Update full survey content with YAML frontmatter (images via GitHub raw URLs)

README.md

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   - n<1K
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-<!-- HuggingFace metadata above, GitHub ignores YAML frontmatter -->
-
 <div align="center">
 
 [English](README.md) | [中文](README_zh.md)
@@ -34,7 +32,7 @@ size_categories:
 </div>
 
 <p align="center">
-  <img src="assets/architecture_diagram.png" width="720" alt="H=(E,T,C,S,L,V) Six-Component Architecture"/>
+  <img src="https://raw.githubusercontent.com/Gloriaameng/LLM-Agent-Harness-Survey/main/assets/architecture_diagram.png" width="720" alt="H=(E,T,C,S,L,V) Six-Component Architecture"/>
 </p>
 
 > ⭐ **This survey is actively maintained. If you find it useful, please star the repo to stay updated and help others find it.**
@@ -116,7 +114,7 @@ We introduce a formal definition of the **agent execution harness** as a six-com
 - 💬 *"The harness is the chassis; the model is the engine."* — practitioner consensus, 2026
 
 <p align="center">
-  <img src="assets/root_cause_diagram.png" width="640" alt="Root Cause Analysis"/>
+  <img src="https://raw.githubusercontent.com/Gloriaameng/LLM-Agent-Harness-Survey/main/assets/root_cause_diagram.png" width="640" alt="Root Cause Analysis"/>
 </p>
 
 ### What This Survey Accomplishes
@@ -138,7 +136,7 @@ We introduce a formal definition of the **agent execution harness** as a six-com
 ## Historical Timeline
 
 <p align="center">
-  <img src="assets/timeline.png" width="720" alt="Historical Evolution of Agent Harnesses"/>
+  <img src="https://raw.githubusercontent.com/Gloriaameng/LLM-Agent-Harness-Survey/main/assets/timeline.png" width="720" alt="Historical Evolution of Agent Harnesses"/>
 </p>
 
 | Year | Milestone | Significance |
@@ -470,7 +468,7 @@ We introduce a formal definition of the **agent execution harness** as a six-com
 > **Key numbers:** AgencyBench — agents achieve **48.4% success on native SDK harness** vs substantially lower on independent harnesses, demonstrating tight harness-agent coupling. Byzantine fault tolerance remains an open problem for adversarial multi-agent settings.
 
 <p align="center">
-  <img src="assets/multi_agent_topology.png" width="680" alt="Multi-Agent Coordination Topologies"/>
+  <img src="https://raw.githubusercontent.com/Gloriaameng/LLM-Agent-Harness-Survey/main/assets/multi_agent_topology.png" width="680" alt="Multi-Agent Coordination Topologies"/>
 </p>
 
 - <u>SAGA</u>†: **"SAGA: A Security Architecture for Governing AI Agentic Systems"**. *Syros et al.* NDSS 2026. [[Paper](https://arxiv.org/abs/2504.21034)]

README_zh.md

@@ -17,8 +17,6 @@ size_categories:
   - n<1K
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-<!-- HuggingFace 元数据在上方，GitHub 会忽略 YAML frontmatter -->
-
 <div align="center">
 
 [English](README.md) | [中文](README_zh.md)
@@ -34,7 +32,7 @@ size_categories:
 </div>
 
 <p align="center">
-  <img src="assets/architecture_diagram.png" width="720" alt="H=(E,T,C,S,L,V) 六组件架构"/>
+  <img src="https://raw.githubusercontent.com/Gloriaameng/LLM-Agent-Harness-Survey/main/assets/architecture_diagram.png" width="720" alt="H=(E,T,C,S,L,V) 六组件架构"/>
 </p>
 
 > ⭐ **本 survey 持续更新中。如果对你有帮助，欢迎 Star 关注最新进展，也帮助更多人发现它。**
@@ -44,7 +42,7 @@ size_categories:
 > **智能体约束工程框架——而非模型本身——是智能体大规模部署可靠性的首要决定因素。**  
 > 本综述将约束工程框架形式化为一级架构对象 **H = (E, T, C, S, L, V)**，系统调研了涵盖23个系统的110余篇论文、博客和报告，并总结了9项开放性技术挑战。  
 > 📄 **[阅读论文](#)** （即将发布）  
-> ✉️ 勘误与建议：gloriamenng@gmail.com (孟倩宇); wangyanan@mail.dlut.edu.cn (王亚楠); chenliyi@xiaohongshu.com (陈力毅)
+> ✉️ 勘误与建议：gloriamenng@gmail.com; wangyanan@mail.dlut.edu.cn; chenliyi@xiaohongshu.com
 
 如果本综述对您有所帮助，请引用：
 
@@ -55,7 +53,7 @@ size_categories:
              Lu, Chengqiang and Wu, Wei and Gao, Yan and Wu, Yi and Hu, Yao},
   year    = {2026},
   url     = {https://github.com/Gloriaameng/LLM-Agent-Harness-Survey},
-  note    = {*共同第一作者 (Equal contribution). Work in progress}
+  note    = {* Equal contribution. Work in progress}
 }
 ```
 
@@ -72,10 +70,10 @@ size_categories:
 
 - [概述](#概述)
 - [历史时间线](#历史时间线)
-- [约束工程框架完备性矩阵](#执行器完备性矩阵)
+- [约束工程框架完备性矩阵](#约束工程框架完备性矩阵)
 - [论文列表](#论文列表)
   - [历史渊源](#历史渊源)
-  - [约束工程框架分类](#执行器分类)
+  - [约束工程框架分类](#约束工程框架分类)
   - [技术挑战](#技术挑战)
     - [安全与沙箱](#安全与沙箱)
     - [评估与基准测试](#评估与基准测试)
@@ -116,7 +114,7 @@ size_categories:
 - 💬 *"约束工程是底盘；模型是引擎。"* ——2026年业界共识
 
 <p align="center">
-  <img src="assets/root_cause_diagram.png" width="640" alt="根因分析"/>
+  <img src="https://raw.githubusercontent.com/Gloriaameng/LLM-Agent-Harness-Survey/main/assets/root_cause_diagram.png" width="640" alt="根因分析"/>
 </p>
 
 ### 本综述的学术贡献
@@ -138,7 +136,7 @@ size_categories:
 ## 历史时间线
 
 <p align="center">
-  <img src="assets/timeline.png" width="720" alt="智能体约束工程的历史演进"/>
+  <img src="https://raw.githubusercontent.com/Gloriaameng/LLM-Agent-Harness-Survey/main/assets/timeline.png" width="720" alt="智能体约束工程的历史演进"/>
 </p>
 
 | 年份 | 里程碑 | 意义 |
@@ -370,80 +368,150 @@ size_categories:
 
 > **关键数据：**SandboxEscapeBench——前沿LLM达到**15–35%容器逃逸率**；PRISM——10个钩子的零分叉运行时将逃逸降至接近零，开销<5ms。
 
-*（安全与沙箱相关论文列表与英文版相同，此处省略重复）*
+- <u>SandboxEscapeBench</u>†: **"Quantifying Frontier LLM Capabilities for Container Sandbox Escape"**. *Marchand et al.* arXiv 2026. [[Paper](https://arxiv.org/abs/2603.02277)]
+- <u>InjecAgent</u>: **"InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents"**. *Zhan et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2403.02691)]
+- <u>ToolHijacker</u>†: **"Prompt Injection Attack to Tool Selection in LLM Agents"**. *Shi et al.* NDSS 2026. [[Paper](https://arxiv.org/abs/2504.19793)]
+- <u>Securing MCP</u>†: **"Securing the Model Context Protocol (MCP): Risks, Controls, and Governance"**. *Errico et al.* arXiv 2025. [[Paper](https://arxiv.org/abs/2511.20920)]
+- <u>SHIELDA</u>†: **"SHIELDA: Structured Handling of Exceptions in LLM-Driven Agentic Workflows"**. *Zhou et al.* arXiv 2025. [[Paper](https://arxiv.org/abs/2508.07935)]
+- <u>PALADIN</u>†: **"PALADIN: Self-Correcting Language Model Agents to Cure Tool-Failure Cases"**. *Vuddanti et al.* ICLR 2026. [[Paper](https://arxiv.org/abs/2509.25238)]
+- <u>AgentBound</u>†: **"Securing AI Agent Execution"**. *Bühler et al.* arXiv 2025. [[Paper](https://arxiv.org/abs/2510.21236)]
+- <u>AgentSys</u>†: **"AgentSys: Secure and Dynamic LLM Agents Through Explicit Hierarchical Memory Management"**. *Wen et al.* arXiv 2026. [[Paper](https://arxiv.org/abs/2602.07398)]
+- <u>Indirect Prompt Injection</u>: **"Not What You've Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection"**. *Greshake et al.* AISec 2023. [[Paper](https://arxiv.org/abs/2302.12173)]
+- <u>AgentHarm</u>†: **"AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents"**. *Andriushchenko et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2410.09024)]
+- <u>TrustAgent</u>: **"TrustAgent: Towards Safe and Trustworthy LLM-Based Agents"**. *Hua et al.* EMNLP 2024. [[Paper](https://arxiv.org/abs/2402.01586)]
+- <u>ToolEmu</u>†: **"Identifying the Risks of LM Agents with an LM-Emulated Sandbox"**. *Ruan et al.* arXiv 2023. [[Paper](https://arxiv.org/abs/2309.15817)]
+- <u>Ignore Previous Prompt</u>: **"Ignore Previous Prompt: Attack Techniques For Language Models"**. *Perez & Ribeiro.* NeurIPS ML Safety Workshop 2022. [[Paper](https://arxiv.org/abs/2211.09527)]
 
 #### 评估与基准测试
 
 > **关键数据：**HAL 统一了**20,000+次评估**，将数周的评估压缩至数小时；OSWorld 报告了**28%假阴性率**的自动化评估；METR 发现通过基准测试的PR人工合并率**低24.2个百分点**，差距以9.6pp/年速度扩大。
 
-*（评估与基准测试相关论文列表与英文版相同，此处省略重复）*
+- <u>AgencyBench</u>†: **"AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World Contexts"**. *Li et al.* arXiv 2026. [[Paper](https://arxiv.org/abs/2601.11044)]
+- <u>AEGIS</u>†: **"AEGIS: No Tool Call Left Unchecked -- A Pre-Execution Firewall and Audit Layer for AI Agents"**. *Yuan et al.* arXiv 2026. [[Paper](https://arxiv.org/abs/2603.12621)]
+- <u>Hell or High Water</u>†: **"Hell or High Water: Evaluating Agentic Recovery from External Failures"**. *Wang et al.* COLM 2025. [[Paper](https://arxiv.org/abs/2508.11027)]
+- <u>SearchLLM</u>†: **"Aligning Large Language Models with Searcher Preferences"**. *Wu et al.* arXiv 2026. [[Paper](https://arxiv.org/abs/2603.10473)]
+- <u>Meta-Harness</u>†: **"Meta-Harness: End-to-End Optimization of Model Harnesses"**. *Lee et al.* arXiv 2026. [[Paper](https://arxiv.org/abs/2603.28052)]
+- <u>TheAgentCompany</u>†: **"TheAgentCompany: Benchmarking LLM Agents on Consequential Real-World Tasks"**. *Xu et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2412.14161)]
+- <u>BrowserGym</u>†: **"The BrowserGym Ecosystem for Web Agent Research"**. *Le Sellier De Chezelles et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2412.05467)]
+- <u>WorkArena</u>†: **"WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?"**. *Drouin et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2403.07718)]
+- <u>R-Judge</u>: **"R-Judge: Benchmarking Safety Risk Awareness for LLM Agents"**. *Yuan et al.* EMNLP 2024. [[Paper](https://arxiv.org/abs/2401.10019)]
+- <u>R2E</u>: **"R2E: Turning any GitHub Repository into a Programming Agent Environment"**. *Jain et al.* ICML 2024. [[Paper](https://proceedings.mlr.press/v235/jain24c.html)]
+- <u>Evaluation Survey</u>: **"Evaluation and Benchmarking of LLM Agents: A Survey"**. *Mohammadi et al.* KDD 2025. [[Paper](https://arxiv.org/abs/2507.21504)]
+- <u>PentestJudge</u>†: **"PentestJudge: Judging Agent Behavior Against Operational Requirements"**. *Caldwell et al.* arXiv 2025. [[Paper](https://arxiv.org/abs/2508.02921)]
 
 #### 协议标准化
 
 > **关键数据：**MCP（工具↔约束工程）：2–15ms延迟；A2A（智能体↔智能体）：50–200ms；ACP（意图级，IBM）——三种协议服务于互补角色。
 
-*（协议标准化相关论文列表与英文版相同，此处省略重复）*
+- <u>MCP</u>: **"Model Context Protocol"**. *Anthropic.* Technical Report 2024. [[Spec](https://modelcontextprotocol.io)]
+- <u>A2A</u>: **"Agent-to-Agent Protocol"**. *Google.* Technical Report 2025. [[Spec](https://google.github.io/A2A/)]
+- <u>Protocol Comparison</u>†: **"A Survey of Agent Interoperability Protocols: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP)"**. *Ehtesham et al.* arXiv 2025. [[Paper](https://arxiv.org/abs/2505.02279)]
+- <u>Gorilla</u>: **"Gorilla: Large Language Model Connected with Massive APIs"**. *Patil et al.* NeurIPS 2023. [[Paper](https://arxiv.org/abs/2305.15334)] [[Code](https://github.com/ShishirPatil/gorilla)]
 
 #### 运行时上下文管理
 
 > **关键数据：**SkillsBench——精选技能注入产生**+16.2pp**改进；"迷失在中间"效应已被记录；长上下文模型将问题从*保留*转移至*显著性*。
 
-*（运行时上下文管理相关论文列表与英文版相同，此处省略重复）*
+- <u>SkillsBench</u>†: **"SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks"**. *Li et al.* arXiv 2026. [[Paper](https://arxiv.org/abs/2602.12670)]
+- <u>ReadAgent</u>: **"A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts"**. *Lee et al.* ICML 2024. [[Paper](https://arxiv.org/abs/2402.09727)]
+- <u>MemoryOS</u>: **"Memory OS of AI Agent"**. *Kang et al.* arXiv 2025. [[Paper](https://arxiv.org/abs/2506.06326)]
+- <u>CoALA</u>: **"Cognitive Architectures for Language Agents"**. *Sumers et al.* TMLR 2024. [[Paper](https://arxiv.org/abs/2309.02427)]
+- <u>SkillFortify</u>†: **"Formal Analysis and Supply Chain Security for Agentic AI Skills"**. *Bhardwaj.* arXiv 2026. [[Paper](https://arxiv.org/abs/2603.00195)]
+- <u>Lost in the Middle</u>: **"Lost in the Middle: How Language Models Use Long Contexts"**. *Liu et al.* TACL 2024. [[Paper](https://arxiv.org/abs/2307.03172)]
+- <u>Context Engineering Survey</u>†: **"Context Engineering: A Survey of 1,400 Papers on Effective Context Management for LLM Agents"**. *Mei et al.* arXiv 2025. [[Paper](https://arxiv.org/abs/2507.13334)]
 
 #### 工具使用与注册
 
 > **关键数据：**Vercel 发现移除**80%工具**比任何模型升级都更有帮助；Schema First (Sigdel & Baral, 2026) —— 一项受控的初步研究表明，基于 schema 的工具契约可以减少**接口层面**的误用（如格式校验失败），但无法减少**语义层面**的误用（即格式正确但任务不当的调用），且各实验条件下端到端任务成功率均为零，表明仅靠接口设计不足以保证工具调用的可靠性；CodeAct 在**17/17项Mint基准**上表现优异，**回合数减少20%**。
 
-*（工具使用与注册相关论文列表与英文版相同，此处省略重复）*
+- <u>CodeAct</u>: **"Executable Code Actions Elicit Better LLM Agents"**. *Wang et al.* ICML 2024. [[Paper](https://arxiv.org/abs/2402.01030)] [[Code](https://github.com/xingyaoww/code-act)]
+- <u>Schema First</u>†: **"Schema First Tool APIs for LLM Agents: A Controlled Study of Tool Misuse, Recovery, and Budgeted Performance"**. *Sigdel & Baral.* arXiv 2026. [[Paper](https://arxiv.org/abs/2603.13404)]
+- <u>ToolLLM</u>: **"ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs"**. *Qin et al.* ICLR 2024. [[Paper](https://arxiv.org/abs/2307.16789)] [[Code](https://github.com/OpenBMB/ToolBench)]
+- <u>ToolSandbox</u>†: **"ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities"**. *Lu et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2408.04682)]
+- <u>AutoTool</u>†: **"AutoTool: Efficient Tool Selection for Large Language Model Agents"**. *Jia & Li.* AAAI 2026. [[Paper](https://arxiv.org/abs/2511.14650)]
+- <u>Tool Learning Survey</u>: **"Tool Learning with Large Language Models: A Survey"**. *Qu et al.* Frontiers of Computer Science 2024. [[Paper](https://arxiv.org/abs/2405.17935)]
+- <u>GoEX</u>†: **"GoEX: Perspectives and Designs Towards a Runtime for Autonomous LLM Applications"**. *Patil et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2404.06921)]
+- <u>AgentTuning</u>: **"AgentTuning: Enabling Generalized Agent Abilities for LLMs"**. *Zeng et al.* ACL 2024. [[Paper](https://arxiv.org/abs/2310.12823)]
 
 #### 记忆架构
 
 > **关键数据：**Mem0 实现了相比完整上下文**90% token减少**；Zep 时序知识：**+18.5% QA准确率**；Agent Workflow Memory：在Mind2Web上**+14.9%**。六种架构模式：平面缓冲区→层次化→情节式→语义式→过程式→图式。
 
-*（记忆架构相关论文列表与英文版相同，此处省略重复）*
+- <u>Agent Workflow Memory (AWM)</u>†: **"Agent Workflow Memory"**. *Wang et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2409.07429)]
+- <u>Mem0</u>†: **"Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory"**. *Khant et al.* arXiv 2025. [[Paper](https://arxiv.org/abs/2504.19413)]
+- <u>A-MEM</u>†: **"A-MEM: Agentic Memory for LLM Agents"**. *Xu et al.* NeurIPS 2025. [[Paper](https://arxiv.org/abs/2502.12110)]
+- <u>MemAct</u>†: **"Memory as Action: Autonomous Context Curation for Long-Horizon Agentic Tasks"**. *Zhang et al.* arXiv 2025. [[Paper](https://arxiv.org/abs/2510.12635)]
+- <u>Memory Survey</u>†: **"Memory for Autonomous LLM Agents: Mechanisms, Evaluation, and Emerging Frontiers"**. *Du.* arXiv 2026. [[Paper](https://arxiv.org/abs/2603.07670)]
+- <u>MemoryBank</u>: **"MemoryBank: Enhancing Large Language Models with Long-Term Memory"**. *Zhong et al.* AAAI 2024. [[Paper](https://arxiv.org/abs/2305.10250)]
+- <u>LoCoMo</u>†: **"Evaluating Very Long-Term Conversational Memory of LLM Agents"**. *Maharana et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2402.17753)]
+- <u>Memory Mechanisms Survey</u>†: **"A Survey on the Memory Mechanism of Large Language Model Based Agents"**. *Zhang et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2404.13501)]
+- <u>Evo-Memory</u>†: **"Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory"**. *Wei et al.* arXiv 2025. [[Paper](https://arxiv.org/abs/2511.20857)]
 
 #### 规划与推理
 
 > **关键数据：**SWE-agent ACI 研究表明，接口设计超越模型能力成为性能的首要决定因素。LATS 将MCTS与语言模型反馈集成用于状态空间搜索。Plan-on-Graph通过引导、记忆和反思机制在知识图谱上实现自适应自我修正规划。
 
-**代表性论文：**
-- <u>Tree of Thoughts</u>: **"Tree of Thoughts: Deliberate Problem Solving with Large Language Models"**. *Yao et al.* NeurIPS 2023. [[论文](https://arxiv.org/abs/2305.10601)] [[代码](https://github.com/princeton-nlp/tree-of-thought-llm)]
-- <u>LATS</u>: **"Language Agent Tree Search Unifies Reasoning, Acting, and Planning in Language Models"**. *Zhou et al.* arXiv 2023. [[论文](https://arxiv.org/abs/2310.04406)] [[代码](https://github.com/lapisrocks/LanguageAgentTreeSearch)]
-- <u>Plan-on-Graph</u>: **"Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs"**. *Chen et al.* NeurIPS 2024. [[论文](https://proceedings.neurips.cc/paper_files/paper/2024/hash/4254e856d01a5e7b7ea050477c3ef9b9-Abstract-Conference.html)]
+- <u>Tree of Thoughts</u>: **"Tree of Thoughts: Deliberate Problem Solving with Large Language Models"**. *Yao et al.* NeurIPS 2023. [[Paper](https://arxiv.org/abs/2305.10601)] [[Code](https://github.com/princeton-nlp/tree-of-thought-llm)]
+- <u>LATS</u>: **"Language Agent Tree Search Unifies Reasoning, Acting, and Planning in Language Models"**. *Zhou et al.* arXiv 2023. [[Paper](https://arxiv.org/abs/2310.04406)] [[Code](https://github.com/lapisrocks/LanguageAgentTreeSearch)]
+- <u>Plan-on-Graph</u>: **"Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs"**. *Chen et al.* NeurIPS 2024. [[Paper](https://proceedings.neurips.cc/paper_files/paper/2024/hash/4254e856d01a5e7b7ea050477c3ef9b9-Abstract-Conference.html)]
+- <u>AFlow</u>†: **"AFlow: Automating Agentic Workflow Generation"**. *Zhang et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2410.10762)]
+- <u>Agent Q</u>†: **"Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents"**. *Putta et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2408.07199)]
+- <u>OPENDEV</u>†: **"Building Effective AI Coding Agents for the Terminal: Scaffolding, Harness, Context Engineering, and Lessons Learned"**. *Bui.* arXiv 2026. [[Paper](https://arxiv.org/abs/2603.05344)]
+- <u>AOrchestra</u>†: **"AOrchestra: Automating Sub-Agent Creation for Agentic Orchestration"**. *Ruan et al.* arXiv 2026. [[Paper](https://arxiv.org/abs/2602.03786)]
+- <u>RAP</u>: **"Reasoning with Language Model is Planning with World Model"**. *Hao et al.* EMNLP 2023. [[Paper](https://arxiv.org/abs/2305.14992)]
+- <u>Inner Monologue</u>: **"Inner Monologue: Embodied Reasoning Through Planning with Language Models"**. *Huang et al.* CoRL 2022. [[Paper](https://arxiv.org/abs/2207.05608)]
+- <u>Agent-Oriented Planning</u>: **"Agent-Oriented Planning in Multi-Agent Systems"**. *Li et al.* ICLR 2025. [[Paper](https://arxiv.org/abs/2410.02189)]
+- <u>ExACT</u>†: **"ExACT: Teaching AI Agents to Explore with Reflective-MCTS and Exploratory Learning"**. *Yu et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2410.02052)]
 
-*（更多规划与推理相关论文见英文版）*
 
 #### 多智能体协同
 
 > **关键数据：**AgencyBench——智能体在原生SDK约束工程上达到**48.4%成功率**，而在独立约束工程框架上显著更低，展示了约束工程-智能体的紧密耦合。对抗性多智能体设置中的拜占庭容错仍是开放问题。
 
 <p align="center">
-  <img src="assets/multi_agent_topology.png" width="680" alt="多智能体协同拓扑"/>
+  <img src="https://raw.githubusercontent.com/Gloriaameng/LLM-Agent-Harness-Survey/main/assets/multi_agent_topology.png" width="680" alt="多智能体协同拓扑"/>
 </p>
 
-*（多智能体协同相关论文列表与英文版相同，此处省略重复）*
+- <u>SAGA</u>†: **"SAGA: A Security Architecture for Governing AI Agentic Systems"**. *Syros et al.* NDSS 2026. [[Paper](https://arxiv.org/abs/2504.21034)]
+- <u>MAS-FIRE</u>†: **"MAS-FIRE: Fault Injection and Reliability Evaluation for LLM-Based Multi-Agent Systems"**. *Jia et al.* arXiv 2026. [[Paper](https://arxiv.org/abs/2602.19843)]
+- <u>Byzantine fault tolerance</u>†: **"Rethinking the Reliability of Multi-agent System: A Perspective from Byzantine Fault Tolerance"**. *Zheng et al.* arXiv 2025. [[Paper](https://arxiv.org/abs/2511.10400)]
+- <u>Multi-agent baseline study</u>†: **"Rethinking the Value of Multi-Agent Workflow: A Strong Single Agent Baseline"**. *Xu et al.* arXiv 2026. [[Paper](https://arxiv.org/abs/2601.12307)]
+- <u>AgentVerse</u>†: **"AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors"**. *Chen et al.* arXiv 2023. [[Paper](https://arxiv.org/abs/2308.10848)]
+- <u>Mixture-of-Agents</u>†: **"Mixture-of-Agents Enhances Large Language Model Capabilities"**. *Wang et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2406.04692)]
+- <u>Multi-Agent Survey</u>: **"Large Language Model Based Multi-Agents: A Survey of Progress and Challenges"**. *Guo et al.* IJCAI 2024. [[Paper](https://arxiv.org/abs/2402.01680)]
+- <u>Concordia</u>†: **"Generative Agent-Based Modeling with Actions Grounded in Physical, Social, or Digital Space Using Concordia"**. *Vezhnevets et al.* arXiv 2023. [[Paper](https://arxiv.org/abs/2312.03664)]
 
 #### 计算经济学
 
 > **关键数据：**OpenRouter 报告**每周13T tokens**（2026年2月），每4周翻倍；AgencyBench 测量平均**1M tokens/任务**；预计2027年智能体计算量增长1000倍；AIOS 通过恰当的智能体调度实现**2.1×吞吐加速**。
 
-*（计算经济学相关论文列表与英文版相同，此处省略重复）*
+- <u>Repo2Run</u>†: **"Repo2Run: Automated Building Executable Environment for Code Repository at Scale"**. *Hu et al.* arXiv 2025. [[Paper](https://arxiv.org/abs/2502.13681)]
+- <u>Policy-First</u>†: **"Guardrails as Infrastructure: Policy-First Control for Tool-Orchestrated Workflows"**. *Sigdel & Baral.* arXiv 2026. [[Paper](https://arxiv.org/abs/2603.18059)]
 
 ---
 
 ### 新兴主题
 
-*（新兴主题论文列表与英文版相同，此处省略重复）*
+- <u>Self-Evolving Agents Survey</u>†: **"A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence"**. *Gao et al.* TMLR 2026. [[Paper](https://arxiv.org/abs/2507.21046)]
+- <u>Self-RAG</u>: **"Self-RAG: Learning to Retrieve, Generate, and Critique Through Self-Reflection"**. *Asai et al.* ICLR 2024. [[Paper](https://arxiv.org/abs/2310.11511)]
+- <u>Constitutional AI</u>: **"Constitutional AI: Harmlessness from AI Feedback"**. *Bai et al.* arXiv 2022. [[Paper](https://arxiv.org/abs/2212.08073)]
+- <u>AppAgent</u>†: **"AppAgent: Multimodal Agents as Smartphone Users"**. *Zhang et al.* arXiv 2023. [[Paper](https://arxiv.org/abs/2312.13771)]
 
 #### 相关综述
 
-*（相关综述论文列表与英文版相同，此处省略重复）*
+- <u>LLM Agents Survey</u>: **"A Survey on Large Language Model Based Autonomous Agents"**. *Wang et al.* arXiv 2023. [[Paper](https://arxiv.org/abs/2308.11432)]
+- <u>Rise of LLM Agents</u>: **"The Rise and Potential of Large Language Model Based Agents: A Survey"**. *Xi et al.* arXiv 2023. [[Paper](https://arxiv.org/abs/2309.07864)]
+- <u>LLM Survey</u>: **"A Survey of Large Language Models"**. *Zhao et al.* arXiv 2023. [[Paper](https://arxiv.org/abs/2303.18223)]
+- <u>AI Agent Systems</u>†: **"AI Agent Systems: Architectures, Applications, and Evaluation"**. *Xu.* arXiv 2025. [[Paper](https://arxiv.org/abs/2601.01743)]
 
 #### 业界实践报告与洞察
 
 > 来自 Stripe、OpenAI、Cursor、METR 及其他前沿实践者的生产部署经验。
 
-*（业界实践报告列表与英文版相同，此处省略重复）*
+- <u>Stripe Minions</u>: **"Minions: Stripe's one-shot, end-to-end coding agents"**. *Gray.* Stripe Dev Blog, Feb 2026. [[Blog](https://stripe.dev/blog/minions-stripes-one-shot-end-to-end-coding-agents)]
+- <u>Harness Engineering (OpenAI)</u>: **"Harness engineering: leveraging Codex in an agent-first world"**. *Lopopolo.* OpenAI Blog, Feb 2026. [[Blog](https://openai.com/index/harness-engineering/)]
+- <u>Self-Driving Codebases</u>: **"Towards self-driving codebases"**. *Lin.* Cursor Blog, Feb 2026. [[Blog](https://cursor.com/blog/self-driving-codebases)]
+- <u>METR SWE-bench Analysis</u>: **"Many SWE-bench-Passing PRs Would Not Be Merged into Main"**. *Whitfill et al.* METR Notes, Mar 2026. [[Report](https://metr.org/notes/2026-03-10-many-swe-bench-passing-prs-would-not-be-merged-into-main/)]
 
 ---
 
@@ -480,5 +548,5 @@ size_categories:
 <p align="center">
   <i>† 表示预印本，尚未经同行评审。</i><br>
   <i>本综述正在积极开发中；完整手稿将于近期发布。</i><br>
-  <i>由 Qianyu Meng 与 Liyi Chen 维护。欢迎提交缺失论文或更新链接的 PR。</i>
+  <i>由 Qianyu Meng, Yanan Wang 与 Liyi Chen 维护。欢迎提交缺失论文或更新链接的 PR。</i>
 </p>