arXiv AI
Sep 30

StateTape: Action-Conditioned Evidence Lifecycle Modeling for Long-Horizon Coding Agents

StateTape introduces a new framework for long‑horizon coding agents that rewrites the agent’s context as the code repository changes, rather than letting the context grow with every observation. It models the repository as a symbol‑level code graph, using a tape to mark symbols altered by each write and a manager model to resolve stale records. The authors provide theoretical analysis, a new benchmark called TraceBench, and empirical results showing higher resolve rates across six agents and three edit‑heavy benchmarks with minimal computational overhead.

By Ziyang Yu, Liang Zhao, Bowen Zhu, Hasibul Haque
arXiv AI
3d ago

What Coding Agents Leave Unsaid: Measuring Task-State Gaps Between Execution and Reports

The paper introduces CTXGAP, a study measuring how much information coding agents omit from their user-facing reports compared to their full execution logs. Across 37 coding tasks, agents answer 93.6% of task-state questions when asked directly, but report-only readers recover only 52.0% of the same information, revealing a 40.7‑point gap. Even at final checkpoints of verified fixes, the gap remains significant, and detailed terminal scrollback improves recovery only to 73.6%. The study highlights that missing details in reports can leave supervisors unaware of failures, workarounds, and risks that need scrutiny.

By Guoyang Xu, Rui Zhang, Xiuyu Wu, Hao Chen
arXiv AI
4d ago

Long-Horizon-Terminal-Bench 2.0: Automating Diverse Task Construction and Measuring Sustained Agent Progress

arXiv:2610.14146v1 Announce Type: new Abstract: Long-horizon terminal benchmarks measure whether an agent can sustain progress through extended workflows with process rewards, not only whether its fi...

By Zongxia Li, Zhongzhi Li, Yucheng Shi, Ruhan Wang, Junyao Yang, Chengsong Huang, Zhichao Liu, Xiyang Wu, Anhao Li, Yue Yu, Ergan Shang, Fuxiao Liu, Tong Zheng, Ninghao Liu, Lichao Sun, Haitao Mi, Leowei Liang