PRO-V: An Efficient Program Generation Multi-Agent System for Automatic RTL Verification

• URL: http://arxiv.org/abs/2506.12200v1
• Date: Fri, 13 Jun 2025 20:06:34 GMT
• Title: PRO-V: An Efficient Program Generation Multi-Agent System for Automatic RTL Verification
• Authors: Yujie Zhao, Zhijing Wu, Hejia Zhang, Zhongming Yu, Wentao Ni, Chia-Tung Ho, Haoxing Ren, Jishen Zhao,
• Abstract summary: Pro-V is a fully program generation multi-agent system for robust RTL verification.<n>It incorporates an efficient best-of-n iterative sampling strategy to enhance the correctness of generated testbenches.<n>Pro-V attains a verification accuracy of 87.17% on golden RTL implementations and 76.28% on RTL mutants.
• Score: 6.983135183126461
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LLM-assisted hardware verification is gaining substantial attention due to its potential to significantly reduce the cost and effort of crafting effective testbenches. It also serves as a critical enabler for LLM-aided end-to-end hardware language design. However, existing current LLMs often struggle with Register Transfer Level (RTL) code generation, resulting in testbenches that exhibit functional errors in Hardware Description Languages (HDL) logic. Motivated by the strong performance of LLMs in Python code generation under inference-time sampling strategies, and their promising capabilities as judge agents, we propose PRO-V a fully program generation multi-agent system for robust RTL verification. Pro-V incorporates an efficient best-of-n iterative sampling strategy to enhance the correctness of generated testbenches. Moreover, it introduces an LLM-as-a-judge aid validation framework featuring an automated prompt generation pipeline. By converting rule-based static analysis from the compiler into natural language through in-context learning, this pipeline enables LLMs to assist the compiler in determining whether verification failures stem from errors in the RTL design or the testbench. PRO-V attains a verification accuracy of 87.17% on golden RTL implementations and 76.28% on RTL mutants. Our code is open-sourced at https://github.com/stable-lab/Pro-V.

Related papers

• DecoRTL: A Run-time Decoding Framework for RTL Code Generation with LLMs [0.0]
  We show that large language models (LLMs) exhibit low confidence in regions of structural ambiguity or semantic complexity.<n>We introduce DecoRTL, a novel run-time decoding strategy, that is both syntax-aware and contrastive for RTL code generation.<n>Our approach operates entirely at inference time without requiring any additional model fine-tuning.
  arXiv  Detail & Related papers  (2025-07-03T01:17:44Z)
• Training Language Models to Generate Quality Code with Program Analysis Feedback [66.0854002147103]
  Code generation with large language models (LLMs) is increasingly adopted in production but fails to ensure code quality.<n>We propose REAL, a reinforcement learning framework that incentivizes LLMs to generate production-quality code.
  arXiv  Detail & Related papers  (2025-05-28T17:57:47Z)
• TuRTLe: A Unified Evaluation of LLMs for RTL Generation [0.6010802600885173]
  We propose TuRTLe, a unified evaluation framework designed to assess LLMs across key RTL generation tasks.<n>We benchmark a diverse set of open LLMs and analyze their strengths and weaknesses in EDA-specific tasks.<n>Our results show that reasoning-based models, such as DeepSeek R1, consistently outperform others across multiple evaluation criteria.
  arXiv  Detail & Related papers  (2025-03-31T07:43:12Z)
• Real-time Verification and Refinement of Language Model Text Generation [60.04718679054704]
  Large language models (LLMs) have shown remarkable performance across a wide range of natural language tasks.<n>A critical challenge remains in that they sometimes generate factually incorrect answers.<n>We propose Streaming-VR, a novel approach designed to enhance the efficiency of verification and refinement of LLM outputs.
  arXiv  Detail & Related papers  (2025-01-14T03:59:48Z)
• RTLSquad: Multi-Agent Based Interpretable RTL Design [3.1734541757969463]
  Large Language Models (LLMs) offer new approaches for automatic RTL code generation and optimization.<n>To address this, we propose RTLSquad, a novel LLM-Based Multi-Agent system for interpretable RTL code generation.
  arXiv  Detail & Related papers  (2025-01-06T02:57:54Z)
• EDA-Aware RTL Generation with Large Language Models [0.7831852829409273]
  Large Language Models (LLMs) have become increasingly popular for generating RTL code.<n> producing error-free RTL code in a zero-shot setting remains highly challenging for even state-of-the-art LLMs.<n>We introduce AIvril2, a self-verifying, LLM-agnostic agentic framework aimed at enhancing RTL code generation through iterative corrections of both syntax and functional errors.
  arXiv  Detail & Related papers  (2024-11-21T00:37:51Z)
• Provenance: A Light-weight Fact-checker for Retrieval Augmented LLM Generation Output [49.893971654861424]
  We present a light-weight approach for detecting nonfactual outputs from retrieval-augmented generation (RAG) We compute a factuality score that can be thresholded to yield a binary decision. Our experiments show high area under the ROC curve (AUC) across a wide range of relevant open source datasets.
  arXiv  Detail & Related papers  (2024-11-01T20:44:59Z)
• AIvril: AI-Driven RTL Generation With Verification In-The-Loop [0.7831852829409273]
  Large Language Models (LLMs) are computational models capable of performing complex natural language processing tasks. This paper introduces AIvril, a framework designed to enhance the accuracy and reliability of RTL-aware LLMs.
  arXiv  Detail & Related papers  (2024-09-03T15:07:11Z)
• MEIC: Re-thinking RTL Debug Automation using LLMs [18.964523115622928]
  This work introduces a novel framework, Make Each Iteration Count (MEIC) MEIC is suitable for identifying and correcting both syntax and function errors. To evaluate our framework, we provide an open-source dataset comprising 178 common RTL programming errors.
  arXiv  Detail & Related papers  (2024-05-10T22:32:39Z)
• LLatrieval: LLM-Verified Retrieval for Verifiable Generation [67.93134176912477]
  Verifiable generation aims to let the large language model (LLM) generate text with supporting documents. We propose LLatrieval (Large Language Model Verified Retrieval), where the LLM updates the retrieval result until it verifies that the retrieved documents can sufficiently support answering the question. Experiments show that LLatrieval significantly outperforms extensive baselines and achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2023-11-14T01:38:02Z)
• Assessing the Reliability of Large Language Model Knowledge [78.38870272050106]
  Large language models (LLMs) have been treated as knowledge bases due to their strong performance in knowledge probing tasks. How do we evaluate the capabilities of LLMs to consistently produce factually correct answers? We propose MOdel kNowledge relIabiliTy scORe (MONITOR), a novel metric designed to directly measure LLMs' factual reliability.
  arXiv  Detail & Related papers  (2023-10-15T12:40:30Z)
• LEVER: Learning to Verify Language-to-Code Generation with Execution [64.36459105535]
  We propose LEVER, a simple approach to improve language-to-code generation by learning to verify the generated programs with their execution results. Specifically, we train verifiers to determine whether a program sampled from the LLMs is correct or not based on the natural language input, the program itself and its execution results. LEVER consistently improves over the base code LLMs(4.6% to 10.9% with code-davinci) and achieves new state-of-the-art results on all of them.
  arXiv  Detail & Related papers  (2023-02-16T18:23:22Z)

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.