Explore/framework/Human-Centric Reflective Architecture for Human-AI Collaborative Decision-Making
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Andreas Kouridakis, Dimitrios Patiniotis Spyropoulos, George Vouros/Human-Centric Reflective Architecture for Human-AI Collaborative Decision-MakingUnknown

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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AgentHub Score
55 / 100
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55Score
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70C+
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45C
Community
42C
Production
58C
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2026-07-262026-10-15
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✓ Best for

  • Multi-agent orchestration
  • Production agentic workflows
  • Stateful long-running tasks

◎ Strengths

  • Stable API
  • Active release cadence
  • Strong GitHub community

✕ Not ideal for

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  • Teams without Python/ML expertise

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  • Breaking changes between minor versions
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AgentHub Score

55
Score 55/100
Below average

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