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

• URL: http://arxiv.org/abs/2607.03025v1
• Date: Fri, 03 Jul 2026 07:12:44 GMT
• Title: Human-Centric Reflective Architecture for Human-AI Collaborative Decision-Making
• Abstract summary: We introduce a human-AI collaborative decision-making framework designed to augment human capabilities and align AI agents with human preferences and expectations.<n>The Human-Centric Reflective Architecture (HCRA) integrates human-calibrated models with reinforcement learning agents that leverage linguistic feedback in an iterative, reflective process.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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