Task-Aware Delegation Cues for LLM Agents

2026-03-11

AI Summary

This work addresses the limitations of human–large language model (LLM) collaboration caused by information asymmetry, where users struggle to assess agent reliability and agents lack effective means to convey uncertainty. The authors propose a task-aware coordination signaling layer that leverages task semantic clustering to construct capability profiles and risk indicators, transforming offline preference data into online delegation primitives. This framework enables mutual verification, adaptive routing, rationale disclosure, and auditable logging. Evaluated on pairwise comparison data from Chatbot Arena, the resulting task taxonomy significantly improves winner prediction accuracy and reduces difficulty estimation error, demonstrating that task types encode actionable structure. The approach establishes a novel design paradigm for dynamic human–AI collaboration that is transparent, negotiable, and auditable.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Coordination and Collaboration
• Natural Language Processing: Safety and Robustness

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

LLM agents increasingly present as conversational collaborators, yet human--agent teamwork remains brittle due to information asymmetry: users lack task-specific reliability cues, and agents rarely surface calibrated uncertainty or rationale. We propose a task-aware collaboration signaling layer that turns offline preference evaluations into online, user-facing primitives for delegation. Using Chatbot Arena pairwise comparisons, we induce an interpretable task taxonomy via semantic clustering, then derive (i) Capability Profiles as task-conditioned win-rate maps and (ii) Coordination-Risk Cues as task-conditioned disagreement (tie-rate) priors. These signals drive a closed-loop delegation protocol that supports common-ground verification, adaptive routing (primary vs.\ primary+auditor), explicit rationale disclosure, and privacy-preserving accountability logs. Two predictive probes validate that task typing carries actionable structure: cluster features improve winner prediction accuracy and reduce difficulty prediction error under stratified 5-fold cross-validation. Overall, our framework reframes delegation from an opaque system default into a visible, negotiable, and auditable collaborative decision, providing a principled design space for adaptive human--agent collaboration grounded in mutual awareness and shared accountability.

Problem

• information asymmetry
• LLM agents
• task-aware delegation
• human-agent collaboration
• reliability cues

Innovation

• task-aware delegation
• capability profiles
• coordination-risk cues
• human-agent collaboration
• LLM agents