EDATracer: An Agentic Framework for Large-Scale EDA Artifact Analysis

arXiv 2608.04032 ↗First posted 2 Aug 2026cs.AR cs.AI cs.MAcited by 0

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

Modern chip design relies on electronic design automation (EDA) tools that generate large, heterogeneous artifacts, including source files, scripts, logs, netlists, and reports. Analyzing these artifacts is critical for debugging, optimization, and design-flow understanding, but remains difficult because relevant evidence is often distributed across many artifact types and design stages. Although LLM agents show promise for EDA assistance, existing approaches lack public benchmarks for large-scale cross-artifact analysis and often struggle to ground reasoning in tool-generated evidence. We present EDATracer, an agentic framework for evidence-grounded EDA artifact analysis. EDATracer organizes design artifacts into a knowledge graph paired with a semantic vector index, enabling LLM agents to retrieve evidence across source files, logs, netlists, and reports. We curate an 18.9 GB dataset of 2,787 synthesizable open-source chip designs and introduce a 90-question benchmark spanning factual, statistical, and reasoning tasks. Across evaluated agents, EDATracer achieves the best pass@1 accuracy, outperforming Cursor and Claude Code by 6.4 and 7.2 percentage points on average, while using 2.0-3.2x fewer tokens.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 17 of its 40 references that are in the map. First posted on arXiv 2 Aug 2026.

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2025AutoEDA: Enabling EDA Flow Automation through Microservice-Based LLM Agents1 Aug

Other references · 16

2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
2021Evaluating Large Language Models Trained on Codecited by 8,053
2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
2024MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queriescited by 253
2023VeriGen: A Large Language Model for Verilog Code Generationcited by 197
2024AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agentscited by 100
2021Machine Learning for Electronic Design Automation: A Surveycited by 71
2023RTLCoder: Outperforming GPT-3.5 in Design RTL Generation with Our Open-Source Dataset and Lightweight Solutioncited by 69
2024Automatically Improving LLM-based Verilog Generation using EDA Tool Feedbackcited by 18
2025SymRTLO: Enhancing RTL Code Optimization with LLMs and Neuron-Inspired Symbolic Reasoningcited by 16
2024Reinforcement Learning Policy as Macro Regulator Rather than Macro Placercited by 10

4 more

2026Autonomous Evolution of EDA Tools: Multi-Agent Self-Evolved ABCcited by 6
2025Towards LLM-based Root Cause Analysis of Hardware Design Failurescited by 4
2025Free and Fair Hardware: A Pathway to Copyright Infringement-Free Verilog Generation using LLMscited by 2
2026LAUDE: LLM-Assisted Unit Test Generation and Debugging of Hardware DEsignscited by 1

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