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HGM-Dual: Lineage-Aware Guided Agent Harness Optimization for Long-Horizon Planning

Sudipta Paul, Pengrui Quan, Aounon Kumar, Nicholas Tzou, Vivek Kulkarni, Srinivas Chappidi · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.30042 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

Harness 组件优化,这提高了目标代理人的过去的提示,工具和工作流程,是一个越来越有效的方式来提高代理人建立在冻结或封闭源的 LLMs. 领先的方法,如达尔文 - 戈德机器和Huxley - 戈德机器(HGM)依赖于回归自我改进,并主要用于编码,在那里需要提高代理人所需的能力与能力相匹配。 规划任务,在其中代理人必须生产一系列的工具调用,共同满足互动的限制,打破这种调整。 在那里,开端的自我改进反复探索了无效的变化,并消耗了相当大的评估预算。 我们引入了 HGM-Dual,一个预算高效的编辑导向的诊断框架,用于预算高效的编程。

Machine translation (Meta M2M100); the English original follows.

Abstract

Harness optimization, which improves an agent's prompts, tools, and workflows, is an increasingly effective way to enhance agents built on frozen or closed-source LLMs. Leading approaches such as the Darwin--Godel Machine and the Huxley--Godel Machine (HGM) rely on recursive self-improvement and were developed mainly for coding, where the competence needed to improve the agent coincides with the competence being improved. Planning tasks, in which an agent must produce a sequence of tool calls that jointly satisfies interacting constraints, break this alignment. There, open-ended self-modification repeatedly explores ineffective changes and consumes substantial evaluation budgets. We introduce HGM-Dual, a diagnostic-guided framework for budget-efficient harness evolution for planning agents. First, HGM-Dual represents agent evolution as lineage-aware context. This context records what each ancestor changed, which behaviors improved or regressed, and which modifications were ineffective, giving the editor explicit edit-outcome attribution instead of an unstructured archive of past agents. Second, HGM-Dual introduces error-conditioned dual-path child generation. For each selected parent, it generates a general child that preserves open-ended exploration and specialized children that target the harness components most likely responsible for the parent's highest-ranked constraint violations. HGM-Dual outperforms the strongest baseline by up to 14.45% relative improvement in case accuracy while matching HGM variants' final performance with 2x-4x fewer evaluation budget.

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Topics: Competence (human resources), Computer science, Context (archaeology), Artificial intelligence, Sequence (biology), Matching (statistics)

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