arxiv: 2604.14709 · v3 · submitted 2026-04-16 · 💻 cs.AI

Recognition: unknown

HWE-Bench: Benchmarking LLM Agents on Real-World Hardware Bug Repair Tasks

Fan Cui , Hongyuan Hou , Zizhang Luo , Chenyun Yin , Yun Liang

Authors on Pith no claims yet

Pith reviewed 2026-05-10 11:23 UTC · model grok-4.3

classification 💻 cs.AI

keywords LLM agentshardware bug repairbenchmarkVerilogChiselRISC-Vfault localizationrepository-level evaluation

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The pith

LLM agents repair 70.7% of real hardware bugs from open-source projects, with success falling on complex designs.

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

The paper introduces HWE-Bench as the first repository-scale benchmark for LLM agents tasked with fixing real hardware bugs. It assembles 417 instances from actual bug-fix pull requests in six open-source hardware projects that use Verilog, SystemVerilog, and Chisel. Evaluation of seven LLMs across four agent frameworks shows the strongest agent succeeds on 70.7% of the tasks. Success exceeds 90% on smaller cores yet falls below 65% on complex SoC projects. Larger differences appear between models than on software benchmarks, and failures trace mainly to fault localization, hardware semantics, and coordinating changes across multiple artifact types.

Core claim

HWE-Bench supplies 417 real-world hardware bug repair tasks extracted from historical pull requests in RISC-V core, SoC, and security projects. The best LLM agent resolves 70.7 percent of these tasks when operating in containerized environments that run the projects' native simulation and regression suites. Performance varies sharply with project complexity, exceeding 90 percent on small cores and dropping below 65 percent on large SoCs. Gaps between models are wider than those reported for software bug repair, and difficulty correlates with scope and bug-type mix rather than file size. Agent errors concentrate in fault localization, hardware-semantic reasoning, and cross-artifact care.

What carries the argument

HWE-Bench benchmark of 417 tasks derived from historical bug-fix pull requests across six hardware projects, executed inside containerized native simulation environments for validation.

If this is right

Current LLM agents can already address a substantial portion of hardware debugging work.
Project scale and the mix of bug types influence success more than the amount of code involved.
Hardware-specific reasoning and multi-file coordination remain key obstacles for further gains.
The benchmark supplies a concrete way to measure progress toward hardware-aware agents.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Specialized training on hardware description languages and simulation traces could narrow performance gaps on larger projects.
The automated extraction pipeline allows the benchmark to grow as new hardware repositories release bug fixes.
Improved agents on this benchmark may shorten iteration times in hardware design by automating routine repairs.

Load-bearing premise

The 417 tasks drawn from historical bug-fix pull requests constitute a representative sample of real hardware debugging problems, and the containerized simulation flows correctly verify that an edit resolves the bug without creating new problems.

What would settle it

An experiment showing that agents achieve comparable success rates on small cores and complex SoCs, or that success correlates strongly with code size rather than project scope and bug-type distribution.

Figures

Figures reproduced from arXiv: 2604.14709 by Chenyun Yin, Fan Cui, Hongyuan Hou, Yun Liang, Zizhang Luo.

**Figure 1.** Figure 1: Overview of a task instance in HWE-Bench. evaluation environment. The benchmark is constructed through a systematic, largely automated pipeline that filters genuine hardware bug fixes from repository noise and establishes reproducible, containerized validation for each instance. We evaluate seven LLMs, both proprietary and open-source, using four agent frameworks across all six repositories. The bestperf… view at source ↗

**Figure 2.** Figure 2: Construction pipeline of HWE-Bench [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Distribution of ground-truth patches in HWE-Bench compared with SWE-bench Verified [8] and SWE-bench Pro [6]. 0% 20% 40% 60% 80% 100% Percentage of cases Overall OpenTitan XiangShan Ibex CVA6 Rocket Chip Caliptra n=417 n=245 n=54 n=35 n=35 n=32 n=16 Logic Spec Interface Config/Integ Timing/Sync SW: HW Config SW: HW Interact SW: FW Logic [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 4.** Figure 4: Distribution of bug categories across the six reposi [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

**Figure 5.** Figure 5: Overall resolved rate of the same set of models across three benchmarks. Scores in (b) are from each model’s official [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗

read the original abstract

Existing benchmarks for hardware design primarily evaluate Large Language Models (LLMs) on isolated, component-level tasks such as generating HDL modules from specifications, leaving repository-scale evaluation unaddressed. We introduce HWE-Bench, the first large-scale, repository-level benchmark for evaluating LLM agents on real-world hardware bug repair tasks. HWE-Bench comprises 417 task instances derived from real historical bug-fix pull requests across six major open-source projects spanning both Verilog/SystemVerilog and Chisel, covering RISC-V cores, SoCs, and security roots-of-trust. Each task is grounded in a fully containerized environment where the agent must resolve a real bug report, with correctness validated through the project's native simulation and regression flows. The benchmark is built through a largely automated pipeline that enables efficient expansion to new repositories. We evaluate seven LLMs with four agent frameworks and find that the best agent resolves 70.7% of tasks overall, with performance exceeding 90% on smaller cores but dropping below 65% on complex SoC-level projects. We observe larger performance gaps across models than commonly reported on software benchmarks, and difficulty is driven by project scope and bug-type distribution rather than code size alone. Our failure analysis traces agent failures to three stages of the debugging process: fault localization, hardware-semantic reasoning, and cross-artifact coordination across RTL, configuration, and verification components, providing concrete directions for developing more capable hardware-aware agents.

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.

Desk Editor's Note private letter to a colleague

HWE-Bench is a practical first repository-level benchmark for hardware bug repair, but its tasks drawn from successful PRs and simulation-based validation need closer checks for bias.

read the letter

HWE-Bench pulls 417 tasks from real historical bug-fix pull requests in six open-source Verilog and Chisel projects, including RISC-V cores and SoCs. The setup runs agents inside containerized copies of each project and checks fixes against the project's own simulation and regression flows. That is the main new piece: moving past isolated module generation to full-repo debugging with native validation tools. They run seven models across four agent frameworks, report 70.7% overall resolution, note bigger spreads than typical software benchmarks, and break failures into fault localization, hardware-semantic reasoning, and cross-file coordination. The automated pipeline for adding more repositories is a useful engineering detail that makes the benchmark expandable. These elements give the work concrete value for measuring agent progress on hardware tasks. The soft spots sit in the task construction. Selecting only bugs that humans eventually fixed in PRs risks under-sampling harder or less-documented cases, which could inflate the headline numbers and make the three-stage failure taxonomy look more general than it is. The native simulation validation is a strength on paper, yet it still depends on whether the existing regression suites catch the original bug's corner cases or side effects; any gaps there would make some reported successes unreliable. The abstract stays high-level on curation criteria, so the full paper needs to show explicit selection rules and any bias checks to support the difficulty claims. This paper is aimed at groups working on LLM agents for hardware design, EDA, or domain-specific code repair. Readers who need a concrete benchmark with real project environments will get usable numbers and categories from it. It deserves a serious referee because the benchmark itself is new and tied to actual repositories, even if the results carry the usual caveats about selection and validation. I would send it for review and ask specifically for more detail on PR filtering and regression coverage.

Referee Report

3 major / 2 minor

Summary. The paper introduces HWE-Bench, the first repository-scale benchmark for LLM agents on hardware bug repair, comprising 417 tasks extracted from historical bug-fix PRs across six open-source Verilog/SystemVerilog and Chisel projects (RISC-V cores, SoCs, security roots-of-trust). Tasks are executed in containerized native simulation environments with correctness checked via project regression flows. Evaluation of seven LLMs across four agent frameworks shows the best agent resolving 70.7% of tasks overall (>90% on small cores, <65% on complex SoCs), with larger model gaps than typical software benchmarks; difficulty correlates with project scope and bug-type distribution rather than code size. Failure analysis attributes errors to three stages: fault localization, hardware-semantic reasoning, and cross-artifact coordination.

Significance. If the task sample is representative and native validation is faithful, the benchmark supplies a much-needed repo-level testbed for hardware agents, quantifies performance drops on realistic SoC-scale bugs, and supplies a concrete three-stage failure taxonomy that can steer future work on hardware-aware reasoning and multi-artifact coordination. The largely automated curation pipeline is also a reusable strength.

major comments (3)

[Task curation (abstract and methods)] The headline resolution rate (70.7%) and the claim that difficulty is driven by project scope/bug-type rather than code size rest on the representativeness of the 417 tasks. The abstract states they are 'derived from real historical bug-fix pull requests,' yet provides only high-level descriptions of selection criteria; because the source PRs are all eventually successful human fixes, the sample may systematically under-represent unfixable or corner-case bugs, directly affecting both aggregate scores and the scope-vs.-size conclusion.
[Evaluation and validation methodology (abstract)] Correctness of proposed edits is validated solely through the projects' native simulation and regression flows. The abstract asserts this 'accurately validate[s] whether a proposed edit truly resolves the bug,' but does not report coverage metrics, whether the original bug report's triggering conditions are re-executed, or checks for side-effects outside the regression suite. Any gaps here would invalidate the per-project performance gaps and the three-stage failure taxonomy.
[Failure analysis] The failure analysis traces errors to 'fault localization, hardware-semantic reasoning, and cross-artifact coordination.' No quantitative breakdown (e.g., percentage of failures per stage) or description of how the taxonomy was derived (manual review protocol, inter-annotator agreement) is supplied, so the diagnostic claim cannot be assessed for robustness.

minor comments (2)

[Abstract] The abstract asserts 'larger performance gaps across models than commonly reported on software benchmarks' without citing the specific software benchmarks or gap sizes used for comparison.
[Evaluation setup] Notation for agent frameworks and model identifiers should be introduced once with a table or explicit list to avoid ambiguity when results are discussed.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below with clarifications and commitments to revision where appropriate.

read point-by-point responses

Referee: [Task curation (abstract and methods)] The headline resolution rate (70.7%) and the claim that difficulty is driven by project scope/bug-type rather than code size rest on the representativeness of the 417 tasks. The abstract states they are 'derived from real historical bug-fix pull requests,' yet provides only high-level descriptions of selection criteria; because the source PRs are all eventually successful human fixes, the sample may systematically under-represent unfixable or corner-case bugs, directly affecting both aggregate scores and the scope-vs.-size conclusion.

Authors: We appreciate the referee raising this point on potential sampling bias. Our choice to use successful historical bug-fix PRs follows the standard practice in repository-level bug repair benchmarks (e.g., SWE-Bench) to ensure each task has a verifiable ground-truth patch that can be automatically validated. This design prioritizes tasks with known human fixes over unfixable cases, for which no reliable evaluation signal exists. We acknowledge that this may under-sample certain corner cases and will expand the Methods section with more granular details on the PR selection, filtering, and extraction criteria. Our empirical analysis of difficulty drivers (project scope, bug-type distribution) is derived from direct measurements across the 417 tasks and holds after controlling for code size; we will add supporting statistics to make this explicit. revision: partial
Referee: [Evaluation and validation methodology (abstract)] Correctness of proposed edits is validated solely through the projects' native simulation and regression flows. The abstract asserts this 'accurately validate[s] whether a proposed edit truly resolves the bug,' but does not report coverage metrics, whether the original bug report's triggering conditions are re-executed, or checks for side-effects outside the regression suite. Any gaps here would invalidate the per-project performance gaps and the three-stage failure taxonomy.

Authors: We agree that additional methodological transparency is warranted. In the revision we will clarify that the native regression flows re-execute the specific test cases and stimuli associated with the original bug report (as documented in the PRs) and verify that no new failures are introduced in the broader suite. We will also report available code coverage figures for the regression suites used. While the projects' maintainers treat these flows as the authoritative validation standard, we recognize that explicit side-effect and coverage details were insufficiently documented and will add them to the Evaluation section. revision: yes
Referee: [Failure analysis] The failure analysis traces errors to 'fault localization, hardware-semantic reasoning, and cross-artifact coordination.' No quantitative breakdown (e.g., percentage of failures per stage) or description of how the taxonomy was derived (manual review protocol, inter-annotator agreement) is supplied, so the diagnostic claim cannot be assessed for robustness.

Authors: The taxonomy was obtained by the authors through manual categorization of failure traces from a sampled subset of unsuccessful agent runs, mapping each error to the earliest stage where the agent diverged from a correct resolution path. We will add a quantitative breakdown (percentages of failures per stage) and a concise description of the review protocol to the revised Failure Analysis section. Formal inter-annotator agreement statistics were not computed, as the analysis was performed by the core team; we will note this limitation and make the categorization criteria fully reproducible. revision: yes

Circularity Check

0 steps flagged

No circularity: benchmark results are direct empirical measurements on external projects

full rationale

The paper introduces HWE-Bench by extracting 417 tasks from historical bug-fix PRs in six independent open-source hardware repositories and validates fixes via each project's native containerized simulation flows. Reported metrics (70.7% overall resolution, >90% on small cores, <65% on SoCs) and the three-stage failure taxonomy are direct counts and qualitative analysis of agent runs against these external ground-truth oracles. No equations, fitted parameters, predictions, or uniqueness theorems appear; no self-citations are load-bearing for the central claims; and no renaming of known results occurs. The derivation chain consists solely of task construction, agent execution, and outcome measurement, all anchored outside the paper's own definitions.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The paper introduces no free parameters, new physical entities, or ad-hoc fitted constants; it rests on standard assumptions about benchmark task extraction and simulation-based validation common to software and hardware repair benchmarks.

axioms (2)

domain assumption Bug fixes from historical pull requests can be isolated as self-contained tasks with sufficient context for an agent to attempt repair.
The benchmark construction pipeline assumes each selected PR yields an independent, solvable repair instance without requiring broader project history.
domain assumption Passing the project's native simulation and regression tests confirms that the bug has been correctly resolved.
Correctness validation depends on the assumption that the simulation environment faithfully reproduces the original bug and its fix.

pith-pipeline@v0.9.0 · 5569 in / 1524 out tokens · 33782 ms · 2026-05-10T11:23:24.801013+00:00 · methodology

discussion (0)

Reference graph

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