Harnesses for Inference-Time Alignment over Execution Trajectories

2026-05-23 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Software Development & Engineering · Depth: Expert, extended

Summary

Harness engineering, an inference-time technique for large language model (LLM) agents, aims to improve long-term performance through task decomposition and guided execution. This study, by Boyuan Wang, Bochao Li, Minghan Wang, Yuxin Tao, and Fang Kong, analyzes harness design through inference-time trajectory alignment, separating harnesses into task decomposition (structuring sub-goals) and guided execution (reshaping local action distributions). The research quantifies how workflow granularity, retry budgets, and guidance-induced action reweighting impact performance limits, revealing failure modes like over-decomposition, over-pruning, and hallucinated execution. Validated through synthetic experiments and real Terminal-Bench v2 benchmarks, the findings show that effective harnesses can be partial, specifying only initial steps for higher pass rates than fully structured workflows.

Key takeaway

For AI Engineers designing LLM agent workflows, recognize that simply adding more structure or guidance can degrade performance. You should prioritize aligning task decomposition granularity with your agent's capabilities and ensuring guidance is evidence-based. Implement partial harnessing by specifying only the initial critical steps, allowing the agent autonomy for the remainder, which can lead to higher task success rates and prevent over-constraining the agent.

Key insights

Effective LLM agent harnesses require alignment between imposed structure and agent capability, not just more structure.

Principles

• Sub-goal scale must match agent capability.
• Guidance must align with task evidence.
• Harnesses need not specify the full execution path.

Method

Partial Harnessing specifies only initial stages, leaving remaining execution to the agent, guided by a marginal stopping rule where tail-risk reduction outweighs scaffold cost.

In practice

• Avoid over-decomposition; match granularity to agent's progress scale.
• Ensure guidance aligns with evidence to prevent hallucination.
• Consider partial harnesses for improved pass rates.

Topics

• LLM Agents
• Harness Engineering
• Inference-Time Alignment
• Task Decomposition
• Guided Execution
• Partial Harnessing
• Terminal-Bench v2

Code references

• stanford-iris-lab/meta-harness-tbench2-artifact

Best for: Research Scientist, NLP Engineer, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.