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arxiv: 2604.15388 · v1 · submitted 2026-04-16 · 💻 cs.AR · cs.AI

Exploring LLM-based Verilog Code Generation with Data-Efficient Fine-Tuning and Testbench Automation

Mu-Chi Chen , Po-Hsuan Huang , Yu-Hung Kao , Yen-Fu Liu , Yu-Kai Hung , Cheng Liang , Shao-Chun Ho , Chia-Heng Tu
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Shih-Hao Hung
This is my paper

Pith reviewed 2026-05-10 09:23 UTC · model grok-4.3

classification 💻 cs.AR cs.AI
keywords LLM code generationVerilogtestbench automationfine-tuningmulti-agent modelshardware description languagesdata efficiency
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The pith

Automating testbench creation with multi-agent models lets fine-tuned LLMs match state-of-the-art Verilog generation performance while using less training data.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows how large language models can generate Verilog hardware code from specifications, but highlights the shortage of suitable training data and testbenches. It proposes a workflow that deploys multiple cooperating agents to automatically produce testbenches, thereby creating high-quality fine-tuning datasets. The resulting fine-tuned model reaches performance levels comparable to leading methods on the refined VerilogEval v2 benchmark, yet requires substantially fewer training examples. A sympathetic reader would care because this lowers the cost of applying LLMs to hardware design, where both data scarcity and verification demands are high.

Core claim

By using multi-agent models to automate testbench generation, the workflow produces effective fine-tuning data for the specification-to-Verilog task. The fine-tuned model then achieves performance comparable to state-of-the-art methods on the refined VerilogEval v2 benchmark while using less training data. The study therefore supplies a practical basis for future LLM-based HDL generation and automated verification.

What carries the argument

Multi-agent LLM workflow that generates testbenches to create data-efficient fine-tuning sets for specification-to-Verilog models.

Load-bearing premise

The multi-agent models must reliably produce high-quality, correct, and unbiased testbenches that do not introduce errors or coverage gaps into the fine-tuning data.

What would settle it

An evaluation in which the model fine-tuned on the automatically generated testbenches shows clearly lower accuracy on the VerilogEval v2 benchmark than state-of-the-art models trained on larger human-curated datasets, or manual inspection reveals systematic flaws in the generated testbenches.

Figures

Figures reproduced from arXiv: 2604.15388 by Cheng Liang, Chia-Heng Tu, Mu-Chi Chen, Po-Hsuan Huang, Shao-Chun Ho, Shih-Hao Hung, Yen-Fu Liu, Yu-Hung Kao, Yu-Kai Hung.

Figure 1
Figure 1. Figure 1: Proposed workflow for Verilog code training data generation. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Multi-agent framework for testbench generation. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Multi-agent framework with pregenerating testbench. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
read the original abstract

Recent advances in large language models have improved code generation, but their use in hardware description languages is still limited. Moreover, training data and testbenches for these models are often scarce. This paper presents a workflow that uses multi-agent models to generate testbenches for high-quality fine-tuning data. By automating testbench creation, the fine-tuned model for the specification-to-Verilog task achieves performance comparable to state-of-the-art methods on the refined VerilogEval v2 benchmark while using less training data. This study provides a basis for future work on LLM-based HDL generation and automated verification.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper presents a workflow using multi-agent LLMs to automatically generate testbenches, creating data for fine-tuning models on specification-to-Verilog generation. It claims this data-efficient approach yields performance comparable to state-of-the-art methods on the refined VerilogEval v2 benchmark while using less training data.

Significance. If the central claims hold with proper validation, the work would be significant for enabling more accessible LLM-based HDL design by mitigating data scarcity through automated testbench creation, with potential impact on hardware verification automation.

major comments (2)
  1. [Abstract] Abstract: The headline claim of 'achieves performance comparable to state-of-the-art methods on the refined VerilogEval v2 benchmark while using less training data' is presented without any quantitative metrics, tables, ablation results, or error analysis, which is load-bearing for assessing whether the data-efficient fine-tuning actually succeeds.
  2. [Workflow / Testbench Generation] Testbench automation section (implied in workflow description): The multi-agent testbench generation is asserted to produce 'high-quality' fine-tuning data, but no validation metrics (e.g., functional pass rates vs. golden references, stimulus coverage percentages, or bias analysis) are reported; this directly affects whether the downstream performance comparison can be trusted.
minor comments (1)
  1. [Abstract] The phrase 'refined VerilogEval v2' is used without a citation or definition of what refinements were applied, reducing clarity for readers unfamiliar with the benchmark.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights opportunities to better substantiate our claims. We address each major comment below and will incorporate revisions to strengthen the manuscript's clarity and evidential support.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The headline claim of 'achieves performance comparable to state-of-the-art methods on the refined VerilogEval v2 benchmark while using less training data' is presented without any quantitative metrics, tables, ablation results, or error analysis, which is load-bearing for assessing whether the data-efficient fine-tuning actually succeeds.

    Authors: We agree that the abstract would be strengthened by including quantitative metrics to support the headline claim. In the revised manuscript, we will update the abstract to report specific results from our experiments, including the pass rates (e.g., pass@1 and pass@5) achieved by the fine-tuned model on VerilogEval v2 relative to state-of-the-art baselines, as well as the exact reduction in training data volume. We will also reference the relevant tables and ablation studies from the results section to provide immediate context for readers. revision: yes

  2. Referee: [Workflow / Testbench Generation] Testbench automation section (implied in workflow description): The multi-agent testbench generation is asserted to produce 'high-quality' fine-tuning data, but no validation metrics (e.g., functional pass rates vs. golden references, stimulus coverage percentages, or bias analysis) are reported; this directly affects whether the downstream performance comparison can be trusted.

    Authors: The referee correctly notes that direct validation metrics for the generated testbenches are not explicitly reported, which limits assessment of data quality. While the downstream performance gains on VerilogEval v2 provide indirect evidence of utility, we will add a dedicated subsection in the revised workflow description. This will include quantitative validation such as the functional pass rate of generated testbenches against golden references, stimulus coverage percentages, and a brief bias analysis of the produced data. These additions will be supported by new tables or figures as appropriate. revision: yes

Circularity Check

0 steps flagged

No significant circularity; claims rest on external benchmark comparison

full rationale

The paper describes a workflow that generates testbenches via multi-agent LLMs, uses them to create fine-tuning data, trains a model for spec-to-Verilog, and reports performance on the public VerilogEval v2 benchmark against external SOTA methods. No step reduces by construction to its own inputs: the benchmark results are measured outcomes on held-out external data rather than fitted parameters renamed as predictions, and no load-bearing premise relies on self-citation chains or self-defined uniqueness theorems. The derivation chain (data generation → fine-tuning → external eval) is independent of the reported numbers and does not exhibit self-definitional, fitted-input, or ansatz-smuggling patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based solely on the abstract, the central claim rests on the unstated premise that multi-agent LLM collaboration can produce reliable testbenches; no explicit free parameters, axioms, or invented entities are described.

pith-pipeline@v0.9.0 · 5425 in / 1121 out tokens · 60814 ms · 2026-05-10T09:23:15.953701+00:00 · methodology

discussion (0)

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