HERMES: modular executable Dev-Primitives for software engineering agents
An arXiv paper introduces Dev-Primitives, an abstraction that pairs each repository artifact — source files, configs, tests, dependencies — with a resident LLM, giving it an agent-native interface for natural-language reasoning, inter-component communication and localized self-modification. Built on top of it, the HERMES harness-engineering framework activates these primitives at repository scale via dependency-aware dynamic activation and a bug-diagnosis mechanism that maps execution evidence back to the components needing revision. The authors report HERMES beats matched baseline harnesses by 12.4% on average across four software engineering benchmarks, stays within 4.5% of a homogeneous GPT-5.6 Sol configuration even with Qwen3-8B Dev-Primitives, and cuts inference cost by 26.2% on Terminal-Bench 4.0.
Why it matters: It shifts attention from the model to harness design, suggesting long-horizon coding agents depend heavily on how repository-level context and tool interfaces are organized.
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
View a PDF of the paper titled Harness Engineering for Software Engineering via Modular Executable Dev-Primitives, by Haibo Jin and 2 other authors
View PDF HTML (experimental)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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How we got here
- AWS adds SageMaker inference-optimization skill for coding agentsAWS Machine Learning Blog · Qwen3-8B
- Agent swarms may be AI's next scaling law, but gains look limitedUnderstanding AI · GPT-5.6 Sol
- VISTA boosts multimodal agents with visual memory and active recallMIT科技评论中文 · GPT-5.6 Sol
- VISTA Gives Frontier Models Visual Memory for ARC-AGI-3创业邦 科技 · GPT-5.6 Sol