
 ```text
-Title: Awesome Loop Engineering: 579 resources from looped models to agent operations
+Title: Awesome Loop Engineering: 601 resources from looped models to agent operations
 
 I have been mapping recurring AI-agent systems: how work enters, agents act, evidence gates results, state survives, and the system retries, escalates, or exits.
 
-The repository connects 579 papers, docs, tools, benchmarks, and guides across model, agent, harness, workflow, operations, and evaluation layers with 20 operational patterns, 20 adaptable contracts, 8 runtime starters, an interactive Resource Atlas, and a structured Hugging Face dataset. Model-level recurrence is labeled as an adjacent foundation rather than a complete operational loop. Corrections to source links, taxonomy, and summaries are especially welcome.
+The repository connects 601 papers, docs, tools, benchmarks, and guides across model, agent, harness, workflow, operations, and evaluation layers with 20 operational patterns, 20 adaptable contracts, 8 runtime starters, an interactive Resource Atlas, and a structured Hugging Face dataset. Model-level recurrence is labeled as an adjacent foundation rather than a complete operational loop. Corrections to source links, taxonomy, and summaries are especially welcome.
 
 https://github.com/ChaoYue0307/awesome-loop-engineering
 ```

meta/OUTREACH.md

@@ -12,7 +12,7 @@ Use these messages to request sources, operating examples, and accuracy correcti
 ## Short Message
 
 ```text
-Hi <name>, Awesome Loop Engineering connects 579 papers, docs, tools, benchmarks, and guides with reusable patterns for recurring AI-agent systems.
+Hi <name>, Awesome Loop Engineering connects 601 papers, docs, tools, benchmarks, and guides with reusable patterns for recurring AI-agent systems.
 
 If you have an original or official source, a correction, a real-world loop, or a cautionary example, you can contribute here:
 https://github.com/ChaoYue0307/awesome-loop-engineering
@@ -38,7 +38,7 @@ https://github.com/ChaoYue0307/awesome-loop-engineering/issues/new?template=anno
 ## Maintainer Request
 
 ```text
-Hi <name>, Awesome Loop Engineering helps researchers and builders move from 579 papers, docs, tools, benchmarks, and guides to 20 operational patterns, 20 adaptable contracts, 8 runtime starters, and a structured dataset.
+Hi <name>, Awesome Loop Engineering helps researchers and builders move from 601 papers, docs, tools, benchmarks, and guides to 20 operational patterns, 20 adaptable contracts, 8 runtime starters, and a structured dataset.
 
 Would it fit the related-resources policy for <project or newsletter>? I can provide a shorter description or submit through the normal contribution process:
 https://github.com/ChaoYue0307/awesome-loop-engineering

meta/social-preview.html

@@ -79,7 +79,7 @@
         <p>Model / agent / operations</p>
       </div>
       <div class="stats">
-        <div class="stat"><b>579</b><span>Papers, docs, tools + guides</span></div>
+        <div class="stat"><b>601</b><span>Papers, docs, tools + guides</span></div>
         <div class="stat"><b>20</b><span>Operational patterns</span></div>
         <div class="stat"><b>20</b><span>Schema-checked contracts</span></div>
         <div class="stat"><b>8</b><span>Runtime starters</span></div>

posts/launch.md

@@ -1,13 +1,13 @@
-# Awesome Loop Engineering v0.8.0
+# Awesome Loop Engineering v0.9.0
 
-Awesome Loop Engineering v0.8.0 maps recurrence from a model's inner computation to an agent's tools and the outer operating loop that verifies, remembers, retries, escalates, and stops.
+Awesome Loop Engineering v0.9.0 maps recurrence from a model's inner computation to an agent's tools and the outer operating loop that verifies, remembers, retries, escalates, and stops.
 
-Explore 579 resources without collapsing looped models, agent execution, harnesses, workflows, and production operations into one idea.
+Explore 601 resources without collapsing looped models, agent execution, harnesses, workflows, and production operations into one idea.
 
 ## What You Can Use
 
-- 579 papers, official docs, tools, benchmarks, and guides linked to the original work
-- 23 selected model-recurrence papers, from Universal Transformers and Huginn to LoopCoder and LoopWM, labeled as adjacent foundations rather than complete agent loops
+- 601 papers, official docs, tools, benchmarks, and guides linked to the original work
+- 29 model-layer resources, from Universal Transformers and Huginn to Loopie, LoopCoder, and LoopWM, labeled as adjacent foundations rather than complete agent loops
 - 20 operational patterns organized by build, operate, optimize, and govern use cases
 - 20 schema-checked loop contracts, one for every pattern
 - 8 runtime starters: 3 dependency-light executables and 5 copy/paste runtime templates
@@ -15,20 +15,21 @@ Explore 579 resources without collapsing looped models, agent execution, harness
 - 50-field CSV, JSONL, and Parquet exports mirrored as a Hugging Face dataset
 - 8 language entry points
 
-## What Changed Since v0.7.0
+## What Changed In v0.9.0
 
-- Added a bounded Model-Level Recurrence section with 23 high-signal papers and the deeper Awesome Loop Models catalog.
+- Added Loopie and 21 more works across model recurrence, agent workflows, verification, security, memory, orchestration, evaluation, and operations.
+- Expanded Model-Level Recurrence to 29 resources, including sparse MoE recurrence, fixed-point halting, and mechanistic evidence, with the deeper Awesome Loop Models catalog for further discovery.
 - Added `loop_layer` and `scope_fit` to every dataset row so model recurrence remains discoverable without being misclassified as operational Loop Engineering.
 - Added a model-to-operations map and loop-layer filter to the Resource Atlas.
 - Added a cross-layer research protocol for comparing internal recurrent depth with external evidence-aware retries under matched compute and cost.
-- Checked all 579 source links and refreshed every arXiv publication record so verified conference or journal publications take precedence over preprints.
+- Checked all 601 source links and refreshed every arXiv publication record so verified conference or journal publications take precedence over preprints.
 - Updated the website, social preview, translations, release metadata, and Hugging Face dataset to the same counts and terminology.
 
 ## Why This Matters
 
 "Loop" is overloaded. A learned block can recur inside one inference, an agent can alternate reasoning and tools inside one task, and an operating system can rerun verified work across time. Each layer matters, but each carries different state, stopping rules, evidence, and risks.
 
-The v0.8.0 map keeps those layers connected and comparable:
+The v0.9.0 map keeps those layers connected and comparable:
 
 1. Study model recurrence when the question is adaptive depth or latent computation.
 1. Study agent and harness resources when the question is reasoning, tools, context, or verification within a task.

posts/launch.zh-CN.md

@@ -1,13 +1,13 @@
-# Awesome Loop Engineering v0.8.0
+# Awesome Loop Engineering v0.9.0
 
-Awesome Loop Engineering v0.8.0 建立了一张从模型内部递归、Agent 执行、Harness 到外层运行系统的完整地图。
+Awesome Loop Engineering v0.9.0 建立了一张从模型内部递归、Agent 执行、Harness 到外层运行系统的完整地图。
 
-可浏览 579 篇论文、官方文档、工具、benchmark 与实践指南，同时保持清晰边界：模型内的循环提供自适应计算，但不等于具备外部验证、持久状态、预算、人类交接与退出规则的运行级 Loop。
+可浏览 601 篇论文、官方文档、工具、benchmark 与实践指南，同时保持清晰边界：模型内的循环提供自适应计算，但不等于具备外部验证、持久状态、预算、人类交接与退出规则的运行级 Loop。
 
 ## 可直接使用
 
-- 579 篇论文、官方文档、工具、benchmark 与实践指南，并直接链接原始来源
-- 23 篇精选模型递归论文，覆盖 Universal Transformers、Huginn、LoopCoder、LoopWM 等方向
+- 601 篇论文、官方文档、工具、benchmark 与实践指南，并直接链接原始来源
+- 29 个模型层资源，覆盖 Universal Transformers、Huginn、Loopie、LoopCoder、LoopWM 等方向
 - 20 个按 build、operate、optimize、govern 组织的 operational patterns
 - 20 个经过 schema 校验的 loop contracts，每个 pattern 对应一个
 - 8 个 runtime starters：3 个轻量级可执行程序与 5 个可复制改造的 runtime 模板
@@ -15,20 +15,21 @@ Awesome Loop Engineering v0.8.0 建立了一张从模型内部递归、Agent 执
 - 包含 50 个字段的 CSV、JSONL 与 Parquet 数据，并同步到 Hugging Face Dataset
 - 8 个语言入口
 
-## 相比 v0.7.0 的变化
+## v0.9.0 的更新
 
-- 新增 Model-Level Recurrence 专区，并接入 Awesome Loop Models 作为更深的架构索引。
+- 新增 Loopie，并补充 21 项相关工作，覆盖模型递归、Agent 工作流、验证、安全、记忆、编排、评测与运行。
+- 将 Model-Level Recurrence 扩展到 29 个资源，纳入稀疏 MoE 递归、fixed-point stopping 与机理分析，并接入 Awesome Loop Models 作为更深的架构索引。
 - 为每条数据新增 `loop_layer` 与 `scope_fit`，避免将模型递归误标为完整的运行级 Loop Engineering。
 - 在 Resource Atlas 中新增模型到运维的层级图与 Loop 层级筛选器。
 - 新增跨层评测方案，用匹配算力和成本的实验比较模型内部递归与外部证据驱动重试。
-- 检查全部 579 个来源链接，并刷新所有 arXiv 论文的会议或期刊发表状态。
+- 检查全部 601 个来源链接，并刷新所有 arXiv 论文的会议或期刊发表状态。
 - 同步更新网站、social preview、翻译、release metadata 与 Hugging Face Dataset。
 
 ## 为什么值得关注
 
 “Loop”可以发生在不同层级：模型内部可以重复隐状态计算，Agent 可以在单个任务中交替调用推理与工具，运行系统则可以跨时间重复经验证的工作。它们彼此相关，但状态、停止规则、证据与风险并不相同。
 
-v0.8.0 将这些层级连接起来：
+v0.9.0 将这些层级连接起来：
 
 1. 当问题是自适应深度与隐式计算时，研究模型递归。
 1. 当问题是任务内的推理、工具、上下文与验证时，研究 Agent 与 Harness。