The paper examines three open-source agent harnesses—LangChain’s deepagents, Earendil’s pi, and DeepSeek’s dsh—each built from contrasting design philosophies. By analyzing their source code and commit histories, the authors find that the mature harnesses converge on five common architectural elements: a commoditized loop, an append‑only replayable session record, model quirks stored as data, progressive disclosure of context, and explicit extension seams. A fourth harness, used as a held‑out check, also displays all five elements and even reuses another’s implementation, indicating that convergence arises from parallel discovery, diffusion, and literal reuse rather than independent invention. The study notes a missing dimension—external verifiability via a tamper‑evident record—highlighting a future axis for provenance‑sensitive domains.
By Dai Jiahong
The paper introduces VerifierBench, a benchmark comprising 222 tasks from six agent benchmarks and 4,993 hidden test cases designed to evaluate the generation of executable verifiers by large language model agents. It presents Verifier4Verifier (V4V), a six-check audit that screens source verifiers and test suites, and reports that across 19 models, normalized balanced accuracy ranges from 10.6 to 62.1, with generated verifiers more reliably rejecting invalid solutions than accepting valid ones. Case studies highlight that many false rejections stem from verifiers that accept no tested solution, revealing unsupported assumptions about solution formats, tool use, and code changes that exclude valid solutions, and underscore the need to test both acceptance of correct solutions and other permissible solutions.
By Jianing Yin, Ziyi Chen, Hao Wang, Yiyou Sun, Zijian Jin, Dawn Song, Daoan Zhang
arXiv:2605.06445v2 Announce Type: replace-cross
Abstract: Large Language Model (LLM) agents demonstrate strong performance in autonomous code generation under loose specifications. However, productio...
By Francesco Dente, Dario Satriani, Paolo Papotti
arXiv:2607. 14896v1 Announce Type: cross Abstract: Addressing a structural-engineering request requires more than a single answer; it requires a chain of interdependent artifacts: interpreted requirements, a computable model, validation records, solver outputs, code-check records, and a final report.
By Sizhong Qin, Yi Gu, Yao Jiang, Ao Cai, Changjian Zhou, Shaoxuan Shuai, Jiachang Wang, Tianhao Shen, Yueqiang Li, Xinhao Li, Li Zeng, Yueshi Chen, Dachen Gao, Genrong Xu, Wenjie Liao, Xinzheng Lu
The paper proposes adding a verification layer to large language model agents, using a V-model design where each step of the agent’s plan has a dedicated verifier. In a pilot study on the MuSiQue dataset, verified configurations outperformed unverified ones, with deterministic gates correcting most errors at zero cost. The authors plan to extend the architecture to full integration and evaluate it over the next year.
By Ali Afoud, Jie JW Wu
arXiv:2601.19747v2 Announce Type: replace-cross
Abstract: In the rapidly evolving field of Electronic Design Automation (EDA), the deployment of Large Language Models (LLMs) for Register-Transfer Lev...
By Jiale Liu, Taiyu Zhou, Tianqi Jiang
arXiv:2605. 27898v2 Announce Type: replace Abstract: As LLMs are increasingly deployed as agents, reliable assessment of their agentic capabilities has become essential.
By Pengyu Zhu, Lijun Li, Yaxing Lyu, Qianxin Luo, Jingyi Yang, Yi Liu, Tingfeng Hui, Xinyu Yuan, Li Sun, Sen Su, Jing Shao
arXiv:2607. 16215v1 Announce Type: new Abstract: Existing guardrail systems for large language model agents operate as binary classifiers that block unsafe content, leaving organizations to discard failing outputs and retry from scratch.
By Sumit Verma, Pritam Prasun, Pritish Kumar
arXiv:2607. 22759v1 Announce Type: cross Abstract: Large language models (LLMs) show promise in code generation, but their capabilities to produce correct, synthesizable hardware description language (HDL) code still remain to be properly benchmarked.
By Angshuman Chakravertty, Rahul Koshti, Buddhi Prakash Sharma, Vinay Chamola
arXiv:2603. 28590v3 Announce Type: replace Abstract: Large language models (LLMs) can generate chains of thought (CoTs) that are not always causally responsible for their final outputs.
By Han Wang, Yifan Sun, Brian Ko, Mann Talati, Jiawen Gong, Zimeng Li, Naicheng Yu, Xucheng Yu, Wei Shen, Vedant Jolly, Huan Zhang
SemVerBench is a benchmark that evaluates how well large language models (LLMs) understand and apply version-constraint resolution semantics, such as determining whether a version satisfies constraints like ^1.2.3 or >=2.0. The study finds that many models struggle with certain corner cases, with GPT‑5.1 performing poorly while Claude and Opus perform much better. The authors suggest that the failures stem from an activation/application gap rather than a lack of knowledge, and recommend that coding agents delegate version resolution to a dedicated resolver tool.
By Qibai Chen, Zeming Liu
arXiv:2607. 20499v1 Announce Type: new Abstract: Large Language Models generate plausible backend code, but a single-pass paradigm provides no guarantee of correctness or runtime reliability.
By Sai Deekshith Lekkala, Jothi Prabha Appadurai, Rohith Reddy Bellibatlu, Manpreet Singh