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

Paper recorded by Signals 4 on 2026-10-21 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-21 on arXiv · recorded by Signals 4 on 2026-10-22

Category: cs.AI · 人工智能 · first seen 2026-10-22

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 c

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#217 most recent of 960 cs.AI papers we have recorded · ↑ newer: SP-HLM: Sparse-Pose-Driven Motion Generation and Whole-Body Control fo · ↓ older: Robust Soft Skill Adaptation via Behavioral Localization in Tool-Using
Cite this page: HGM-Dual: Lineage-Aware Guided Agent Harness Optimization for Long-Horizon Planning: the #217 most recent of 960 cs.AI papers we have recorded (as of 2026-10-21). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/hgm-dual-lineage-aware-guided-agent-harness-optimization-for-long-horizon-planni.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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