Agentic Hardware Design as Repository-Level Code Evolution

• URL: http://arxiv.org/abs/2606.28279v1
• Date: Fri, 26 Jun 2026 17:21:06 GMT
• Title: Agentic Hardware Design as Repository-Level Code Evolution
• Abstract summary: HORIZON is a self-evolving agent framework that treats hardware design as repository-level code evolution.<n>We evaluate our approach on ChipBench, RTLLM, Verilog-Eval, and nine CVDP categories.
• Score: 8.614736536216526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present HORIZON, a self-evolving agent framework that treats hardware design as repository-level code evolution. A Markdown harness is compiled into a project pack containing domain knowledge, an executable evaluator, an acceptance predicate, and a git/runtime policy; a hands-free agent loop then evolves an isolated git worktree, using repository operations for state management, tracing, and replay. This extends prior works of repository-scale self-evolution from EDA software systems, to hardware-design artifacts themselves. We evaluate our approach on ChipBench, RTLLM, Verilog-Eval, and nine CVDP categories, achieving 100\% benchmark completion across all suites with a fully hands-free agentic loop. However, we do not claim that agentic AI for hardware design is solved: these benchmarks are controlled proxies for a much broader engineering problem in chip design. Section~\ref{sec:discuss} examines the limitations of the current study and highlights open research challenges.

Related papers

• DeepRepro: State-Aware Subplanning for Paper-to-Code Reproduction in Evolving Repositories [116.27249583403871]
  We propose a state-aware framework for paper-to-code reproduction based on execution-state-aware subplanning.<n>DeepRepro transforms evolving repository states and runtime feedback into fine-grained implementation subplans.<n>Experiments on PaperBench Code-Dev show that DeepRepro consistently outperforms strong scientific and commercial code-agent baselines.
  arXiv  Detail & Related papers  (2026-08-27T02:54:30Z)
• Loop Engineering: Building Blocks, Adoption, and Impact [9.402135587560347]
  In June 2026, practitioners began to describe a further level called loop engineering.<n>The term spread rapidly, accompanied by bold claims and vocal skepticism, but its adoption in software projects has not been measured.<n>We present an exploratory review of the emerging gray literature, which largely agrees on what a well-engineered loop contains.
  arXiv  Detail & Related papers  (2026-08-22T09:52:23Z)
• Vero: Can AI Agents Build Formally Verified Software Repositories? [45.09790101960906]
  Vero is the first benchmark to evaluate joint implementation and proof at the repository level.<n>It contains 43 multi-module instances from real-world repositories spanning Python, Dafny, Verus, and Coq.<n>To improve benchmark reliability, Vero also includes an audit mechanism where agents are allowed to formally prove unsatisfiability of provided specification.
  arXiv  Detail & Related papers  (2026-08-13T17:41:27Z)
• Self-Evolving Coding Agents [11.43197414613246]
  Self-evolving coding agents can inspect repositories, invoke tools, execute tests, debug failures, and generate patches.<n>We first define self-evolving coding agents and distinguish them from conventional coding agents and general self-evolving agents.<n>We find that executable feedback, repository-level context, and coding trajectories give software engineering a distinctive role as a natural domain for agent self-evolution.
  arXiv  Detail & Related papers  (2026-08-04T09:43:46Z)
• LoopsBench: From Harness Engineering to Loop Engineering in Benchmarking Coding Agent [60.27239351179265]
  LOOPSBENCH is a benchmark for loop engineering in coding agent evaluation.<n>It comprises 112 tasks from authentic sources spanning 8 programming languages and 9 domains.<n>Its flow-aware runtime releases tests along the ready frontier and retains completed nodes as regression obligations.
  arXiv  Detail & Related papers  (2026-07-31T20:18:25Z)
• GitInject: Real-World Prompt Injection Attacks in AI-Powered CI/CD Pipelines [22.29523291822444]
  We present GitInject, an open-source framework for evaluating prompt injection vulnerabilities in real, live GitHub.<n>We study workflow configurations across four AI providers and document eleven named attacks spanning config-file injection, credential exfiltration, judgment manipulation, and availability.<n>We find that all tested providers are susceptible to at least one attack class in their default configuration, and that the most critical vulnerabilities are structural.
  arXiv  Detail & Related papers  (2026-06-07T19:17:18Z)
• Agentic Agile-V: From Vibe Coding to Verified Engineering in Software and Hardware Development [0.0]
  Agentic AI coding systems can inspect repositories, plan implementation steps, edit files, run tests, and submit pull requests.<n>Current evidence does not support the simple claim that autonomous code generation automatically improves engineering outcomes.<n>This paper argues that the central problem is no longer prompt engineering; it is engineering process control.
  arXiv  Detail & Related papers  (2026-05-19T20:10:13Z)
• SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks [55.76734816061826]
  We introduce SlopCodeBench, a language-agnostic benchmark comprising 20 problems and 93 checkpoints.<n>We track two trajectory-level quality signals: verbosity, the fraction of redundant or duplicated code, and structural erosion.<n>No agent solves any problem end-to-end across 11 models; the highest checkpoint solve rate is 17.2%.
  arXiv  Detail & Related papers  (2026-03-25T19:26:44Z)
• CktEvo: Repository-Level RTL Code Benchmark for Design Evolution [7.09459001862514]
  We present CktEvo, a benchmark and reference framework for repo-level RTL evolution.<n>Unlike prior benchmarks, our benchmark targets complete IP cores where PPA emerges from cross-file dependencies.<n>We formalize the task as: given an initial repository, produce edits that preserve functional behavior while improving PPA.<n>Our experiments demonstrate that the reference framework realizes PPA improvements without any human interactions.
  arXiv  Detail & Related papers  (2026-02-10T02:46:15Z)
• ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development [72.4729759618632]
  We introduce ABC-Bench, a benchmark to evaluate agentic backend coding within a realistic, executable workflow.<n>We curated 224 practical tasks spanning 8 languages and 19 frameworks from open-source repositories.<n>Our evaluation reveals that even state-of-the-art models struggle to deliver reliable performance on these holistic tasks.
  arXiv  Detail & Related papers  (2026-01-16T08:23:52Z)
• RepoGraph: Enhancing AI Software Engineering with Repository-level Code Graph [63.87660059104077]
  We present RepoGraph, a plug-in module that manages a repository-level structure for modern AI software engineering solutions.<n>RepoGraph substantially boosts the performance of all systems, leading to a new state-of-the-art among open-source frameworks.
  arXiv  Detail & Related papers  (2024-10-03T05:45:26Z)
• Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion? [60.84912551069379]
  We present the Code-Development Benchmark (Codev-Bench), a fine-grained, real-world, repository-level, and developer-centric evaluation framework. Codev-Agent is an agent-based system that automates repository crawling, constructs execution environments, extracts dynamic calling chains from existing unit tests, and generates new test samples to avoid data leakage.
  arXiv  Detail & Related papers  (2024-10-02T09:11:10Z)
• Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository Exploration [64.19431011897515]
  This paper presents Alibaba LingmaAgent, a novel Automated Software Engineering method designed to comprehensively understand and utilize whole software repositories for issue resolution.<n>Our approach introduces a top-down method to condense critical repository information into a knowledge graph, reducing complexity, and employs a Monte Carlo tree search based strategy.<n>In production deployment and evaluation at Alibaba Cloud, LingmaAgent automatically resolved 16.9% of in-house issues faced by development engineers, and solved 43.3% of problems after manual intervention.
  arXiv  Detail & Related papers  (2024-06-03T15:20:06Z)

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.