AGENTIC-SYSTEMSApril 3, 20261 min read22 views

One Panel Does Not Fit All: Case-Adaptive Multi-Agent Deliberation for Clinical Prediction

Researchers introduce CAMP, a case-adaptive multi-agent framework that dynamically assembles specialist panels for clinical prediction, outperforming traditional fixed-role LLM methods while maintaining transparency and efficiency.

Computer Science > Artificial Intelligence

Title:One Panel Does Not Fit All: Case-Adaptive Multi-Agent Deliberation for Clinical Prediction

View PDF HTML (experimental)Abstract:Large language models applied to clinical prediction exhibit case-level heterogeneity: simple cases yield consistent outputs, while complex cases produce divergent predictions under minor prompt changes. Existing single-agent strategies sample from one role-conditioned distribution, and multi-agent frameworks use fixed roles with flat majority voting, discarding the diagnostic signal in disagreement. We propose CAMP (Case-Adaptive Multi-agent Panel), where an attending-physician agent dynamically assembles a specialist panel tailored to each case's diagnostic uncertainty. Each specialist evaluates candidates via three-valued voting (KEEP/REFUSE/NEUTRAL), enabling principled abstention outside one's expertise. A hybrid router directs each diagnosis through strong consensus, fallback to the attending physician's judgment, or evidence-based arbitration that weighs argument quality over vote counts. On diagnostic prediction and brief hospital course generation from MIMIC-IV across four LLM backbones, CAMP consistently outperforms strong baselines while consuming fewer tokens than most competing multi-agent methods, with voting records and arbitration traces offering transparent decision audits.

Bibliographic and Citation Tools

Code, Data and Media Associated with this Article

Demos

Recommenders and Search Tools

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Source: arXiv cs.AI Recent

More in this category

agentic-systems

Schema-Aware Localisation (SAL): Live Schema Grounding and Hallucination Validation for Oracle NL2SQL

Researchers introduce Schema-Aware Localisation (SAL), a lightweight middleware for Oracle NL2SQL that connects LLMs to live database catalogs. SAL dramatically improves SQL execution accuracy from 2.2% to 62.6% while drastically reducing hallucination-induced execution failures without requiring model retraining.

agentic-systems

Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits

Researchers have proposed a loss-aware structured feature-map pruning framework for convolutional neural networks using multi-armed bandits. The approach significantly reduces computational complexity and storage costs while maintaining accuracy levels close to original unpruned models.

agentic-systems

SCAIR: Schema-Conditioned Agentic Iterative Reasoning for Enterprise Knowledge Graphs

Researchers have introduced SCAIR, a training-free agentic framework that enhances Knowledge Graph-based RAG (KG-RAG) for enterprise applications by injecting schema constraints into multi-hop iterative reasoning.

agentic-systems

Synthetic Scenario Generation for Evaluation of Industry 4.0 Agents

Researchers have developed ScenarioGeneratorAgent, an automated pipeline that generates synthetic evaluation scenarios for Industry 4.0 AI agents. Tested on Smart Grid Transformers, the approach accelerates benchmark generation by 8x while maintaining high quality and adherence to IEC standards.

agentic-systems

Concept-based Visual Counterfactual Explanations with Diffusion Models

Researchers introduced C-VCE, a novel diffusion framework that generates human-interpretable visual counterfactual explanations by building concept bottleneck layers directly into the generative model.

agentic-systems

Teaching LLMs to Update Beliefs for Efficient Long-Horizon Interaction

As LLMs tackle longer tasks, retaining complete interaction histories becomes contextually and computationally expensive. The ABBEL framework addresses this by isolating summaries into natural-language 'belief states' and supervising them via belief grading, recovering accuracy while significantly cutting memory usage.