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[2602.22546] Requesting Expert Reasoning: Augmenting LLM Agents with Learned Collaborative Intervention
Summary
This article presents a framework called AHCE for enhancing Large Language Model (LLM) agents through effective human collaboration, significantly improving task success rates in specialized domains.
Why It Matters
As LLMs struggle with specialized knowledge, this research highlights a method to integrate human expertise effectively, addressing a critical gap in AI capabilities. The findings could influence future AI development, particularly in applications requiring expert knowledge.
Key Takeaways
- AHCE framework allows LLMs to collaborate with human experts for improved reasoning.
- Task success rates increased by 32% on normal and nearly 70% on difficult tasks with minimal human input.
- The Human Feedback Module (HFM) treats human experts as interactive reasoning tools.
- The approach moves beyond simple requests for help to structured expert engagement.
- This research could enhance AI applications in specialized fields requiring expert knowledge.
Computer Science > Artificial Intelligence arXiv:2602.22546 (cs) [Submitted on 26 Feb 2026] Title:Requesting Expert Reasoning: Augmenting LLM Agents with Learned Collaborative Intervention Authors:Zhiming Wang, Jinwei He, Feng Lu View a PDF of the paper titled Requesting Expert Reasoning: Augmenting LLM Agents with Learned Collaborative Intervention, by Zhiming Wang and 1 other authors View PDF HTML (experimental) Abstract:Large Language Model (LLM) based agents excel at general reasoning but often fail in specialized domains where success hinges on long-tail knowledge absent from their training data. While human experts can provide this missing knowledge, their guidance is often unstructured and unreliable, making its direct integration into an agent's plan problematic. To address this, we introduce AHCE (Active Human-Augmented Challenge Engagement), a framework for on-demand Human-AI collaboration. At its core, the Human Feedback Module (HFM) employs a learned policy to treat the human expert as an interactive reasoning tool. Extensive experiments in Minecraft demonstrate the framework's effectiveness, increasing task success rates by 32% on normal difficulty tasks and nearly 70% on highly difficult tasks, all with minimal human intervention. Our work demonstrates that successfully augmenting agents requires learning how to request expert reasoning, moving beyond simple requests for help. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.22546 [cs.AI] (or a...
