arXiv AI

EvoDRC: A Self-Evolving Agentic Framework for Automated DRC Violation Repair

arXiv:2607. 20019v1 Announce Type: new Abstract: Design rule check (DRC) closure remains a major bottleneck in advanced-node physical design.

arXiv AI
Jul 22

Bridging the Last Mile of Circuit Design: PostEDA-Bench, a Hierarchical Benchmark for PPA Convergence and DRC Fixing

arXiv:2605. 06936v3 Announce Type: replace-cross Abstract: LLM-based agents are increasingly applied to the "last mile" of Electronic Design Automation (EDA): repairing residual sign-off Design Rule Check (DRC) violations and converging Power-Performance-Area (PPA) targets after tool runs.

By Pengju Liu, Nuo Xu, Jinwei Tang, Yu Cao, Caiwen Ding
arXiv AI
Aug 5

AgenticECO: An Agentic Framework for ECO on 3D Integrated Circuits

arXiv:2608. 03738v1 Announce Type: new Abstract: As Moore's law slows, the industry is turning to three-dimensional integration; yet in merged 3D-IC flows, routed designs expose bond-level defects with no 2D analogue, and post-route engineering change orders (ECO) remain manual, expertise-bound work.

By Shuo Ren, Yaohui Han, Libo Shen, Zhiqiang Jia, Rongliang Fu, Bei Yu, Tsung-Yi Ho
arXiv AI
Sep 3

Dictionary-Guided Mutation Operators for Automated HDL Repair

The paper introduces a dictionary-guided HDL repair system that uses ANTLR-derived mutation vocabularies and a simulation-divergence fault localization module to generate syntactically valid Verilog mutations. The mutation operator performs token substitutions, insertions, and deletions via regex matching, while the fault localization scores source lines based on proximity to diverging output wires, guiding the search. Evaluated on the CirFix benchmark, the approach achieves correct repairs on 14 bug variants, including a multi-bug case, and outperforms CirFix with an 18x speedup on a two-edit benchmark.

By Maisha Mastora, Dean Sullivan
arXiv AI
Aug 5

Don't Regenerate, Debug: A Domain-Specific Agent for Repairing Near-Miss Hardware Operators

arXiv:2608. 02712v1 Announce Type: cross Abstract: Kernel generation for hardware accelerators such as GPUs and NPUs has become a proving ground for large language models (LLMs), and state-of-the-art systems raise correctness through pipelines that couple LLMs with agentic reinforcement learning and evolutionary search.

By Yansong Sun, Shenxiu Wu, Siyuan Chen, Runlin Hou, Junhao Qiu, Junming Cao, Shudi Shao, Zhichao Lu, Qingfu Zhang
arXiv AI
Sep 15

Externalizing Requirement-to-Repair Artifacts as Observable Traces for LLM-Based Program Repair

The paper introduces THEMIS, a stage-aware repair workflow that externalizes the requirement-to-repair process by generating semantic interpretations, a runtime requirement-code graph, graph-derived developer guidance, retained repair rationale and patches, and post-edit audit records. A retrospective audit of 300 SWE-bench Lite cases shows that these artifacts enable cross-stage inspection, with a complete developer rationale available for 288 cases and 214 cases retaining a full audited field set. The retained records also allow systematic measurement of cross-stage correspondence, revealing high recurrence of target symbols across rationales and patches, and a preliminary improvement in resolving cases compared to a direct same-input condition.

By Zewen Tao, Shin-nosuke Ishikawa