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Trace2Skill: Verifier-Guided Skill Evolution for Long-Context EDA Agents

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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.

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Authors: Zijian Du, Nathaniel Pinckney
Released: 2026-05-23 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Trace2Skill: Verifier-Guided Skill Evolution for Long-Context EDA Agents overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, rate, without, candidate, test, language, model. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2605.21810

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