UCAgent: An End-to-End Agent for Block-Level Functional Verification

Computer Science > Software Engineering [Submitted on 26 Mar 2026] UCAgent: An End-to-End Agent for Block-Level Functional Verification Junyue Wang, Zhicheng Yao, Yan Pi, Xiaolong Li, Fangyuan Song, Jinru Wang, Yunlong Xie, Sa Wang, Yungang Bao Functional verification remains a critical bottleneck in modern IC development cycles, accounting for approximately 70% of total development time in many projects. However, traditional methods, including constrained-random and formal verification, struggle to keep pace with the growing complexity of modern semiconductor designs. While recent advances in Large Language Models (LLMs) have shown promise in code generation and task automation, significant challenges hinder the realization of end-to-end functional verification automation. These challenges include (i) limited accuracy in generating Verilog/SystemVerilog verification code, (ii) the fragility of LLMs when executing complex, multi-step verification workflows, and (iii) the difficulty of maintaining verification consistency across specifications, coverage models, and test cases throughout the workflow. To address these challenges, we propose UCAgent, an end-to-end agent that automates hardware block-level functional verification based on three core mechanisms. First, we establish a pure Python verification environment using Picker and Toffee to avoid relying on LLM-generated SystemVerilog verification code. Second, we introduce a configurable 31-stage fine-grained verification workflow to guide the LLM, where each stage is verified by an automated checker. Furthermore, we propose a Verification Consistency Labeling Mechanism (VCLM) that assigns hierarchical labels to LLM-generated artifacts, improving the reliability and traceability of verification. Experimental results show that UCAgent can complete end-to-end automated verification on multiple modules, including the UART, FPU, and integer divider modules, achieving up to 98.5% code coverage and up to 100% functional coverage. UCAgent also discovers previously unidentified design defects in realistic designs, demonstrating its practical potential. Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Multiagent Systems (cs.MA) Cite as: arXiv:2603.25768 [cs.SE]   (or arXiv:2603.25768v1 [cs.SE] for this version)   https://doi.org/10.48550/arXiv.2603.25768 Focus to learn more Submission history From: Junyue Wang [view email] [v1] Thu, 26 Mar 2026 07:21:27 UTC (1,121 KB) Access Paper: HTML (experimental) view license Current browse context: cs.SE < prev   |   next > new | recent | 2026-03 Change to browse by: cs cs.AI cs.AR cs.MA References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)