Comprehensive Verilog Design Problems: A Next-Generation Benchmark Dataset for Evaluating Large Language Models and Agents on RTL Design and Verification

• URL: http://arxiv.org/abs/2506.14074v1
• Date: Tue, 17 Jun 2025 00:11:13 GMT
• Title: Comprehensive Verilog Design Problems: A Next-Generation Benchmark Dataset for Evaluating Large Language Models and Agents on RTL Design and Verification
• Authors: Nathaniel Pinckney, Chenhui Deng, Chia-Tung Ho, Yun-Da Tsai, Mingjie Liu, Wenfei Zhou, Brucek Khailany, Haoxing Ren,
• Abstract summary: We present the Comprehensive Verilog (CVDP) benchmark, a new dataset and infrastructure to advance research in hardware and verification.<n>CVDP includes 783 problems across task categories, covering verification, debug, generation, alignment, and technical Q&A.<n>Problemes are offered in both non-agent and agentic formats.
• Score: 6.0652877909448835
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present the Comprehensive Verilog Design Problems (CVDP) benchmark, a new dataset and infrastructure to advance LLM and agent research in hardware design and verification. CVDP includes 783 problems across 13 task categories, covering RTL generation, verification, debugging, specification alignment, and technical Q&A authored by experienced hardware engineers. Problems are offered in both non-agentic and agentic formats. The benchmark introduces more realistic and challenging contexts than prior work, with state-of-the-art models achieving no more than 34% pass@1 on code generation. Agentic tasks$\unicode{x2013}$especially those involving RTL reuse and verification$\unicode{x2013}$are particularly difficult. Evaluation uses open-source tools and model scoring infrastructure, with comprehension tasks assessed via BLEU and LLM-based judging. CVDP reveals substantial gaps in current model capabilities, underscoring the need for continued research toward robust, real-world hardware design automation.

Related papers

• TraceLLM: Leveraging Large Language Models with Prompt Engineering for Enhanced Requirements Traceability [4.517933493143603]
  This paper introduces TraceLLM, a framework for enhancing requirements traceability through prompt engineering and demonstration selection.<n>We assess prompt generalization and robustness using eight state-of-the-art LLMs on four benchmark datasets.
  arXiv  Detail & Related papers  (2026-02-01T14:29:13Z)
• Towards Comprehensive Benchmarking Infrastructure for LLMs In Software Engineering [19.584762693453893]
  BEHELM is a holistic benchmarking infrastructure that unifies software-scenario specification with multi-metric evaluation.<n>Our goal is to reduce the overhead currently required to construct benchmarks while enabling a fair, realistic, and future-proof assessment of LLMs in software engineering.
  arXiv  Detail & Related papers  (2026-01-28T21:55:10Z)
• VeriSciQA: An Auto-Verified Dataset for Scientific Visual Question Answering [53.662676566188175]
  A key bottleneck lies in the lack of public, large-scale, high-quality Scientific Visual Question Answering (SVQA) datasets.<n>We propose a verification-centric Generate-then-Verify framework that first generates QA pairs with figure-associated textual context.<n>We instantiate this framework to curate VeriSciQA, a dataset of 20,351 QA pairs spanning 20 scientific domains and 12 figure types.
  arXiv  Detail & Related papers  (2025-11-25T04:14:52Z)
• SWE-Compass: Towards Unified Evaluation of Agentic Coding Abilities for Large Language Models [59.90381306452982]
  evaluating large language models (LLMs) for software engineering has been limited by narrow task coverage, language bias, and insufficient alignment with real-world developer.<n>We introduce SWE-1, a comprehensive benchmark that unifies heterogeneous code-related evaluations into a structured and production-aligned framework.<n>SWE- spans 8 task types, 8 programming scenarios, and 10 programming languages, with 2000 high-quality instances curated from authentic GitHub pull requests.
  arXiv  Detail & Related papers  (2025-11-07T18:01:32Z)
• GUI-360$^\circ$: A Comprehensive Dataset and Benchmark for Computer-Using Agents [59.107657859025586]
  GUI-360$circ$ is a large-scale, comprehensive dataset and benchmark suite designed to advance computer-using agents (CUAs)<n>The released corpus contains over 1.2M executed action steps across thousands of trajectories in popular Windows office applications.<n>The dataset supports three canonical tasks, GUI grounding, screen parsing, and action prediction, and a hybrid GUI+API action space.
  arXiv  Detail & Related papers  (2025-11-06T12:19:02Z)
• Towards Realistic Project-Level Code Generation via Multi-Agent Collaboration and Semantic Architecture Modeling [7.753074942497876]
  We introduce CodeProjectEval, a project-level code generation dataset built from 18 real-world repositories with 12.7 files and 2,388.6 lines of code per task.<n>We propose ProjectGen, a multi-agent framework that decomposes projects into architecture design, skeleton generation, and code filling stages.<n>Experiments show that ProjectGen achieves state-of-the-art performance, passing 52/124 test cases on the small-scale project-level code generation dataset DevBench.
  arXiv  Detail & Related papers  (2025-11-05T12:12:35Z)
• A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System [56.40989626804489]
  This survey provides the first holistic analysis of Large Language Models-powered software engineering.<n>We review over 150 recent papers and propose a taxonomy along two key dimensions: (1) Solutions, categorized into prompt-based, fine-tuning-based, and agent-based paradigms, and (2) Benchmarks, including tasks such as code generation, translation, and repair.
  arXiv  Detail & Related papers  (2025-10-10T06:56:50Z)
• Leveraging Generative Models for Real-Time Query-Driven Text Summarization in Large-Scale Web Search [54.987957691350665]
  Query-Driven Text Summarization (QDTS) aims to generate concise and informative summaries from textual documents based on a given query.<n>Traditional extractive summarization models, based primarily on ranking candidate summary segments, have been the dominant approach in industrial applications.<n>We propose a novel framework to pioneer the application of generative models to address real-time QDTS in industrial web search.
  arXiv  Detail & Related papers  (2025-08-28T08:51:51Z)
• A Serverless Architecture for Real-Time Stock Analysis using Large Language Models: An Iterative Development and Debugging Case Study [0.0]
  This paper documents the design, implementation, and iterative debug of a novel, serverless system for real-time stock analysis.<n>We detail the architectural evolution of the system, from initial concepts to a robust, event-driven pipeline.<n>The final architecture operates at a near-zero cost, demonstrating a viable model for individuals to build sophisticated AI-powered financial tools.
  arXiv  Detail & Related papers  (2025-07-13T11:29:51Z)
• Benchmarking Deep Search over Heterogeneous Enterprise Data [73.55304268238474]
  We present a new benchmark for evaluating a form of retrieval-augmented generation (RAG)<n>RAG requires source-aware, multi-hop reasoning over diverse, sparsed, but related sources.<n>We build it using a synthetic data pipeline that simulates business across product planning, development, and support stages.
  arXiv  Detail & Related papers  (2025-06-29T08:34:59Z)
• Evaluating Large Language Models on Non-Code Software Engineering Tasks [4.381476817430934]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in code understanding and generation.<n>We present the first comprehensive benchmark, which we name Software Engineering Language Understanding' (SELU)<n>SELU covers classification, regression, Named Entity Recognition (NER) and Masked Language Modeling (MLM) targets, with data drawn from diverse sources.
  arXiv  Detail & Related papers  (2025-06-12T15:52:32Z)
• MONAQ: Multi-Objective Neural Architecture Querying for Time-Series Analysis on Resource-Constrained Devices [16.639965422376303]
  We propose MONAQ, a novel framework that reformulates NAS into Multi-Objective Neural Architecture Querying tasks.<n>MonAQ is equipped with multimodal query generation for processing multimodal time-series inputs and hardware constraints.<n> Experiments on fifteen datasets demonstrate that MONAQ-discovered models outperform both handcrafted models and NAS baselines.
  arXiv  Detail & Related papers  (2025-05-15T16:35:33Z)
• Evaluating Large Language Models for Real-World Engineering Tasks [75.97299249823972]
  This paper introduces a curated database comprising over 100 questions derived from authentic, production-oriented engineering scenarios.<n>Using this dataset, we evaluate four state-of-the-art Large Language Models (LLMs)<n>Our results show that LLMs demonstrate strengths in basic temporal and structural reasoning but struggle significantly with abstract reasoning, formal modeling, and context-sensitive engineering logic.
  arXiv  Detail & Related papers  (2025-05-12T14:05:23Z)
• Thinking Longer, Not Larger: Enhancing Software Engineering Agents via Scaling Test-Time Compute [61.00662702026523]
  We propose a unified Test-Time Compute scaling framework that leverages increased inference-time instead of larger models.<n>Our framework incorporates two complementary strategies: internal TTC and external TTC.<n>We demonstrate our textbf32B model achieves a 46% issue resolution rate, surpassing significantly larger models such as DeepSeek R1 671B and OpenAI o1.
  arXiv  Detail & Related papers  (2025-03-31T07:31:32Z)
• OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Models [58.45517851437422]
  Visually-situated text parsing (VsTP) has recently seen notable advancements, driven by the growing demand for automated document understanding.<n>Existing solutions often rely on task-specific architectures and objectives for individual tasks.<n>In this paper, we introduce Omni V2, a universal model that unifies VsTP typical tasks, including text spotting, key information extraction, table recognition, and layout analysis.
  arXiv  Detail & Related papers  (2025-02-22T09:32:01Z)
• MAGE: A Multi-Agent Engine for Automated RTL Code Generation [5.899673582879575]
  MAGE is the first open-source multi-agent AI system designed for robust and accurate Verilog RTL code generation.<n>MAGE achieves a 95.7% rate of syntactic and functional correctness code generation on VerilogEval-Human 2 benchmark.
  arXiv  Detail & Related papers  (2024-12-10T21:53:55Z)
• GUI Agents with Foundation Models: A Comprehensive Survey [91.97447457550703]
  This survey consolidates recent research on (M)LLM-based GUI agents.<n>We identify key challenges and propose future research directions.<n>We hope this survey will inspire further advancements in the field of (M)LLM-based GUI agents.
  arXiv  Detail & Related papers  (2024-11-07T17:28:10Z)
• Revisiting VerilogEval: A Year of Improvements in Large-Language Models for Hardware Code Generation [6.463959200930805]
  We evaluate new commercial and open models since the release of the open-source VerilogEval benchmark.<n>We find measurable improvements in state-of-the-art models.<n>We find that prompt engineering remains crucial for achieving good pass rates.
  arXiv  Detail & Related papers  (2024-08-20T17:58:56Z)
• DesignQA: A Multimodal Benchmark for Evaluating Large Language Models' Understanding of Engineering Documentation [3.2169312784098705]
  This research introduces DesignQA, a novel benchmark aimed at evaluating the proficiency of multimodal large language models (MLLMs) in comprehending and applying engineering requirements in technical documentation. DesignQA uniquely combines multimodal data-including textual design requirements, CAD images, and engineering drawings-derived from the Formula SAE student competition.
  arXiv  Detail & Related papers  (2024-04-11T16:59:54Z)
• Towards Complex Document Understanding By Discrete Reasoning [77.91722463958743]
  Document Visual Question Answering (VQA) aims to understand visually-rich documents to answer questions in natural language. We introduce a new Document VQA dataset, named TAT-DQA, which consists of 3,067 document pages and 16,558 question-answer pairs. We develop a novel model named MHST that takes into account the information in multi-modalities, including text, layout and visual image, to intelligently address different types of questions.
  arXiv  Detail & Related papers  (2022-07-25T01:43:19Z)

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.