Human-Centric Reflective Architecture for Human-AI Collaborative Decision-Making

Researchers introduced the Human-Centric Reflective Architecture (HCRA), a framework for human-AI collaborative decision-making that models the task as a stochastic game and integrates human-calibrated models with reinforcement learning agents using linguistic feedback. Evaluation showed HCRA improves decision-making effectiveness and recommendation quality.

arXiv:2607.03025v1 Announce Type: new Abstract: The use of Large Language Models LLMs across diverse areas of human activity-ranging from everyday tasks to safety-critical applications-aims to enhance decision-making effectiveness with minimal human feedback. Concurrently, it seeks to align decisions with human expectations, preferences, and needs while mitigating risks associated with AI non-determinism. However, humans frequently over- or under-rely on AI recommendations, and current AI systems remain poorly calibrated to human expectations. To address these challenges, we introduce a human-AI collaborative decision-making framework designed to augment human capabilities and align AI agents with human preferences and expectations. Specifically, this paper a formulates the collaborative decision-making task as a stochastic game between an AI agent and a human player, and b proposes the Human-Centric Reflective Architecture HCRA , which integrates human-calibrated models with reinforcement learning agents that leverage linguistic feedback in an iterative, reflective process. Evaluation results demonstrate that HCRA enhances decision-making effectiveness and delivers high-quality recommendations.