arXiv AI

StagedWorkspace: A Versioned Workspace for Knowledge-Work Agents

arXiv:2608. 18050v1 Announce Type: new Abstract: AI agents increasingly perform knowledge work (i.

arXiv AI

Aug 12

GitSkills: A Dataset of Agent Skills on GitHub

arXiv:2608. 10906v1 Announce Type: cross Abstract: An agent skill is a folder containing a SKILL.

By Giuseppe Destefanis, Daniel Graziotin, Matteo Vaccargiu, Marco Ortu

agents

arXiv AI

Jun 11

Exploration Structure in LLM Agents for Multi-File Change Localization

arXiv:2606. 11976v1 Announce Type: cross Abstract: Software engineering tools increasingly rely on LLM based agents to localize files to change to resolve a software issue.

By Akeela Darryl Fattha, Kia Ying Chua, Lingxiao Jiang, Laura Wynter

llms agents benchmarks

arXiv AI

Jun 24

LemonHarness Technical Report

arXiv:2606. 24311v1 Announce Type: new Abstract: As large language model (LLM) agents are applied to longer tasks, they increasingly modify workspace state across multiple rounds of iteration.

By Kailong Ren, Fubo Sun, Jiachen Liu, Liu Yang, Zimo Yin, Jiaying Li, Congli Yin, Ming He, Yu Huo, Jiawei Liu, Zeping Chen, Yubin Huangfu, Ronghua Li, Yixuan Wu, Xing Su, Yanzhi Xu, Likang Wu, Hongke Zhao, Lei Zhang, Xiaohui Geng, Jianping Fan

llms rag agents

arXiv AI

Jul 31

Filesystem-Based Memory for LLM Agents: Organization, Evolution, and Sustainability

arXiv:2607. 26637v1 Announce Type: cross Abstract: Deployed LLM agents increasingly keep their long-term memory as a filesystem: a directory tree of markdown files that the agent itself reads, writes, and reorganizes through generic file tools.

By Sizhe Zhou, Sheldon Yu, Hui Wei, Junda Wu, Siru Ouyang, Yizhu Jiao, Shijia Pan, Julian McAuley, Yu Zhang, Tong Yu, Jiawei Han

llms agents robotics benchmarks

Hugging Face Trending Papers

Jun 10

Exploration Structure in LLM Agents for Multi-File Change Localization

Software engineering tools increasingly rely on LLM based agents to localize files to change to resolve a software issue. Most AI agents explore repositories linearly, that is, visiting one directory or file per step.

llms agents benchmarks

arXiv AI

Jul 28

CORVUS: Context Optimization and Reduction Via Underlying Synchronization for LLM Coding Agents

arXiv:2607. 22711v1 Announce Type: cross Abstract: LLM coding agents operate by constructing trajectories that accumulate reasoning, tool calls, and results to enable multi-step decision-making.

By Mingwei Zheng, David OBrien, Siwei Cui, Pardis Pashakhanloo, Rajdeep Mukherjee, Myeongsoo Kim, Sachit Kuhar

llms agents

arXiv Machine Learning

Jul 31

Change2Task: From Repository Changes to Executable Coding Agent Tasks and Environments

arXiv:2607. 28591v1 Announce Type: cross Abstract: Scaling coding agents requires a continuing supply of executable data for training, benchmarking, and continuous evaluation.

By Haomin Qi, Xingliang Wang, Xuanqi Gao, Baihui Sang, Xin Zhang, Minghua Ma, Pengfei Gao, Yu Kang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang

agents benchmarks

arXiv AI

Jul 14

Dynamic Agent Skills: A Lifecycle Survey and Taxonomy of Evolving Skill Libraries

arXiv:2607. 10113v1 Announce Type: new Abstract: Large language model agents increasingly store reusable procedures outside the model.

By Yubo Li

llms agents reinforcement-learning efficiency benchmarks safety

arXiv AI

Aug 11

DocAtlas: Long-Document Understanding as Mutable-State Interaction

arXiv:2608. 07527v1 Announce Type: cross Abstract: Long-document understanding requires models to find and combine evidence across many pages, layouts, tables, figures, and charts.

By Hongchen Wei, Yuanzhe Wang, Bei Liu, Yifan Yang, Qi Dai, Kai Qiu, Yunsheng Li, Dongdong Chen, Chong Luo, Zhenzhong Chen, Baining Guo

llms rag agents reinforcement-learning multimodal

arXiv AI

Jun 16

Dr-DCI: Scaling Direct Corpus Interaction via Dynamic Workspace Expansion

arXiv:2606. 14885v1 Announce Type: new Abstract: Agentic search over large corpora relies on retriever-mediated interfaces (e.

By Yi Lu, Zhuofeng Li, Ping Nie, Haoxiang Zhang, Yuyu Zhang, Kai Zou, Wenhu Chen, Jimmy Lin, Dongfu Jiang, Yu Zhang

agents benchmarks

Hugging Face Trending Papers

Jul 30

Change2Task: From Repository Changes to Executable Coding Agent Tasks and Environments

Scaling coding agents requires a continuing supply of executable data for training, benchmarking, and continuous evaluation. Each task must couple a realistic software state with a specification, development tools, and reliable verification.

agents benchmarks

arXiv AI

Jul 14

What Context Does a Coding Agent Actually Need to Act?

arXiv:2607. 09691v1 Announce Type: cross Abstract: A modern coding agent can hold an entire repository in its context window.

By Brian Sam-Bodden

llms agents benchmarks

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