HGM-Dual: Lineage-Aware Guided Agent Harness Optimization for Long-Horizon Planning
[Sztuczna inteligencja] Autorzy: Sudipta Paul, Pengrui Quan, Aounon Kumar i 3 więcej
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 ea…
- Kategoria arxiv
- cs.AI
- Kategoria
- Sztuczna inteligencja