SKILL: Self-correcting Knowledge-guided Iterative Large Language Model Agent for Logic Optimization

[2608.14579] SKILL: Self-correcting Knowledge-guided Iterative Large Language Model Agent for Logic Optimization Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Artificial Intelligence arXiv:2608.14579 (cs) [Submitted on 12 Jun 2026] Title: SKILL: Self-correcting Knowledge-guided Iterative Large Language Model Agent for Logic Optimization Authors: Rui Yang View a PDF of the paper titled SKILL: Self-correcting Knowledge-guided Iterative Large Language Model Agent for Logic Optimization, by Rui Yang View PDF HTML (experimental) Abstract: Logic synthesis optimization poses significant challenges due to exponentially growing search spaces, sparse reward signals, and diverse logic structures. Traditional expert-designed flows lack adaptability, while reinforcement learning (RL) methods often suffer from low sample efficiency and limited interpretability. We introduce SKILL, a Self-correcting Knowledge-guided Iterative Large Language Model Agent that unifies multi-agent LLM reasoning and RL-based environment interaction for automated synthesis optimization. SKILL coordinates three specialized LLMs: GPT-4o for strategic planning, Claude Sonnet 4 for detailed reasoning, and Gemini 2.5 Pro for efficient analysis with a PPO-based RL agent that learns actionable policies through direct interaction with synthesis tools. A novel self-correcting module monitors environment feedback (PDA metrics), detects suboptimal behaviors, and invokes LLM-guided recovery strategies. Evaluations on IWLS, OpenCores, and EPFL benchmarks show SKILL achieves a 12.4 % PDA improvement over expert flows and 86.3% success rate on logic systems up to 500K gates. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.14579 [cs.AI] (or arXiv:2608.14579v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.14579 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Rui Yang [ view email ] [v1] Fri, 12 Jun 2026 08:54:58 UTC (19 KB) Full-text links: Access Paper: View a PDF of the paper titled SKILL: Self-correcting Knowledge-guided Iterative Large Language Model Agent for Logic Optimization, by Rui Yang View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• SKILL: Self-correcting Knowledge-guided Iterative Large Language Model Agent for Logic Optimization#610081
• Explore Knowledge Representation, Reasoning, and Planning Techniques for Building Robust and Efficient Intelligent Systems#709935
• The Fellowship of the Query: Learning Retrieval Actions#1076309
• Exposition and Recognition: Preparing Subjective Logic in Hegel's Science of Logic#776798
• Making Sense of Paraconsistent Logic: The Nature of Logic, Classical Logic and Paraconsistent Logic#772291