arXiv AI
Jul 23
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.
DRC-Aid: Design-Rule Correction via Agentic Framework utilizing Inference-Time Large Language Models
arXiv:2607. 22761v1 Announce Type: cross Abstract: Resolving Design Rule Violations (DRVs) in layouts entails an iterative loop of geometric edits and verification.
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arXiv AI
Jul 15
Multi-Perspective Agentic Program Repair via Code Property Graphs and Temporal Execution Graphs
arXiv:2607. 12605v1 Announce Type: cross Abstract: Large language models (LLMs) have improved automated program repair (APR), but two limitations remain.
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.
arXiv Computer Vision
Sep 25
AgenticCADedit: A Stateful, Tool-Mediated Agentic Approach to Multimodal 3D CAD Editing
AgenticCADedit introduces a stateful, tool‑mediated approach to multimodal 3D CAD editing, transforming the process from generating a single complete program to executing a sequence of incremental, verifiable actions on a persistent CAD state. By committing each step, inspecting geometry, and selectively reverting faulty operations, the method preserves partial progress and builds upon earlier edits. Experiments across three large language models show substantial gains in validity and acceptance, with the weakest baseline model’s validity rising from 51.0% to 94.8% and a token‑cost reduction of 66.7% compared to neuralCAD‑Edit.
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.
arXiv Machine Learning
Jun 5
CASS-RTL: Correctness-Aware Subspace Steering for RTL Generation with LLMs
arXiv:2606. 05680v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have enabled the automatic synthesis (generation) of register-transfer level (RTL) code from natural language instructions, offering a promising pathway to accelerate chip design.
arXiv AI
4d ago
One Readout, Many Repairs: Diffusion-Guided Hierarchical Search for Tool-Agent Repair
arXiv:2609.34879v2 Announce Type: replace Abstract: Tool agents use large language models to act through external tools, yet successfully executed calls can still leave user requests unfulfilled. Too...
arXiv Machine Learning
Jun 25
Polaris: A G\"odel Agent Framework for Small Language Models through Experience-Abstracted Policy Repair
arXiv:2603. 23129v4 Announce Type: replace Abstract: G\"odel agent realize recursive self-improvement: an agent inspects its own policy and traces and then modifies that policy in a tested loop.
arXiv Machine Learning
Jun 24
Polaris: A Godel Agent Framework for Small Language Models through Experience-Abstracted Policy Repair
arXiv:2603. 23129v3 Announce Type: replace Abstract: G\"odel agent realize recursive self-improvement: an agent inspects its own policy and traces and then modifies that policy in a tested loop.
arXiv AI
Aug 5
TraceCAD: Trace-Guided Repair for Agentic CAD Generation
arXiv:2608. 03062v1 Announce Type: new Abstract: LLM-based CAD agents produce executable parametric programs, but their correction loops may lose evidence about satisfied requirements, faulty operations, and prior repairs.
arXiv Machine Learning
Sep 24
ChipMEM: Verification-Grounded Memory for EDA Agents
ChipMEM introduces a verification‑grounded memory layer for electronic design automation agents that combines cross‑task procedural memory with within‑trajectory statistical guidance. The procedural component stores a skill only after it passes synthesis, simulation, or formal checks, while a Bayesian component ranks recovery strategies based on tool‑call outcomes. Experiments on RTLRewriter‑Bench and CVDP tasks show that ChipMEM improves equivalence‑passing outputs and area metrics compared to agents without memory.
arXiv AI
Sep 7
Better Understanding, Better Fixes? A Study of Hallucination in LLM-based Automated Program Repair
The paper investigates hallucination in large language model–based automated program repair (APR). It defines hallucination as producing patches or intermediate artifacts that are not grounded in available repair evidence, and analyzes it across final patches and intermediate tasks such as triggering test case identification, line coverage prediction, and additional test case generation. Experiments on 832 Defects4J bugs show that only 21.0%–55.9% of patches pass the developer test suite, with 72.7% of sampled repairs exhibiting hallucinations, often due to incorrect causal localization or repair strategies.