mlsanityData to 14 Oct 2026

TumorBoard: Evidence-Grounded Multi-Agent Decision Support for Longitudinal Neuro-Oncology

arXiv 2608.03190 ↗First posted 4 Aug 2026cs.AIcited by 0

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Abstract (arXiv)

Neuro-oncology decisions require coordinated interpretation of serial MRI, pathology, molecular markers, treatment history, performance status, and evolving guidelines. We present TumorBoard, a multi-agent decision-support system built around a shared longitudinal case state and an auditable claim-evidence ledger. Specialist agents for radiology, neuropathology, molecular diagnosis, guidelines, and therapy planning produce atomic claims with provenance. An adversarial critic exposes contradictions, and a safety governor releases, qualifies, or defers recommendations according to evidence sufficiency and temporal validity. On a 360-case hidden benchmark at a matched token budget, TumorBoard achieved an action F1 of 0.772 and evidence entailment of 0.914. It exceeded the strongest typed-council baseline by 3.1 percentage points (95% CI: 1.6 to 4.7, adjusted p = 0.0012), while recommendation-to-evidence coverage reached 0.927. Under evidence deletion, the system deferred 84.2% of unsafe cases and limited harmful recommendations to 5.8%. The safety governor reduced harmful release by 7.8 percentage points at a false-deferral cost of 4.3 percentage points. Ablation studies of the ledger, critic, and governor produced the predicted failure patterns, establishing structured coordination as the source of the measured multi-agent advantage.

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References · 15

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  3. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  4. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  5. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  6. 2022Large Language Models Encode Clinical Knowledgecited by 1,830
  7. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,808
  8. 2023AgentBench: Evaluating LLMs as Agentscited by 1,267
  9. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 452
  10. 2023Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarizationcited by 246
  11. 2023Large language models in healthcare and medical domain: A reviewcited by 127
  12. 2023Almanac: Retrieval-Augmented Language Models for Clinical Medicinecited by 123
3 more
  1. 2023Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Healthcited by 118
  2. 2024A Framework for Human Evaluation of Large Language Models in Healthcare Derived from Literature Reviewcited by 66
  3. 2023Large Language Models as Agents in the Cliniccited by 4

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