VeriTrace: Human-Like Temporal Exploration Completes Agentic Action Space

• URL: http://arxiv.org/abs/2608.02878v1
• Date: Mon, 03 Aug 2026 21:00:58 GMT
• Title: VeriTrace: Human-Like Temporal Exploration Completes Agentic Action Space
• Abstract summary: 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.<n>We present VeriTrace, a multi-agent system whose Inspector agent operates over a complete debug action space.<n>VeriTrace achieves 100% Pass@1 on VerilogEval-V2, the first system to attain perfect functional correctness on this benchmark.
• Score: 4.069939236366667
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Leveraging Trajectory Graphs for Pre-Execution Error Diagnosis in Agentic LLM Systems [47.66236392009794]
  Large Language Model(LLM)-based agents have demonstrated exceptional performance across a wide range of complex interactive tasks.<n>We propose textitTrajectory Graph Copilot, a novel framework that acts as a copilot'' for LLM agents by diagnosing potential action errors before they are executed.
  arXiv  Detail & Related papers  (2026-07-29T20:14:43Z)
• StainFlow: Entity-Stain Tracking and Evidence Linking for Process Rewards in GUI Agents [67.03593791535786]
  Reinforcement Learning (RL) has become a promising approach for improving GUI Agents in long-horizon, digital environments.<n>To mitigate this issue, recent studies introduce Process Reward Models (PRMs)<n>PRMs provide finer-grained training feedback through global milestone verification or local step-level evaluation.<n>We propose StainFlow, an entity-stain-flow process reward model for GUI Agents.
  arXiv  Detail & Related papers  (2026-06-05T08:17:28Z)
• Towards Self-Improving Error Diagnosis in Multi-Agent Systems [31.04613892300063]
  We introduce ErrorProbe, a self-improving framework for semantic failure attribution.<n>ErrorProbe identifies responsible agents and the originating error step.<n>It maintains a verified episodic memory that updates only when error patterns are confirmed by executable evidence.
  arXiv  Detail & Related papers  (2026-04-19T23:13:05Z)
• AgentTrace: Causal Graph Tracing for Root Cause Analysis in Deployed Multi-Agent Systems [0.0]
  We present AgentTrace, a lightweight causal tracing framework for post-hoc failure diagnosis in deployed multi-agent AI systems.<n>AgentTrace reconstructs causal graphs from execution logs, traces backward from error manifestations, and ranks candidate root causes using interpretable structural and positional signals.<n>Our results suggest that causal tracing provides a practical foundation for improving the reliability and trustworthiness of agentic systems in the wild.
  arXiv  Detail & Related papers  (2026-03-16T00:46:44Z)
• AgentProcessBench: Diagnosing Step-Level Process Quality in Tool-Using Agents [50.481033105867205]
  We introduce AgentProcessBench, the first benchmark dedicated to evaluating step-level effectiveness in realistic, tool-augmented trajectories.<n>The benchmark comprises 1,000 diverse trajectories and 8,509 human-labeled step annotations with 89.1% inter-annotator agreement.<n>It features a ternary labeling scheme to capture exploration and an error propagation rule to reduce labeling ambiguity.
  arXiv  Detail & Related papers  (2026-03-15T16:13:58Z)
• DoVer: Intervention-Driven Auto Debugging for LLM Multi-Agent Systems [48.971606069204825]
  DoVer is an intervention-driven debug framework for large language model (LLM)-based multi-agent systems.<n>It augments hypothesis generation with active verification through targeted interventions.<n>DoVer flips 18-28% of failed trials into successes, achieves up to 16% milestone progress, and validates or refutes 30-60% of failure hypotheses.
  arXiv  Detail & Related papers  (2025-12-07T09:23:48Z)
• InspectCoder: Dynamic Analysis-Enabled Self Repair through interactive LLM-Debugger Collaboration [71.18377595277018]
  Large Language Models (LLMs) frequently generate buggy code with complex logic errors that are challenging to diagnose.<n>We present InspectCoder, the first agentic program repair system that empowers LLMs to actively conduct dynamic analysis via interactive debugger control.
  arXiv  Detail & Related papers  (2025-10-21T06:26:29Z)
• Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction [58.51530390018909]
  Large Language Model based multi-agent systems excel at collaborative problem solving but remain brittle to cascading errors.<n>We present MASC, a metacognitive framework that endows MAS with real-time, unsupervised, step-level error detection and self-correction.
  arXiv  Detail & Related papers  (2025-10-16T05:35:37Z)
• Interpretable Early Failure Detection via Machine Learning and Trace Checking-based Monitoring [9.565145785280452]
  We develop a framework for interpretable early failure detection based on vectorized trace checking.<n>The framework shows a 2-10% net improvement in key performance metrics compared to the state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-08-25T08:30:01Z)
• A Unified Debugging Approach via LLM-Based Multi-Agent Synergy [39.11825182386288]
  FixAgent is an end-to-end framework for unified debug through multi-agent synergy. It significantly outperforms state-of-the-art repair methods, fixing 1.25$times$ to 2.56$times$ bugs on the repo-level benchmark, Defects4J.
  arXiv  Detail & Related papers  (2024-04-26T04:55:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.