Adaptive Arena-based Contestable Argumentative Network-of-Experts for Open-Ended Care Plan Coordination

2026-08-05

AI Summary

This work addresses the challenges of information heterogeneity and the lack of transparency and safety in large language models within multidisciplinary care coordination. To this end, the authors propose CANOE, a multi-agent neuro-symbolic framework that integrates role-based expert agents, arena-based quantified bipolar argumentation (QBAF), adaptive expert recruitment, and a human-in-the-loop correction mechanism. Notably, CANOE introduces, for the first time, an intervenable deterministic recomputation pipeline to yield clinical decisions that are explainable, contestable, and safe. Experimental results on the Discharge Me! and MedicalRAG datasets demonstrate that CANOE, when combined with medically fine-tuned language models, significantly enhances clinical correctness, safety, and interpretability, achieving strong performance across multiple metrics including ROUGE-L, AlignScore, and MEDCON F1.

Abstract

Care plan coordination demands synthesizing heterogeneous clinical, functional, and psychosocial information across multiple professional disciplines, where monolithic LLM pipelines cannot perform in a transparent or safe manner. We present CANOE (Contestable Argumentative Network-of-Experts), a multi-agent neuro-symbolic framework that addresses these limitations through five modules: complexity assessment, adaptive team recruitment, role-based argumentative computation via an Arena-based Quantitative Bipolar Argumentation Framework (A-QBAF), human-in-the-loop contestation, and care-plan synthesis. Role-specialized agents generate supporting and attacking arguments for candidate interventions; conflicts are resolved through arena-based clash resolution before acceptability scores propagate across the argumentation graph. Care planners may accept, reject, edit, or add arguments, and the framework will deterministically recompute the final plan. Evaluation on Discharge Me! and MedicalRAG using ROUGE-L, AlignScore, MEDCON F1, FKGL, and LLM-as-a-judge shows that medically fine-tuned models achieve the strongest clinical correctness and safety, while CANOE's argumentative structure provides faithful explanation and human contestability.

Problem

• care plan coordination
• heterogeneous information integration
• transparent decision-making
• safety in clinical AI
• multi-disciplinary reasoning

Innovation

• Argumentative AI
• Neuro-symbolic framework
• Human-in-the-loop contestability
• Arena-based reasoning
• Care plan coordination