SKILL: Self-correcting Knowledge-guided Iterative Large Language Model Agent for Logic Optimization
SKILL is a self-correcting, knowledge-guided iterative LLM agent that unifies multi-agent reasoning with RL-based environment interaction for automated logic synthesis optimization It coordinates three specialized LLMs—GPT-4o (strategic planning), Claude Sonnet 4 (detailed reasoning), and Gemini 2.5 Pro (efficient analysis)—paired with a PPO-based RL agent A novel self-correcting module monitors PDA metrics from the synthesis environment, detects suboptimal behaviors, and triggers LLM-guided rec
Analysis
TL;DR
- SKILL is a self-correcting, knowledge-guided iterative LLM agent that unifies multi-agent reasoning with RL-based environment interaction for automated logic synthesis optimization
- It coordinates three specialized LLMs—GPT-4o (strategic planning), Claude Sonnet 4 (detailed reasoning), and Gemini 2.5 Pro (efficient analysis)—paired with a PPO-based RL agent
- A novel self-correcting module monitors PDA metrics from the synthesis environment, detects suboptimal behaviors, and triggers LLM-guided recovery strategies
- SKILL achieves a 12.4% PDA improvement over expert-designed flows and an 86.3% success rate on logic systems up to 500K gates across IWLS, OpenCores, and EPFL benchmarks
- The approach addresses key limitations of traditional expert flows (lack of adaptability) and pure RL methods (low sample efficiency, limited interpretability)
Why It Matters
This work represents a significant step toward bridging the gap between symbolic AI reasoning and reinforcement learning in hardware design automation, a domain where search spaces are exponentially large and reward signals are notoriously sparse. For AI practitioners, it demonstrates a practical multi-agent architecture where different LLMs are assigned specialized roles rather than treated as monolithic decision-makers, offering a template for complex optimization problems. The self-correcting feedback loop also provides a compelling pattern for building resilient AI agents that can recover from suboptimal trajectories without human intervention.
Technical Details
- Multi-agent LLM coordination: SKILL decomposes the optimization task across three specialized models—GPT-4o handles high-level strategic planning, Claude Sonnet 4 performs detailed step-by-step reasoning, and Gemini 2.5 Pro conducts efficient analysis—creating a division of labor that leverages each model's strengths
- PPO-based RL integration: A Proximal Policy Optimization agent learns actionable synthesis policies through direct interaction with EDA tools, combining the sample efficiency of LLM priors with the iterative improvement capabilities of RL
- Self-correcting module: The system continuously monitors Physical Design Accuracy (PDA) metrics from the synthesis environment, detects deviations from optimal behavior, and invokes targeted LLM-guided recovery strategies to correct course
- Benchmark evaluation: Tested on three standard benchmarks—IWLS, OpenCores, and EPFL—demonstrating 12.4% PDA improvement over expert flows and 86.3% success rate on circuits up to 500K gates
- Problem framing: Addresses the core challenges of logic synthesis optimization—exponentially growing search spaces, sparse reward signals, and diverse logic structures—by combining interpretability of LLM reasoning with the adaptability of RL
Industry Insight
- The multi-agent specialization pattern (assigning distinct LLMs to planning, reasoning, and analysis roles) is likely to become a standard architecture for complex AI agents in engineering domains, as it balances capability with interpretability better than single-model approaches
- The self-correcting feedback mechanism demonstrated here could be generalized beyond logic synthesis to other CAD and design automation workflows, potentially reducing reliance on hand-tuned expert heuristics across EDA toolchains
- The 12.4% improvement over expert flows on established benchmarks signals that LLM+RL hybrid agents are approaching—and in some cases surpassing—human expertise in specialized optimization tasks, which could accelerate adoption of AI-driven design automation in semiconductor and hardware companies
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