Agent Skills can specify procedural and resource obligations for tool use, and language models instantiate them as concrete programs. However, when models turn this guidance into code for existing too...
SkillEffect is a checked‑lowering runtime that ensures agent tool calls stay within memory limits by verifying each proposed program against an immutable input before execution. It uses audited relation plugins to provide source recognition, bounded intermediate representation construction, and postconditions, while a shared runtime handles selection, bounded VM execution, and atomic capacity leasing. Experiments across six operator families show that bounded access significantly reduces peak memory usage and improves completion rates under fixed memory caps.
By Yinuo Wang, Yiyu Shi
arXiv:2603. 20075v2 Announce Type: replace-cross Abstract: Compilers are critical to modern computing, yet fixing compiler bugs is difficult.
By Yingwei Zheng, Cong Li, Shaohua Li, Yuqun Zhang, Zhendong Su
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
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
Large language model (LLM) agents are evolving from request-response assistants into long-running software actors: they maintain state across model calls, fork subtasks, wait for external events, request human authority, generate tools, and perform side effects that must be resumed and audited. This paper presents Agent libOS, a library-OS-inspired runtime substrate for LLM agents.