Requesting Expert Reasoning: Augmenting LLM Agents with Learned Collaborative Intervention
#LLM agents #Human-AI collaboration #AHCE framework #Expert reasoning #Long-tail knowledge #Task success rates #Minecraft experiments #AI limitations
📌 Key Takeaways
- Researchers developed AHCE framework for Human-AI collaboration
- The framework addresses LLM limitations in specialized domains requiring long-tail knowledge
- Human experts are treated as interactive reasoning tools rather than simple information sources
- Experiments in Minecraft showed 32% improvement in normal tasks and 70% in difficult tasks
- Successful augmentation requires learning how to request expert reasoning effectively
📖 Full Retelling
Researchers Zhiming Wang, Jinwei He, and Feng Lu introduced AHCE (Active Human-Augmented Challenge Engagement), a novel framework for on-demand Human-AI collaboration, in a paper submitted to arXiv on February 26, 2026, addressing the limitation of Large Language Model agents that often fail in specialized domains requiring knowledge absent from their training data. The research tackles a fundamental challenge in artificial intelligence where LLM-based agents excel at general reasoning but struggle when success depends on specialized knowledge not included in their training data. While human experts can potentially fill this knowledge gap, their guidance is often unstructured and unreliable, making direct integration into an agent's planning process difficult. The AHCE framework represents a significant advancement by treating human experts as interactive reasoning tools rather than simple information sources, employing a learned policy to determine when and how to request expert intervention. Extensive experiments conducted within the Minecraft environment demonstrated the framework's effectiveness, with task success rates improving by 32% on normal difficulty tasks and nearly 70% on highly difficult tasks, all while requiring minimal human intervention. The researchers emphasize that successfully augmenting AI agents requires learning how to request expert reasoning effectively, moving beyond simple requests for help to create a more sophisticated collaborative approach.
🏷️ Themes
Artificial Intelligence, Human-AI Collaboration, Knowledge Augmentation
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Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.22546 [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 Abstract: Large Language Model 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 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 arXiv:2602.22546v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.22546 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Zhiming Wang [ view email ] [v1] Thu, 26 Feb 2026 02:38:25 UTC (1,334 KB) Full-text links: Access Paper: 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 TeX Source view license Current browse context: cs.AI < prev | nex...
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