Adaptive Arena-based Contestable Argumentative Network-of-Experts for Open-Ended Care Plan Coordination
- URL: http://arxiv.org/abs/2608.05391v1
- Date: Wed, 05 Aug 2026 20:18:45 GMT
- Title: Adaptive Arena-based Contestable Argumentative Network-of-Experts for Open-Ended Care Plan Coordination
- Abstract summary: We present CANOE (Contestable Argumentative Network-of-Experts), a multi-agent neuro-symbolic framework.<n>It addresses limitations through five modules: complexity assessment, adaptive team recruitment, role-based argumentative computation, and care-plan synthesis.<n>CanOE's argumentative structure provides faithful explanation and human contestability.
- Score: 1.4988096015658898
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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.
Related papers
- ProMediConv: Benchmarking Proactive Conversational Agents in Legal Dispute Mediation [65.39532118490511]
ProMediConv is a novel benchmarking framework that models mediation as a proactive, multi-stage, and party-aware dialogue process.<n>Using 972 complete real-world cases, we construct a high-fidelity mediation dataset with utterance-level annotations of strategies and BP states.<n>We also propose MAD (Mean Attribute Difference), a fine-grained metric that captures BP shifts throughout the dialogue.
arXiv Detail & Related papers (2026-09-10T05:26:40Z) - CLAIM: Leading Open-domain Active Clarification of Large Language Models with Uncertainty Measurement [50.91045324738942]
CLAIM is an uncertainty-driven framework for active clarification learning in open-domain settings.<n>We propose an entropy-driven synthetic data generation pipeline that integrates uncertainty estimation with semantic clustering and reasoning-based judgments.<n> Experimental results demonstrate that CLAIM can learn stable and generalizable clarification strategies without relying on manually labeled data.
arXiv Detail & Related papers (2026-08-12T04:27:45Z) - An Agentic LLM-Based Framework for Population-Scale Mental Health Screening [0.3568466510804538]
This paper proposes an agentic framework for building robust LLM-based pipelines.<n>A proof-of-concept in transcript-based detection demonstrates that the framework converges to stable configurations.<n>Results highlight the potential of agentic AI to enable population-level mental health screening over large clinical datasets.
arXiv Detail & Related papers (2026-05-13T06:08:43Z) - LLM Psychosis: A Theoretical and Diagnostic Framework for Reality-Boundary Failures in Large Language Models [0.0]
The deployment of large language models (LLMs) as interactive agents has exposed a category of behavioral failure that prevailing terminology fails to adequately characterize.<n>This paper introduces LLM Psychosis as a structured theoretical framework for pathological breakdowns in model cognition that exhibit functional resemblance to psychotic disorders.
arXiv Detail & Related papers (2026-04-07T09:44:15Z) - Argumentation for Explainable and Globally Contestable Decision Support with LLMs [16.75184263883707]
ArgEval is a framework that shifts from instance-specific reasoning to structured evaluation of general decision options.<n>These frameworks provide explainable recommendations for specific cases while still supporting global contestability.<n>We show that ArgEval can produce explainable guidance aligned with clinical practice.
arXiv Detail & Related papers (2026-03-15T22:37:14Z) - CARE: An Explainable Computational Framework for Assessing Client-Perceived Therapeutic Alliance Using Large Language Models [19.027335814014528]
We present CARE, an LLM-based framework to automatically predict multi-dimensional alliance scores and generate interpretable rationales from counseling transcripts.<n> CARE is built on the CounselingWAI dataset and enriched with 9,516 expert-curated rationales.<n>Experiments show that CARE outperforms leading LLMs and substantially reduces the gap between counselor evaluations and client-perceived alliance.
arXiv Detail & Related papers (2026-02-24T07:52:56Z) - Adaptive Collaboration of Arena-Based Argumentative LLMs for Explainable and Contestable Legal Reasoning [1.4988096015658898]
ACAL is a neuro-symbolic framework that integrates adaptive multi-agent collaboration with an Arena-based Quantitative Bipolar Argumentation Framework (A-QBAF)<n>ACAL dynamically deploys expert agent teams to construct arguments, employs a clash resolution mechanism to adjudicate conflicting claims, and utilizes uncertainty-aware escalation for borderline cases.<n>Our framework supports a Human-in-the-Loop (HITL) contestability workflow, enabling users to directly audit and modify the underlying reasoning graph to influence the final judgment.
arXiv Detail & Related papers (2026-02-21T17:47:13Z) - On the Plasticity and Stability for Post-Training Large Language Models [54.757672540381236]
We identify a root cause as the conflict between plasticity and stability gradients.<n>We propose Probabilistic Conflict Resolution (PCR), a framework that models gradients as random variables.<n>PCR significantly smooths the training trajectory and achieves superior performance in various reasoning tasks.
arXiv Detail & Related papers (2026-02-06T07:31:26Z) - MedAD-R1: Eliciting Consistent Reasoning in Interpretible Medical Anomaly Detection via Consistency-Reinforced Policy Optimization [46.65200216642429]
We introduce MedAD-38K, the first large-scale, multi-modal, and multi-center benchmark for MedAD featuring diagnostic Chain-of-Thought (CoT) annotations alongside structured Visual Question-Answering (VQA) pairs.<n>Our proposed model, MedAD-R1, achieves state-of-the-art (SOTA) performance on the MedAD-38K benchmark, outperforming strong baselines by more than 10%.
arXiv Detail & Related papers (2026-02-01T07:56:10Z) - MedAlign: A Synergistic Framework of Multimodal Preference Optimization and Federated Meta-Cognitive Reasoning [52.064286116035134]
We develop MedAlign, a framework to ensure visually accurate LVLM responses for Medical Visual Question Answering (Med-VQA)<n>We first propose a multimodal Direct Preference Optimization (mDPO) objective to align preference learning with visual context.<n>We then design a Retrieval-Aware Mixture-of-Experts (RA-MoE) architecture that utilizes image and text similarity to route queries to a specialized and context-augmented LVLM.
arXiv Detail & Related papers (2025-10-24T02:11:05Z) - Silence is Not Consensus: Disrupting Agreement Bias in Multi-Agent LLMs via Catfish Agent for Clinical Decision Making [80.94208848596215]
We present a new concept called Catfish Agent, a role-specialized LLM designed to inject structured dissent and counter silent agreement.<n>Inspired by the catfish effect'' in organizational psychology, the Catfish Agent is designed to challenge emerging consensus to stimulate deeper reasoning.
arXiv Detail & Related papers (2025-05-27T17:59:50Z) - Few-shot Policy (de)composition in Conversational Question Answering [54.259440408606515]
We propose a neuro-symbolic framework to detect policy compliance using large language models (LLMs) in a few-shot setting.<n>We show that our approach soundly reasons about policy compliance conversations by extracting sub-questions to be answered, assigning truth values from contextual information, and explicitly producing a set of logic statements from the given policies.<n>We apply this approach to the popular PCD and conversational machine reading benchmark, ShARC, and show competitive performance with no task-specific finetuning.
arXiv Detail & Related papers (2025-01-20T08:40:15Z) - LlaMADRS: Prompting Large Language Models for Interview-Based Depression Assessment [75.44934940580112]
This study introduces LlaMADRS, a novel framework leveraging open-source Large Language Models (LLMs) to automate depression severity assessment.<n>We employ a zero-shot prompting strategy with carefully designed cues to guide the model in interpreting and scoring transcribed clinical interviews.<n>Our approach, tested on 236 real-world interviews, demonstrates strong correlations with clinician assessments.
arXiv Detail & Related papers (2025-01-07T08:49:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.