Trace2Skill: Verifier-Guided Skill Evolution for Long-Context EDA Agents
📰 ArXiv cs.AI
Learn how Trace2Skill improves hardware agents for complex Verilog design problems without requiring RTL-specialized model fine-tuning
Action Steps
- Implement Trace2Skill framework to improve hardware agent performance
- Use verifier-guided skill evolution to refine agent skills
- Apply test-time scaling to enhance agent accuracy
- Configure the framework to handle large repository snapshots
- Test the framework on complex Verilog design problems to evaluate its effectiveness
Who Needs to Know This
This benefits teams working on hardware LLM agents, particularly those dealing with complex Verilog design problems, as it enhances the agent's ability to solve these challenges efficiently
Key Insight
💡 Trace2Skill enhances hardware agents without needing RTL-specialized model fine-tuning, making it a valuable tool for teams working on complex design problems
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🚀 Improve hardware agents for complex Verilog design problems with Trace2Skill! 💻
Key Takeaways
Learn how Trace2Skill improves hardware agents for complex Verilog design problems without requiring RTL-specialized model fine-tuning
Full Article
Title: Trace2Skill: Verifier-Guided Skill Evolution for Long-Context EDA Agents
Abstract:
arXiv:2605.21810v1 Announce Type: new Abstract: Complex Verilog Design Problems (CVDP) challenge hardware LLM agents because solving them requires localizing verifier-relevant RTL, testbenches, include paths, and build dependencies inside large repository snapshots, making precise edits, and recovering from sparse hidden-verifier failures. We present Trace2Skill, a test-time scaling framework that improves a hardware agent without RTL-specialized model fine-tuning. Rather than training a new mod
Abstract:
arXiv:2605.21810v1 Announce Type: new Abstract: Complex Verilog Design Problems (CVDP) challenge hardware LLM agents because solving them requires localizing verifier-relevant RTL, testbenches, include paths, and build dependencies inside large repository snapshots, making precise edits, and recovering from sparse hidden-verifier failures. We present Trace2Skill, a test-time scaling framework that improves a hardware agent without RTL-specialized model fine-tuning. Rather than training a new mod
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