HERMES：可执行 Dev-Primitives 框架

Hugging Face · Papers10-05 20:00Haibo Jin, Xinjie Li, Peng Kuang, Haohan Wang

AI 总结

arXiv 上一篇论文提出 Dev-Primitives——把仓库中的代码、配置、测试、依赖等构件与一个常驻 LLM 配对，使其具备可执行、可对话、可局部自我修改的智能体原生接口；在此基础上构建 HERMES 框架，通过依赖感知的动态激活与缺陷诊断机制在仓库规模上调度这些构件。论文称在四个软件工程基准上较匹配的基线 harness 平均高出 12.4%，使用 Qwen3-8B 作为 Dev-Primitives 时与全同构的 GPT-5.6 Sol 配置差距在 4.5% 以内，并在 Terminal-Bench 4.0 上把推理成本降低 26.2%。

为什么重要：它把研究重点从模型本身转向“harness 设计”，提示长周期编程智能体的表现可能很大程度上取决于如何组织仓库级上下文与工具接口。

HERMESDev-PrimitivesQwen3-8B

来源原文Hugging Face · Papers · 约 3 分钟读完

Computer Science > Software Engineering

arXiv:2610.07832 (cs)

[Submitted on 6 Oct 2026]

Title:Harness Engineering for Software Engineering via Modular Executable Dev-Primitives

Authors:Haibo Jin, Xinjie Li, Peng Kuang, Haohan Wang

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Abstract:Large language models (LLMs) equipped with terminal access have demonstrated strong capabilities in automating software engineering tasks. However, existing agents remain brittle on long-horizon workflows, where they must repeatedly reconstruct program state scattered across source files, configurations, tests, dependencies, and runtime behavior, leading to increasingly long interaction histories, context explosion, and semantic drift. Large repositories further complicate the identification of task-relevant components. To address these challenges, we introduce \textbf{Dev-Primitives} (\emph{Development Primitives}), a modular and executable abstraction that transforms repository components from passive software artifacts into active participants in software engineering. Each Dev-Primitive pairs a repository artifact with a resident LLM, which gives the artifact an agent-native interface grounded in its own implementation and dependencies, enabling natural-language reasoning, inter-component communication, and localized self-modification. Building on Dev-Primitives, we propose \textbf{HERMES}, a Harness Engineering framework for software engineeRing via Modular Executable Dev-PrimitiveS, which instantiates these primitives at repository scale through a dependency-aware dynamic activation mechanism and a bug diagnosis mechanism that maps execution evidence back to the components that must be revised. Extensive experiments on four software engineering benchmarks demonstrate that HERMES outperforms matched baseline harnesses by 12.4\% on average. Moreover, when paired with strong activation and diagnosis models, HERMES, even with Qwen3-8B Dev-Primitives, remains within 4.5\% of the homogeneous GPT-5.6 Sol configuration across all four benchmarks, while reducing inference cost by 26.2\% on Terminal-Bench 4.0, highlighting the importance of harness design in software engineering agents.

Comments:	30 pages
Subjects:	Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
Cite as:	arXiv:2610.07832 [cs.SE]
	(or arXiv:2610.07832v1 [cs.SE] for this version)
	https://doi.org/10.48550/arXiv.2610.07832 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Haibo Jin [view email]
[v1] Tue, 6 Oct 2026 06:32:11 UTC (526 KB)

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