Agora is a system that uses Git as a shared memory for autonomous research agents, recording each claim as an immutable commit in an append‑only directed acyclic graph. In a 12‑day run, 13 language‑model workers independently explored a weight‑transfer problem, producing 1,703 contributions that improved a 119.6M‑parameter model’s performance from 3.39 to 1.899 bits per byte. The system’s design includes a diversity‑aware selection rule and an index that tracks the frontier, neglected branches, and verification status of each claim.
By Yifan Zhang, Yunheng Zou, Shaokun Zhang, Jian Hu, Hao Zhang, Binfeng Xu, Jan Kautz, Yi Dong
arXiv:2607. 14390v1 Announce Type: cross Abstract: Coding agents now produce a growing share of a team's code, while the reasoning behind each change -- the alternatives weighed, the constraints discovered, the approaches rejected -- is trapped in assistant transcripts that vanish with the session.
By Frank Guo
arXiv:2606. 08106v1 Announce Type: new Abstract: Self-evolving agents improve by repeatedly proposing changes to their own prompts, skills, or workflows and keeping those that score higher on a small held-out set.
By Zayx Shawn
arXiv:2609.11987v1 Announce Type: cross
Abstract: An agentic coding system couples a language model to a harness: the tools, prompts and control flow that turn a chat model into an autonomous softwar...
By Mohsen Arjmandi
The paper introduces “Revise”, a runtime system that performs validity-guided, fine-grained recovery for online revisions in structured agent workflows. When a revision arrives, Revise intersects the change with recorded data and control dependencies, propagates the impact through the partially executed DAG, stops invalid work, preserves unaffected progress, and recomputes only the affected region. Experiments on real coding‑agent traces and LangGraph/LLMCompiler applications show that Revise matches a latest‑version oracle, reduces model calls by up to 56%, and improves service‑level objective goodput under load.
By Ruoling Qi, Xuaner Wu, Penghang Liu, Jian Chen, Yirui Liu
arXiv:2603.01209v3 Announce Type: replace
Abstract: In CodeAct, language-model agents write Python that calls tools and use execution feedback to choose actions. Persistent runtimes preserve Python v...
By Victor May, Van Khue Nguyen, Aaditya Salgarkar, Yishan Wang, Diganta Misra, Huu Nguyen
The paper investigates how machine‑learning models can determine whether a claim (a test assertion) remains valid after a code change. It compares two questioning strategies: asking whether a diff preserves behavior versus asking whether a specific claim still holds. The authors find that the latter approach yields far higher precision (up to 0.974) across models of varying cost, while the former performs poorly (precision 0.291–0.329). They also benchmark against a regression‑test selector, showing that even near‑complete knowledge of a change’s reach does not reliably identify falsified claims. The study is grounded in 10,369 mined claims with 184 execution‑verified flips from 23 Python libraries.
By Atul Anand
The paper investigates how language‑model judges can make version‑dependent errors when evaluating upgraded agents. Using 35 public coding‑agent submissions, two customer‑service agents, and over a thousand expert‑labeled trajectories, the authors show that fixed judges often reject task‑conditioned error invariance and can incorrectly approve failed patches, especially as agent capability increases. Paired audits of current outputs reduce interval width only marginally, and the study concludes that independent human patch review is still necessary.
By Jiapeng Li
The paper reports on a series of experiments examining how different forms of directives—such as record pointers, criteria, or combinations—affect an agent’s choice of archived source records when it inherits six one-line memories. Across twelve registered studies involving 14,760 attempts on a single instrument lineage, the authors measured the impact of various directive formats on six direct-provider models, nine OpenRouter-served models, and several Claude and Opus 5 models, noting differences in performance metrics and replication outcomes. The results are purely descriptive, detailing the effects of exact edits on fixed panels with registered intervals and no claim of underlying mechanisms.
By Kazuki Nakayashiki
arXiv:2608. 13228v1 Announce Type: new Abstract: Agent harnesses combine retrieval, routing, state, provenance, and verification, but locally successful components may disagree on shared state.
By Saveliy Batruin
The study investigates how inference‑time interventions and weight consolidation affect open‑ended generation in an online bin‑packing task. By iteratively generating, verifying, selecting, and consolidating with LoRA, the model’s outputs shift toward higher value, reducing excess by 1.7 points and outperforming random consolidation by 3.1 points. Across three independent runs, the mean performance remained consistent, and the best candidates converged to the classic heuristic’s level without exceeding it, while consolidation also lowered the proportion of better‑than‑classic candidates but increased their absolute number.
By Roberto I. Ono Filho
arXiv:2608.21964v1 Announce Type: new
Abstract: Large language model (LLM) agents increasingly operate over evolving software repositories, where success depends on repository-specific procedural kno...
By Chenyuan Duan, Ge Shi, Zineng Mao, Ge Zhang, Hao Liang, Yinzhu Piao, Yuchen Wu, Zhixin Yao, Kaiyu Huang, Wenhao Huang, Linzhuang Sun, Shen Yan, Wentao Zhang