mlsanityData to 14 Oct 2026

VeriTrace: Human-Like Temporal Exploration Completes Agentic Action Space

arXiv 2608.02878 ↗First posted 3 Aug 2026cs.AIcited by 0

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

Abstract (arXiv)

Large language models have shown promise for automated Verilog RTL generation, yet state-of-the-art multi-agent systems plateau at ~95% accuracy on standard benchmarks. We trace this ceiling to an incomplete debugging action space: existing systems restrict which signals the agent can inspect, which time windows it can query, or both, reducing debugging to pattern matching on a narrow, predetermined view of circuit behavior rather than hypothesis-driven root-cause analysis. We present VeriTrace, a multi-agent system whose Inspector agent operates over a complete debugging action space, with independent control over signal selection, time-window bounds, and iteration depth. This capability, which we term Agentic Temporal Exploration, enables the agent to form hypotheses about failure causes, query the waveform for evidence, and refine its understanding iteratively, mirroring the exploratory process of human verification engineers. VeriTrace achieves 100% Pass@1 on VerilogEval-V2, the first system to attain perfect functional correctness on this benchmark. On a shared Claude Sonnet 4.0 backbone, VeriTrace outperforms the strongest reproduced baseline by +5.1%, demonstrating that debugging agency closes the final accuracy gap.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 29 of its 38 references that are in the map. First posted on arXiv 3 Aug 2026.

Builds on

29 of 38 references.

Closest ancestors · 8

Same direction, newest first

  1. 2026ACE-RTL: When Agentic Context Evolution Meets RTL-Specialized LLMs10 Feb
  2. 2025Comprehensive Verilog Design Problems: A Next-Generation Benchmark Dataset for Evaluating Large Language Models and Agents on RTL Design and Verification17 Jun
  3. 2024VerilogCoder: Autonomous Verilog Coding Agents with Graph-based Planning and Abstract Syntax Tree (AST)-based Waveform Tracing Tool15 Aug
  4. 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agents23 Jul
  5. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering6 May
  6. 2024Executable Code Actions Elicit Better LLM Agents1 Feb
  7. 2023ChatEDA: A Large Language Model Powered Autonomous Agent for EDA20 Aug
  8. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation16 Aug

Other references · 21

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  3. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  4. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  5. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  6. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,191
  7. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,808
  8. 2023Teaching Large Language Models to Self-Debugcited by 988
  9. 2023VerilogEval: Evaluating Large Language Models for Verilog Code Generationcited by 252
  10. 2023ChipNeMo: Domain-Adapted LLMs for Chip Designcited by 174
  11. 2023RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Modelcited by 148
  12. 2022Benchmarking Large Language Models for Automated Verilog RTL Code Generationcited by 115
9 more
  1. 2023RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Modelscited by 100
  2. 2024BetterV: Controlled Verilog Generation with Discriminative Guidancecited by 100
  3. 2024OriGen:Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflectioncited by 56
  4. 2024Revisiting VerilogEval: A Year of Improvements in Large-Language Models for Hardware Code Generationcited by 45
  5. 2024CraftRTL: High-quality Synthetic Data Generation for Verilog Code Models with Correct-by-Construction Non-Textual Representations and Targeted Code Repaircited by 42
  6. 2025A Survey of Research in Large Language Models for Electronic Design Automationcited by 41
  7. 2024RTL-Repo: A Benchmark for Evaluating LLMs on Large-Scale RTL Design Projectscited by 26
  8. 2025ScaleRTL: Scaling LLMs with Reasoning Data and Test-Time Compute for Accurate RTL Code Generationcited by 22
  9. 2024AIvril: AI-Driven RTL Generation With Verification In-The-Loopcited by 15

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Not cited yet.

Similar, not cited

Close in wording, no citation either way

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  3. 2024MAGE: A Multi-Agent Engine for Automated RTL Code Generation
  4. 2025VeriLA: A Human-Centered Evaluation Framework for Interpretable Verification of LLM Agent Failures
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