mlsanityData to 14 Oct 2026

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

arXiv 2608.14579 ↗First posted 12 Jun 2026cs.AIcited by 0

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 16 of its 53 references that are in the map. First posted on arXiv 12 Jun 2026.

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16 of 53 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework1 Aug

Other references · 15

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2023GPT-4 Technical Reportcited by 18,396
  5. 2017Proximal Policy Optimization Algorithmscited by 15,215
  6. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  7. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  8. 2021Convex optimizationcited by 2,515
  9. 2022Emergent Abilities of Large Language Modelscited by 2,512
  10. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  11. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,434
  12. 2018A Tutorial on Bayesian Optimizationcited by 744
3 more
  1. 2019DRiLLS: Deep Reinforcement Learning for Logic Synthesiscited by 47
  2. 2021BOiLS: Bayesian Optimisation for Logic Synthesiscited by 18
  3. 2024Logic Synthesis Optimization with Predictive Self-Supervision via Causal Transformerscited by 1

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