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arXiv cs.AI· Jiale Liu, Taiyu Zhou, Tianqi Jiang·· 12 小时前AI 评分29

Veri-Sure:结合时序追踪、切片与形式化验证的多智能体 RTL 代码生成框架

Veri-Sure: Multi-Agent RTL Code Generation with Temporal Tracing, Slicing and Formal Verification

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Veri-Sure 是一个多智能体 RTL 代码生成框架,通过设计契约对齐各智能体意图,并用静态依赖切片引导的补丁机制做局部修复,避免整文件重生成带来的回归与修复幻觉。

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Abstract:In the rapidly evolving field of Electronic Design Automation (EDA), the deployment of Large Language Models (LLMs) for Register-Transfer Level (RTL) code generation has emerged as a promising direction. However, because RTL code describes concurrent hardware behavior with strict cycle-level timing and thus differs fundamentally from software code, LLM-based RTL code generation remains bottlenecked by (i) sparse feedback and limited test coverage of simulation-centric evaluation, (ii) regressions and repair hallucinations introduced by whole-file regeneration during debugging, and (iii) semantic drift as intent is reinterpreted across repair iterations and agent handoffs. In this work, we propose Veri-Sure, a multi-agent framework that establishes a design contract to align agents' intent and uses a patching mechanism guided by static dependency slicing to perform precise, localized repairs. By integrating a multi-branch verification pipeline that combines trace-driven temporal analysis with formal verification, Veri-Sure yields more robust and reliable RTL code generation. We also introduce VerilogEval-v2-EXT, extending the original benchmark with 53 more industrial-grade design tasks and stratified difficulty levels, and show that Veri-Sure achieves state-of-the-art verified-correct RTL code generation performance, surpassing standalone LLMs and prior agentic systems. Code and dataset are available at this https URL.
Comments: NeurIPS 2026
Subjects: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
Cite as: arXiv:2601.19747 [cs.AR]
  (or arXiv:2601.19747v2 [cs.AR] for this version)
  https://doi.org/10.48550/arXiv.2601.19747

arXiv-issued DOI via DataCite

Submission history

From: Jiale Liu [view email]
[v1] Tue, 27 Jan 2026 16:10:23 UTC (1,552 KB)
[v2] Thu, 15 Oct 2026 18:44:39 UTC (1,545 KB)

来源:arXiv cs.AI · arxiv.org