key stringlengths 8 50 | title stringlengths 11 130 | venue stringlengths 1 111 | date stringlengths 4 7 | url stringlengths 28 159 | arxiv_id stringlengths 0 10 | code stringlengths 0 68 | citations int64 0 21.3k | github_stars int64 0 64k | collections listlengths 0 3 | sections listlengths 1 3 |
|---|---|---|---|---|---|---|---|---|---|---|
10778628 | JARVIS-1: Open-World Multi-Task Agents With Memory-Augmented Multimodal Language Models | IEEE Transactions on Pattern Analysis & Machine Intelligence | 2023-11 | https://doi.ieeecomputersociety.org/10.1109/TPAMI.2024.3511593 | 2311.05997 | https://github.com/CraftJarvis/JARVIS-1 | 206 | 410 | [
"harness-design"
] | [
"targets/harness"
] |
11334583 | LongCodeZip: Compress Long Context for Code Language Models | 2025 40th IEEE/ACM International Conference on Automated Software Engineering (ASE) | 2025-10 | https://doi.org/10.1109/ase63991.2025.00020 | 2510.00446 | https://github.com/YerbaPage/LongCodeZip | 42 | 164 | [
"harness-design"
] | [
"targets/harness"
] |
anokhin2025herobench | HeroBench: A Benchmark for Long-Horizon Planning and Structured Reasoning in Virtual Worlds | arXiv preprint arXiv:2508.12782 | 2025-08 | https://arxiv.org/abs/2508.12782 | 2508.12782 | https://github.com/stefanrer/HeroBench | 6 | 14 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
bfcl2025 | The Berkeley Function Calling Leaderboard (BFCL): From tool use to agentic evaluation of large language models | Proceedings of the 42nd International Conference on Machine Learning | 2025 | https://proceedings.mlr.press/v267/patil25a.html | https://github.com/ShishirPatil/gorilla | 435 | 13,007 | [
"benchmarks"
] | [
"evidence/benchmarks"
] | |
bonatti2025windows | Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale | International Conference on Machine Learning | 2024-09 | https://arxiv.org/abs/2409.08264 | 2409.08264 | https://github.com/microsoft/WindowsAgentArena | 194 | 889 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
buildbench2025 | BuildBench: Benchmarking LLM Agents on Compiling Real-World Open-Source Software | arXiv | 2025-09 | https://arxiv.org/abs/2509.25248 | 2509.25248 | 1 | 0 | [
"benchmarks"
] | [
"evidence/benchmarks"
] | |
cai2026mossselfevolutionsourcelevelrewriting | MOSS: Self-Evolution through Source-Level Rewriting in Autonomous Agent Systems | arXiv | 2026-05 | https://arxiv.org/abs/2605.22794 | 2605.22794 | https://github.com/hkgai-official/Moss | 5 | 21 | [
"harness-design"
] | [
"targets/harness"
] |
cemri2026multi | Why Do Multi-Agent LLM Systems Fail? | Advances in Neural Information Processing Systems | 2025-03 | https://proceedings.neurips.cc/paper_files/paper/2025/hash/b1041e52d3be19f0a9bc491657488e4a-Abstract-Datasets_and_Benchmarks_Track.html | 2503.13657 | https://github.com/multi-agent-systems-failure-taxonomy/MAST | 535 | 410 | [
"benchmarks",
"harness-design"
] | [
"evidence/benchmarks",
"targets/harness"
] |
chan2025mlebench | MLE-bench: Evaluating machine learning agents on machine learning engineering | ICLR 2025 | 2024-10 | https://arxiv.org/abs/2410.07095 | 2410.07095 | https://github.com/openai/mle-bench | 360 | 1,716 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
chen2024spin | SPIN: Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models | Proceedings of the 41st International Conference on Machine Learning | 2024 | https://proceedings.mlr.press/v235/chen24j.html | https://github.com/uclaml/SPIN | 621 | 1,254 | [] | [
"targets/weights"
] | |
chen2025iterresearch | IterResearch: Rethinking Long-Horizon Agents with Interaction Scaling | arXiv preprint arXiv:2511.07327 | 2025-11 | https://arxiv.org/abs/2511.07327 | 2511.07327 | https://github.com/Alibaba-NLP/DeepResearch | 17 | 19,873 | [
"model-design"
] | [
"targets/weights"
] |
chen2025loop | Reinforcement Learning for Long-Horizon Interactive LLM Agents | arXiv preprint arXiv:2502.01600 | 2025-02 | https://arxiv.org/abs/2502.01600 | 2502.01600 | 99 | 0 | [
"model-design"
] | [
"targets/weights"
] | |
chen2025mlrbench | MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning Research | arXiv preprint arXiv:2505.19955 | 2025-05 | https://arxiv.org/abs/2505.19955 | 2505.19955 | https://github.com/chchenhui/mlrbench | 49 | 34 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
chen2026agent | Agent^2 RL-Bench: Can LLM Agents Engineer Agentic RL Post-Training? | arXiv preprint arXiv:2604.10547 | 2026-04 | https://arxiv.org/abs/2604.10547 | 2604.10547 | https://github.com/microsoft/RD-Agent | 3 | 14,332 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
chen2026aiscientist | Toward autonomous long-horizon engineering for ML research | arXiv | 2026-04 | https://arxiv.org/abs/2604.13018 | 2604.13018 | https://github.com/AweAI-Team/AiScientist | 8 | 145 | [
"benchmarks",
"harness-design"
] | [
"targets/substrate"
] |
chen2026harnessxcomposableadaptiveevolvable | HarnessX: A Composable, Adaptive, and Evolvable Agent Harness Foundry | arXiv | 2026-06 | https://arxiv.org/abs/2606.14249 | 2606.14249 | 15 | 0 | [
"harness-design"
] | [
"targets/harness"
] | |
chen2026knowu | KnowU-Bench: Towards Interactive, Proactive, and Personalized Mobile Agent Evaluation | arXiv preprint arXiv:2604.08455 | 2026-04 | https://arxiv.org/abs/2604.08455 | 2604.08455 | https://github.com/ZJU-REAL/KnowU-Bench | 16 | 75 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
chen2026pastprologueplugincontroller | The Past Is Prologue: A Plug-in Controller for Selective Updates in Sequentially Evolving LLM Memory | arXiv | 2026-06 | https://arxiv.org/abs/2606.31121 | 2606.31121 | 1 | 0 | [
"harness-design"
] | [
"targets/harness"
] | |
chen2026recursiveselfimprovementaibounded | Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops | — | 2026-07 | https://arxiv.org/abs/2607.07663 | 2607.07663 | 6 | 0 | [
"harness-design"
] | [
"analyses"
] | |
chhikara2025mem0 | Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory | European Conference on Artificial Intelligence (ECAI) | 2025-04 | https://doi.org/10.3233/faia251160 | 2504.19413 | https://github.com/mem0ai/mem0 | 567 | 64,001 | [
"harness-design"
] | [
"targets/harness"
] |
choudhury2025processrewardmodelsllm | Process Reward Models for LLM Agents: Practical Framework and Directions | arXiv | 2025-02 | https://arxiv.org/abs/2502.10325 | 2502.10325 | https://github.com/sanjibanc/agent_prm | 79 | 59 | [
"harness-design"
] | [
"targets/harness"
] |
cirepairbench2026 | CI-Repair-Bench: A Repository-Aware Benchmark for Automated Patch Validation via CI Workflows | arXiv | 2026-04 | https://arxiv.org/abs/2604.27148 | 2604.27148 | https://github.com/RabeyaMuna/CI-REPAIR-BENCH | 0 | 1 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
clitoolbench2026 | Evaluating LLM-Based 0-to-1 Software Generation in End-to-End CLI Tool Scenarios | arXiv | 2026-04 | https://arxiv.org/abs/2604.06742 | 2604.06742 | https://github.com/kinesiatricssxilm14/CLI-Tool-Bench | 1 | 2 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
dataenvgym2025 | DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback | International Conference on Learning Representations | 2024-10 | https://arxiv.org/abs/2410.06215 | 2410.06215 | https://github.com/codezakh/DataEnvGym | 20 | 34 | [] | [
"targets/data"
] |
deepswe2026 | DeepSWE: Measuring Frontier Coding Agents on Original, Long-Horizon Engineering Tasks | arXiv preprint arXiv:2607.07946 | 2026-07 | https://arxiv.org/abs/2607.07946 | 2607.07946 | https://github.com/datacurve-ai/deep-swe | 13 | 1,488 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
deng2023mind2web | Mind2Web: Towards a Generalist Agent for the Web | Advances in Neural Information Processing Systems | 2023-06 | https://arxiv.org/abs/2306.06070 | 2306.06070 | https://github.com/OSU-NLP-Group/Mind2Web | 1,370 | 1,021 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
deng2026swemilestone | SWE-Milestone: Evaluating AI Agents on Continuous Software Evolution | International Conference on Machine Learning | 2026-03 | https://arxiv.org/abs/2603.13428 | 2603.13428 | https://github.com/DeepCommit-ai/SWE-Milestone | 6 | 70 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
dong2026longhorizon | Towards Long-Horizon Agents: A Survey | Preprints | 2026 | https://doi.org/10.20944/preprints202607.1328.v1 | 0 | 0 | [] | [
"analyses"
] | ||
du2025deepresearch | DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents | arXiv preprint arXiv:2506.11763 | 2025-06 | https://arxiv.org/abs/2506.11763 | 2506.11763 | https://github.com/Ayanami0730/deep_research_bench | 212 | 816 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
erdogan2025planandact | Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks | International Conference on Machine Learning | 2025-03 | https://arxiv.org/abs/2503.09572 | 2503.09572 | https://github.com/SqueezeAILab/plan-and-act | 197 | 45 | [
"model-design"
] | [
"targets/weights"
] |
fang-etal-2026-memp | Memp: Exploring Agent Procedural Memory | Findings of the Association for Computational Linguistics: ACL 2026 | 2025-08 | https://aclanthology.org/2026.findings-acl.866/ | 2508.06433 | https://github.com/zjunlp/MemP | 62 | 35 | [
"harness-design"
] | [
"targets/harness"
] |
featurebench2026 | FeatureBench: Benchmarking Agentic Coding for Complex Feature Development | arXiv | 2026-02 | https://arxiv.org/abs/2602.10975 | 2602.10975 | https://github.com/LiberCoders/FeatureBench | 26 | 87 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
feng2025gigpo | Group-in-Group Policy Optimization for LLM Agent Training | Advances in Neural Information Processing Systems | 2025-05 | https://arxiv.org/abs/2505.10978 | 2505.10978 | https://github.com/langfengQ/verl-agent | 375 | 2,250 | [
"model-design"
] | [
"targets/weights"
] |
frontierswe2026 | FrontierSWE | Proximal Blog | 2026 | https://frontierswe.com/blog | https://github.com/Proximal-Labs/frontier-swe | 0 | 219 | [
"benchmarks"
] | [
"evidence/benchmarks"
] | |
gaosurvey | A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence | TMLR 2026 | 2025-07 | https://arxiv.org/abs/2507.21046 | 2507.21046 | https://github.com/CharlesQ9/Self-Evolving-Agents | 99 | 1,300 | [] | [
"analyses"
] |
gonzalezpumariega2025robotouille | Robotouille: An Asynchronous Planning Benchmark for LLM Agents | International Conference on Learning Representations | 2025-02 | https://arxiv.org/abs/2502.05227 | 2502.05227 | https://github.com/portal-cornell/robotouille | 37 | 46 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
gou2024critic | CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing | International Conference on Learning Representations | 2023-05 | https://arxiv.org/abs/2305.11738 | 2305.11738 | https://github.com/microsoft/ProphetNet | 863 | 746 | [
"harness-design"
] | [
"targets/harness"
] |
gou2025mind2web | Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judge | arXiv preprint arXiv:2506.21506 | 2025-06 | https://arxiv.org/abs/2506.21506 | 2506.21506 | https://github.com/OSU-NLP-Group/Mind2Web-2 | 68 | 114 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
gulati2026askearlyasklate | Ask Early, Ask Late, Ask Right: When Does Clarification Timing Matter for Long-Horizon Agents? | arXiv | 2026-05 | https://arxiv.org/abs/2605.07937 | 2605.07937 | 2 | 0 | [
"harness-design"
] | [
"targets/harness"
] | |
guo2026questionansweringtaskcompletion | From Question Answering to Task Completion: A Survey on Agent System and Harness Design | arXiv | 2026-06 | https://arxiv.org/abs/2606.20683 | 2606.20683 | 3 | 0 | [
"harness-design"
] | [
"targets/harness"
] | |
gutierrez2024hipporag | HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models | Advances in Neural Information Processing Systems | 2024-05 | https://arxiv.org/abs/2405.14831 | 2405.14831 | https://github.com/OSU-NLP-Group/HippoRAG | 328 | 3,963 | [
"harness-design"
] | [
"targets/harness"
] |
han2026robocerebra | RoboCerebra: A Large-scale Benchmark for Long-horizon Robotic Manipulation Evaluation | Advances in Neural Information Processing Systems | 2025-06 | https://arxiv.org/abs/2506.06677 | 2506.06677 | https://github.com/buaa-colalab/RoboCerebra | 30 | 75 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
he2024webvoyager | WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models | Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | 2024-01 | https://arxiv.org/abs/2401.13919 | 2401.13919 | https://github.com/MinorJerry/WebVoyager | 434 | 1,122 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
hill2023mineplanner | MinePlanner: A Benchmark for Long-Horizon Planning in Large Minecraft Worlds | Proceedings of the 6th ICAPS Workshop on the International Planning Competition (WIPC) | 2023-12 | https://arxiv.org/abs/2312.12891 | 2312.12891 | https://github.com/IretonLiu/mine-pddl | 8 | 23 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
hou2026single | Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning | arXiv preprint arXiv:2607.07508 | 2026-07 | https://arxiv.org/abs/2607.07508 | 2607.07508 | 7 | 0 | [
"model-design"
] | [
"targets/weights"
] | |
hu2025adas | Automated design of agentic systems | ICLR 2025 | 2024-08 | https://arxiv.org/abs/2408.08435 | 2408.08435 | https://github.com/ShengranHu/ADAS | 282 | 1,631 | [
"harness-design"
] | [
"targets/harness"
] |
hu2025memory | Memory in the Age of AI Agents | arXiv preprint arXiv:2512.13564 | 2025-12 | https://arxiv.org/abs/2512.13564 | 2512.13564 | 245 | 0 | [
"harness-design"
] | [
"targets/harness"
] | |
hu2025step | Step-DeepResearch Technical Report | arXiv preprint arXiv:2512.20491 | 2025-12 | https://arxiv.org/abs/2512.20491 | 2512.20491 | https://github.com/stepfun-ai/StepDeepResearch | 12 | 571 | [
"harness-design"
] | [
"targets/harness"
] |
huang2024selfcorrect | Large Language Models Cannot Self-Correct Reasoning Yet | International Conference on Learning Representations | 2023-10 | https://arxiv.org/abs/2310.01798 | 2310.01798 | 1,135 | 0 | [
"harness-design"
] | [
"targets/harness"
] | |
huang2026rawexperienceskillconsumption | From Raw Experience to Skill Consumption: A Systematic Study of Model-Generated Agent Skills | arXiv | 2026-05 | https://arxiv.org/abs/2605.23899 | 2605.23899 | 13 | 0 | [
"harness-design"
] | [
"targets/harness"
] | |
imajuku2025alebench | ALE-Bench: A Benchmark for Long-Horizon Objective-Driven Algorithm Engineering | Advances in Neural Information Processing Systems | 2025-06 | https://arxiv.org/abs/2506.09050 | 2506.09050 | https://github.com/SakanaAI/ALE-Bench | 28 | 213 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
jansen2025codescientist | CodeScientist: End-to-End Semi-Automated Scientific Discovery with Code-based Experimentation | Findings of the Association for Computational Linguistics: ACL 2025 | 2025 | https://aclanthology.org/2025.findings-acl.692/ | https://github.com/allenai/codescientist | 55 | 348 | [] | [
"targets/substrate"
] | |
jiang2025aide | AIDE: AI-driven exploration in the space of code | arXiv | 2025-02 | https://arxiv.org/abs/2502.13138 | 2502.13138 | https://github.com/WecoAI/aideml | 174 | 1,491 | [] | [
"targets/data"
] |
jiang2026darwindynamicagenticallyrewriting | DARWIN: Dynamic Agentically Rewriting Self-Improving Network | arXiv | 2026-02 | https://arxiv.org/abs/2602.05848 | 2602.05848 | https://github.com/henryyjiang/DARWIN | 1 | 0 | [
"harness-design"
] | [
"targets/harness"
] |
jimenez2024swe | SWE-bench: Can Language Models Resolve Real-World GitHub Issues? | ICLR 2024 | 2023-10 | https://arxiv.org/abs/2310.06770 | 2310.06770 | https://github.com/SWE-bench/SWE-bench | 3,453 | 5,705 | [
"benchmarks"
] | [
"foundations/long-horizon"
] |
jin2026chainswe | ChainSWE: Benchmarking Coding Agents on Multi-Bug Software Maintenance | arXiv preprint arXiv:2607.02606 | 2026-07 | https://arxiv.org/abs/2607.02606 | 2607.02606 | 1 | 0 | [
"benchmarks"
] | [
"evidence/benchmarks"
] | |
jin2026reveal | ReVeal: Self-Evolving Code Agents via Reliable Self-Verification | The Fourteenth International Conference on Learning Representations | 2025-06 | https://arxiv.org/abs/2506.11442 | 2506.11442 | 12 | 0 | [
"harness-design"
] | [
"targets/harness"
] | |
kamoi2024can | When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs | Transactions of the Association for Computational Linguistics | 2024-06 | https://arxiv.org/abs/2406.01297 | 2406.01297 | 327 | 0 | [
"harness-design"
] | [
"targets/harness"
] | |
kang2025acon | ACON: Optimizing Context Compression for Long-horizon LLM Agents | arXiv preprint arXiv:2510.00615 | 2025-10 | https://arxiv.org/abs/2510.00615 | 2510.00615 | https://github.com/microsoft/acon | 84 | 106 | [
"harness-design"
] | [
"targets/harness"
] |
kapoor2024omniact | OmniACT: A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web | Computer Vision -- ECCV 2024 | 2024-02 | https://arxiv.org/abs/2402.17553 | 2402.17553 | 167 | 0 | [
"benchmarks"
] | [
"evidence/benchmarks"
] | |
kapoor2025holistic | Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluation | arXiv preprint arXiv:2510.11977 | 2025-10 | https://arxiv.org/abs/2510.11977 | 2510.11977 | https://github.com/princeton-pli/hal-harness | 56 | 311 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
khalifa2026process | Process Reward Models That Think | Transactions on Machine Learning Research | 2025-04 | https://arxiv.org/abs/2504.16828 | 2504.16828 | https://github.com/mukhal/ThinkPRM | 103 | 91 | [
"harness-design"
] | [
"targets/harness"
] |
khanal2026beyond | Beyond pass@1: A Reliability Science Framework for Long-Horizon LLM Agents | arXiv preprint arXiv:2603.29231 | 2026-03 | https://arxiv.org/abs/2603.29231 | 2603.29231 | 5 | 0 | [
"benchmarks"
] | [
"evidence/benchmarks"
] | |
kim2026sciencescalingagentsystems | Towards a Science of Scaling Agent Systems | arXiv | 2025-12 | https://arxiv.org/abs/2512.08296 | 2512.08296 | https://github.com/ybkim95/agent-scaling | 115 | 42 | [
"benchmarks",
"harness-design"
] | [
"evidence/benchmarks",
"targets/harness"
] |
kimi2026k3 | Kimi K3: Open Frontier Intelligence | arXiv | 2026-07 | https://arxiv.org/abs/2607.24653 | 2607.24653 | https://github.com/MoonshotAI/Kimi-K3 | 8 | 8,617 | [
"benchmarks",
"model-design",
"harness-design"
] | [
"evidence/benchmarks",
"targets/weights",
"targets/harness"
] |
kirgis2026shadow | Can AI Agents Conduct Open-Ended AI Research? Early Evidence from Two Case Studies | arXiv | 2026-07 | https://arxiv.org/abs/2607.27191 | 2607.27191 | 3 | 0 | [
"benchmarks"
] | [
"evidence/benchmarks"
] | |
koh2024visualwebarena | VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks | Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | 2024-01 | https://arxiv.org/abs/2401.13649 | 2401.13649 | https://github.com/web-arena-x/visualwebarena | 0 | 485 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
kovacs2026squeez | Squeez: Task-Conditioned Tool-Output Pruning for Coding Agents | arXiv preprint arXiv:2604.04979 | 2026-04 | https://arxiv.org/abs/2604.04979 | 2604.04979 | https://github.com/KRLabsOrg/squeez | 2 | 23 | [
"harness-design"
] | [
"targets/harness"
] |
kulikov2026autodata | Autodata: An Agentic Data Scientist to Create High Quality Synthetic Data | arXiv | 2026-06 | https://arxiv.org/abs/2606.25996 | 2606.25996 | 6 | 0 | [
"model-design"
] | [
"targets/data"
] | |
kwa2026measuring | Measuring AI Ability to Complete Long Software Tasks | NeurIPS 2025 | 2025-03 | https://arxiv.org/abs/2503.14499 | 2503.14499 | https://github.com/METR/eval-analysis-public | 134 | 313 | [] | [
"evidence/measurement"
] |
laban2026llms | LLMs Get Lost In Multi-Turn Conversation | International Conference on Learning Representations | 2025-05 | https://arxiv.org/abs/2505.06120 | 2505.06120 | https://github.com/microsoft/lost_in_conversation | 398 | 296 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
lange2025shinkaevolve | ShinkaEvolve: Towards open-ended and sample-efficient program evolution | arXiv | 2025-09 | https://openreview.net/forum?id=lKEdGCoDNC | 2509.19349 | https://github.com/SakanaAI/ShinkaEvolve | 139 | 1,354 | [] | [
"targets/substrate"
] |
lee2024benchmarking | Benchmarking Mobile Device Control Agents across Diverse Configurations | arXiv preprint arXiv:2404.16660 | 2024-04 | https://arxiv.org/abs/2404.16660 | 2404.16660 | https://github.com/jylee425/b-moca | 47 | 33 | [
"benchmarks"
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"evidence/benchmarks"
] |
lee2025learning | Learning to Contextualize Web Pages for Enhanced Decision Making by LLM Agents | The Thirteenth International Conference on Learning Representations | 2025-03 | https://arxiv.org/abs/2503.10689 | 2503.10689 | https://github.com/dgjun32/lcow_iclr2025 | 21 | 6 | [
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] | [
"targets/harness"
] |
lee2026metaharness | Meta-Harness: End-to-End Optimization of Model Harnesses | arXiv | 2026-03 | https://arxiv.org/abs/2603.28052 | 2603.28052 | https://github.com/stanford-iris-lab/meta-harness-tbench2-artifact | 141 | 1,183 | [
"harness-design"
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"targets/harness"
] |
lee2026rhi | Recursive harness self-improvement | arXiv | 2026-07 | https://arxiv.org/abs/2607.15524 | 2607.15524 | 6 | 0 | [
"harness-design"
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"targets/harness"
] | |
li2023behavior | BEHAVIOR-1K: A benchmark for embodied AI with 1,000 everyday activities and realistic simulation | Proceedings of The 6th Conference on Robot Learning | 2023 | https://proceedings.mlr.press/v205/li23a.html | https://github.com/StanfordVL/BEHAVIOR-1K | 382 | 1,657 | [
"benchmarks"
] | [
"evidence/benchmarks"
] | |
li2025salt | SALT: Step-level Advantage Assignment for Long-horizon Agents via Trajectory Graph | Findings of the Association for Computational Linguistics: EACL 2026 | 2025-10 | https://arxiv.org/abs/2510.20022 | 2510.20022 | 16 | 0 | [
"model-design"
] | [
"targets/weights"
] | |
li2025sorl | Stabilizing Off-Policy Training for Long-Horizon LLM Agent via Turn-Level Importance Sampling and Clipping-Triggered Normalization | arXiv preprint arXiv:2511.20718 | 2025-11 | https://arxiv.org/abs/2511.20718 | 2511.20718 | https://github.com/Cloud0723/SORL | 4 | 0 | [
"model-design"
] | [
"targets/weights"
] |
li2025webweaver | WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research | arXiv preprint arXiv:2509.13312 | 2025-09 | https://arxiv.org/abs/2509.13312 | 2509.13312 | https://github.com/Alibaba-NLP/DeepResearch | 41 | 19,873 | [
"harness-design"
] | [
"targets/harness"
] |
li2026acmagenticcontextmanagement | ACM: Agentic Context Management for Long Horizon Tasks | arXiv | 2026-07 | https://arxiv.org/abs/2607.23809 | 2607.23809 | https://github.com/lixiaochuan2020/agentic-context-management | 0 | 31 | [
"harness-design"
] | [
"targets/harness"
] |
li2026autosota | AutoSOTA: An End-to-End Automated Research System for State-of-the-Art AI Model Discovery | arXiv | 2026-04 | https://arxiv.org/abs/2604.05550 | 2604.05550 | https://github.com/tsinghua-fib-lab/AutoSOTA | 15 | 662 | [
"benchmarks"
] | [
"targets/substrate"
] |
li2026compactionrlreinforcementlearningcontext | CompactionRL: Reinforcement Learning with Context Compaction for Long-Horizon Agents | arXiv | 2026-07 | https://arxiv.org/abs/2607.05378 | 2607.05378 | 1 | 0 | [
"harness-design"
] | [
"targets/harness"
] | |
li2026harness | Agent Harness Engineering: A Survey | — | 2026 | https://picrew.github.io/LLM-Harness/ | https://github.com/Picrew/LLM-Harness | 0 | 2 | [] | [
"analyses"
] | |
li2026weavebench | WeaveBench: A Long-Horizon, Real-World Benchmark for Computer-Use Agents with Hybrid Interfaces | arXiv preprint arXiv:2606.09426 | 2026-06 | https://arxiv.org/abs/2606.09426 | 2606.09426 | 4 | 0 | [
"benchmarks"
] | [
"evidence/benchmarks"
] | |
liao2026kernelevolve | KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta | arXiv | 2025-12 | https://doi.org/10.1109/ISCA66397.2026.00063 | 2512.23236 | 2 | 0 | [] | [
"targets/substrate"
] | |
lindenbauer2025complexity | The Complexity Trap: Simple Observation Masking Is as Efficient as LLM Summarization for Agent Context Management | arXiv preprint arXiv:2508.21433 | 2025-08 | https://arxiv.org/abs/2508.21433 | 2508.21433 | 23 | 0 | [
"harness-design"
] | [
"targets/harness"
] | |
liu2024agentbench | AgentBench: Evaluating LLMs as Agents | International Conference on Learning Representations | 2023-08 | https://arxiv.org/abs/2308.03688 | 2308.03688 | https://github.com/THUDM/AgentBench | 1,185 | 3,691 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
liu2025mlmaster | ML-Master: Towards AI-for-AI via integration of exploration and reasoning | arXiv | 2025-06 | https://arxiv.org/abs/2506.16499 | 2506.16499 | https://github.com/sjtu-sai-agents/ML-Master | 54 | 447 | [
"benchmarks"
] | [
"targets/data"
] |
liu2026diveclaudecodedesign | Dive into Claude Code: The Design Space of Today's and Future AI Agent Systems | arXiv | 2026-04 | https://arxiv.org/abs/2604.14228 | 2604.14228 | https://github.com/VILA-Lab/Dive-into-Claude-Code | 28 | 2,078 | [
"harness-design"
] | [
"targets/harness"
] |
liu2026escherloopmutualevolutionclosedloop | Escher-Loop: Mutual Evolution by Closed-Loop Self-Referential Optimization | arXiv | 2026-04 | https://arxiv.org/abs/2604.23472 | 2604.23472 | https://github.com/scaling-group/escher-loop | 5 | 7 | [
"harness-design"
] | [
"targets/harness"
] |
liu2026llms | Do LLMs Catch Their Own Mistakes? A Comprehensive Benchmark for Reflective Tool Use LLMs | Findings of the Association for Computational Linguistics: ACL 2026 | 2026 | https://aclanthology.org/2026.findings-acl.86/ | 0 | 0 | [
"benchmarks"
] | [
"evidence/benchmarks"
] | ||
longclibench2026 | LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfaces | arXiv | 2026-02 | https://arxiv.org/abs/2602.14337 | 2602.14337 | https://github.com/finyorko/longcli-bench | 21 | 46 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
lu-etal-2025-runaway | Runaway is Ashamed, But Helpful: On the Early-Exit Behavior of Large Language Model-based Agents in Embodied Environments | Findings of the Association for Computational Linguistics: EMNLP 2025 | 2025-05 | https://aclanthology.org/2025.findings-emnlp.1304/ | 2505.17616 | https://github.com/Coldmist-Lu/AgentExit | 7 | 2 | [
"harness-design"
] | [
"targets/harness"
] |
lu2024aiscientist | The AI Scientist: Towards fully automated open-ended scientific discovery | arXiv | 2024-08 | https://arxiv.org/abs/2408.06292 | 2408.06292 | https://github.com/SakanaAI/AI-Scientist | 1,059 | 14,442 | [] | [
"targets/research"
] |
lu2026endtoendautomation | Towards End-to-End Automation of AI Research | Nature 2026 | 2026-03 | https://doi.org/10.1038/s41586-026-10265-5 | https://github.com/SakanaAI/AI-Scientist-v2 | 211 | 7,048 | [
"benchmarks"
] | [
"evidence/benchmarks"
] | |
lu2026meta | The Meta-Agent Challenge: Are Current Agents Capable of Autonomous Agent Development? | arXiv preprint arXiv:2606.04455 | 2026-06 | https://arxiv.org/abs/2606.04455 | 2606.04455 | https://github.com/ant-research/meta-agent-challenge | 2 | 20 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
lu2402weblinx | WebLINX: Real-World Website Navigation with Multi-Turn Dialogue | International Conference on Machine Learning | 2024-02 | https://arxiv.org/abs/2402.05930 | 2402.05930 | https://github.com/McGill-NLP/weblinx | 182 | 163 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
lu2408toolsandbox | ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities | Findings of the Association for Computational Linguistics: NAACL 2025 | 2024-08 | https://aclanthology.org/2025.findings-naacl.65/ | 2408.04682 | https://github.com/apple/ToolSandbox | 221 | 279 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
lupidi2026airsbench | AIRS-Bench: A Suite of Tasks for Frontier AI Research Science Agents | arXiv | 2026-02 | https://arxiv.org/abs/2602.06855 | 2602.06855 | https://github.com/facebookresearch/airs-bench | 19 | 111 | [
"benchmarks"
] | [
"evidence/benchmarks"
] |
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