mlsanityData to 14 Oct 2026

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

arXiv 2603.25768 ↗First posted 26 Mar 2026cs.SE cs.AI cs.ARcited by 2

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

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 18 of its 41 references that are in the map. First posted on arXiv 26 Mar 2026.

Builds on

18 of 41 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025A Multi-Agent Generative AI Framework for IC Module-Level Verification Automation29 Jul
  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

Other references · 15

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2024StarCoder 2 and The Stack v2: The Next Generationcited by 502
  4. 2025Multi-SWE-bench: A Multilingual Benchmark for Issue Resolvingcited by 171
  5. 2023RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Modelscited by 100
  6. 2024OriGen:Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflectioncited by 56
  7. 2024Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Taskscited by 52
  8. 2024Towards LLM-Powered Verilog RTL Assistant: Self-Verification and Self-Correctioncited by 39
  9. 2024CodeV: Empowering LLMs with HDL Generation through Multi-Level Summarizationcited by 38
  10. 2025QiMeng-CodeV-R1: Reasoning-Enhanced Verilog Generationcited by 35
  11. 2024Do LLMs "know" internally when they follow instructions?cited by 26
  12. 2024LLM-Aided Testbench Generation and Bug Detection for Finite-State Machinescited by 17
3 more
  1. 2025From Concept to Practice: an Automated LLM-aided UVM Machine for RTL Verificationcited by 9
  2. 2025Understanding and Mitigating Errors of LLM-Generated RTL Codecited by 8
  3. 2025Are LLMs Ready for Practical Adoption for Assertion Generation?cited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026GoGoTB: Agentic RTL Verification with Specification-Grounded Coverage Closure
  2. 2026AgentDV: Closed-Loop Agentic AI for Hardware Design Verification
  3. 2026Open-Source LLM-Driven Formal Verification: A Multi-Agent Pipeline for RTL Repair
  4. 2026Back to the Future: Rethinking EDA Infrastructure for Agentic Systems in Chip Design Verification
  5. 2026Shift-Left High-Level Synthesis Verification via Knowledge-Augmented LLM Agent ↗
  6. 2025ASIC-Agent: An Autonomous Multi-Agent System for ASIC Design with Benchmark Evaluation
  7. 2026LLM-based Hardware Development with Hierarchical IRs and End-to-End Multi-Agent Workflow
  8. 2025Hey AI, Generate Me a Hardware Code! Agentic AI-based Hardware Design & Verification