The paper introduces Pufibara, an agent harness designed to maintain engineering state and evidence across revisions in Modelica-based physical system modeling. It also presents a 232-task Modelica Agent Workflow Benchmark covering model repair, generation, and tuning, evaluated by an external benchmark-owned evaluator. Experiments show Pufibara outperforms Claude Code in task success and resource efficiency across two LLM backends.
By Zizhe Wang
Spec‑Harness evaluates how well large language models (LLMs) synthesize Java Modeling Language (JML) specifications by measuring behavioral adequacy across precondition and postcondition correctness and completeness. The study shows that while prompt optimization can raise verifier pass rates, many accepted specifications remain behaviorally weak, either over‑ or under‑constraining inputs and outputs. Spec‑Harness also serves as a feedback mechanism that improves the quality of specifications generated by general‑purpose coding agents and a specialized JML agent.
By Md Rakib Hossain Misu, Iris Ma, Cristina V. Lopes
arXiv:2508. 02721v2 Announce Type: replace-cross Abstract: While powerful, the inherent non-determinism of large language model (LLM) agents limits their application in structured operational environments where procedural fidelity and predictable execution are strict requirements.
By Libin Qiu, Yuhang Ye, Zhirong Gao, Xide Zou, Junfu Chen, Ziming Gui, Weizhi Huang, Xiaobo Xue, Wenkai Qiu, Kun Zhao
The paper introduces JAZ, a minimalist LLM agent framework that centers on a single primitive called “invoke”, which allows an LLM to write and execute arbitrary code, including recursive calls, while treating all inputs and interaction history as variables in the code environment. JAZ provides built‑in hooks for constraints and monitoring but relies solely on prompting, without external tools, memory systems, or file‑system access. Experiments show that JAZ “invoke” outperforms specialized external harnesses such as Letta (MemGPT) and ACE on long‑horizon recall tasks and continual self‑improvement, achieving higher accuracy at lower cost.
By Zhening Li, Joshua Liu, Mateja Vukelic, Nicole Shen, Supriya Lall, Amitayush Thakur, Alex Zhang, Omar Khattab, Jonathan Light, Armando Solar-Lezama
arXiv:2604.00280v3 Announce Type: replace-cross
Abstract: Formal specifications play a central role in ensuring software reliability, yet automatically synthesizing high-quality specifications remain...
By Md Rakib Hossain Misu, Cristina V. Lopes, Iris Ma, Yuangang Li, Shuvendu Lahiri
arXiv:2512. 02080v3 Announce Type: replace-cross Abstract: The integration of Formal Verification tools with Large Language Models (LLMs) offers a path to scale software verification beyond manual workflows.
By Pierre Dantas, Lucas Cordeiro, Youcheng Sun, Waldir Junior
arXiv:2609.21190v1 Announce Type: cross
Abstract: Ensuring the correctness of LLM-generated code is a core challenge for modern software engineering. Benchmarks for agentic code generation check corr...
By George Ma, Benjamin Mikek, Haoyu Li, Ferhat Erata, Yuhao Zhang, Zeren Shui, Behrooz Omidvar Tehrani, Jun Huan, Murali Krishna Ramanathan, Somayeh Sojoudi, Hao Zhou, Anoop Deoras
The paper surveys how large language models (LLMs) are being used in hardware verification, covering tasks such as assertion generation, testbench creation, bug localization, and formal reasoning. It categorizes the literature by methodology, verification goal, tool interaction, benchmarks, and evaluation metrics, and distinguishes between inference-time techniques (prompting, retrieval, structured reasoning, agentic workflows) and training-time adaptation. The authors highlight that LLMs excel as semantic reasoning, search, and orchestration components within verification workflows, yet emphasize the need for semantic alignment, scalable integration, generalization, and rigorous evaluation to ensure true verification correctness.
By Hao Zheng, Jaime Rafael Imperial, Bardia Nadimi, Xiangfei Kong
The paper presents a unified dataset of 350 Lean tasks to evaluate specification generation (SpecGen) for verifiable code, combining metrics of formal validity, reference similarity, equivalence, and behavioural adequacy. It highlights that restricting similarity measures to a subset of tasks can shift rankings, and shows that a perfect postcondition score can still miss critical input coverage, underscoring the need for separate feedback on input and output constraints.
By Srijith Nair, Aditya Vempaty, Jia Liu, Ashish Jagmohan
arXiv:2510.14980v3 Announce Type: replace
Abstract: Large language models (LLMs) have shown strong abilities in writing and revising programs, yet many program-synthesis benchmarks still evaluate pro...
By Wenqian Zhang, Yangyi Huang, Weiyang Liu, Zhen Liu
arXiv:2608. 09277v1 Announce Type: new Abstract: Verified code generation asks a large language model (LLM) to generate both an executable program and a machine-checkable proof that the program meets a formal specification, promising software that is correct by construction.
By Zenan Li, Ziran Yang, Peiyang Song, Zhaoyu Li, Kaiyu Yang
arXiv:2608. 02680v1 Announce Type: cross Abstract: Tool-using language-model agents repeatedly rediscover procedures they have already executed, producing traces that mix reusable structure with retries, exploration, accidental ordering, and repeated lookups.
By Salma El Yadouni (EPFL), Guanyi Li (Binome Technologies)