VeriTrace: Human-Like Temporal Exploration Completes Agentic Action Space
Quick Answer
VeriTrace introduces Agentic Temporal Exploration, enabling a multi-agent system to achieve 100% accuracy on VerilogEval-V2 by expanding the debugging action space.
Quick Take
This innovation allows agents to form hypotheses and iteratively refine their understanding, outperforming the previous baseline by 5.1%.
Key Points
- VeriTrace achieves 100% Pass@1 on VerilogEval-V2, marking a breakthrough in functional correctness.
- The system allows independent control over signal selection, time-window bounds, and iteration depth.
- Agentic Temporal Exploration mirrors human verification processes, enhancing debugging capabilities.
- VeriTrace outperforms the strongest baseline by 5.1%, closing the accuracy gap in .
- Existing systems limit debugging to narrow views, hindering effective root-cause analysis.
DeepSignal Analysis
What happened
VeriTrace has been introduced as a multi-agent system that enhances debugging capabilities in Verilog RTL generation. It achieves 100% accuracy on the VerilogEval-V2 benchmark by expanding the debugging action space, allowing agents to explore hypotheses and refine their understanding iteratively. This marks a significant improvement over previous systems, which plateaued at approximately 95% accuracy.
Key evidence
- VeriTrace operates over a complete debugging action space, allowing independent control over signal selection and time-window bounds.
- The system achieved 100% Pass@1 on VerilogEval-V2, marking it as the first to attain perfect functional correctness on this benchmark.
- VeriTrace outperformed the strongest reproduced baseline by 5.1%, demonstrating the effectiveness of its debugging agency.
Why it matters
The introduction of Agentic Temporal Exploration in VeriTrace represents a shift from traditional debugging methods, which often rely on limited pattern matching. By enabling a more exploratory approach, it mirrors the processes used by human verification engineers, potentially leading to more robust and accurate debugging in automated systems. This advancement could influence future developments in AI-driven verification tools.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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.
| Comments: | ICLAD 2026, Long Oral |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.02878 [cs.AI] |
| (or arXiv:2608.02878v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2608.02878 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yu-Tung Liu [view email]
[v1]
Mon, 3 Aug 2026 21:00:58 UTC (829 KB)
— Originally published at arxiv.org
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising
HOBA (Hierarchical On-policy Bidding Agents) is a novel hierarchical reinforcement learning framework that enhances online advertising bidding systems by improving adaptability and reducing hyperparameter tuning costs. It utilizes a for hyperparameter inference, a SARSA agent for expert model selection, and a dynamic expert pool for bid execution, achieving a +3.6% increase in target cost during large-scale deployment and outperforming state-of-the-art baselines on AuctionNet.