New LLM agent SKILL optimizes logic synthesis with multi-model approach

Researchers have developed SKILL, a novel agent that uses multiple large language models and reinforcement learning to optimize logic synthesis. The system employs GPT-4o for strategic planning, Claude Sonnet 4 for detailed reasoning, and Gemini 2.5 Pro with a PPO-based RL agent for interaction with synthesis tools. SKILL incorporates a self-correcting module to monitor feedback and implement recovery strategies, demonstrating a 12.4% improvement over existing expert flows on benchmarks. AI

IMPACT This approach could lead to more adaptable and efficient logic synthesis tools, potentially accelerating hardware design.

RANK_REASON The cluster describes a research paper detailing a new method for logic optimization using LLMs and RL. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Claude Sonnet 4
• Gemini 2.5 Pro
• GPT-4o
• OpenCores
• Proximal Policy Optimization
• SKILL
• Swiss Federal Institute of Technology in Lausanne

• paper
• model release

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