BioInsight: Multi-Agent Orchestration for Interactive Biomedical Knowledge Discovery

• URL: http://arxiv.org/abs/2606.20997v1
• Date: Fri, 19 Jun 2026 00:15:54 GMT
• Title: BioInsight: Multi-Agent Orchestration for Interactive Biomedical Knowledge Discovery
• Abstract summary: We present textscBioInsight, a multi-agent system that moves from static biomedical report generation to interactive evidence-centered interactive interface generation.<n>Given a disease name, a protein association table, and optional cohort metadata, BioInsight organizes disease-specific evidence through typed intermediate artifacts.<n>The system decomposes evidence retrieval from mechanistic reasoning, normalizes citations through deterministic components, and converts the same structured evidence used in the report into an interactive interface.
• Score: 34.78175107628791
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Biomedical researchers increasingly use AI-generated analyses and reports to interpret protein-level signals, but static outputs are often insufficient for research decision-making, where users need to inspect evidence, assess uncertainty, compare mechanisms, and refine hypotheses. We present \textsc{BioInsight}, a multi-agent system that moves from static biomedical report generation to interactive evidence-centered interactive interface generation. Given a disease name, a protein association table, and optional cohort metadata, BioInsight organizes disease-specific evidence through typed intermediate artifacts, including ranked pathways, literature evidence packets, protein-level reasoning notes, citation-grounded reports, dashboard schemas, and rendered interactive interfaces. The system decomposes evidence retrieval from mechanistic reasoning, normalizes citations through deterministic components, and converts the same structured evidence used in the report into an interactive interface. We evaluate BioInsight on standardized biomedical QA, challenging protein-function reasoning, and end-to-end biomedical evidence synthesis. Results show that BioInsight achieves best, and suggest that biomedical AI systems should move beyond text-only and static reports toward provenance-preserving, interactive evidence artifacts.

Related papers

• Bioinfoysis Technical Report [22.71562563762352]
  We introduce textbfBioinfoysis, a multi-agent harness that represents each request as a persistent artifact-grounded analysis run.<n>A controlled runtime validates generated scripts, tables, and figures before they are used in downstream analysis or reporting.<n>Across four underlying language models, Bioinfoysis increases average accuracy from 27.81% to 64.13% on SeqQA2 and from 3.13% to 31.25% on DbQA2.
  arXiv  Detail & Related papers  (2026-09-03T13:59:00Z)
• BioM-JEPA: joint-embedding prediction of graph-connected gene blocks in single cells [64.21963170814706]
  BioM-JEPA predicts aggregate representations of graph-connected gene blocks.<n>A student network infers each target-block representation from the remaining genes in a cell.<n>Across CellBench tasks, BioM-JEPA embeddings retained expression, pathway and neighbourhood information.
  arXiv  Detail & Related papers  (2026-08-06T11:58:29Z)
• Towards World Models in Biomedical Research [77.59710966764466]
  We propose biomedical world models as a paradigm for AI-driven discovery.<n>These models learn latent representations of molecular, cellular, tissue and clinical states, together with intervention-conditioned dynamics.<n>We discuss how biomedical world models could function as data engines, environment simulators and scientific planning substrates across applications including virtual cells, organoids, virtual patients and surgical simulation.
  arXiv  Detail & Related papers  (2026-06-04T09:28:54Z)
• PlantMarkerBench: A Multi-Species Benchmark for Evidence-Grounded Plant Marker Reasoning [10.873172077806798]
  PlantMarkerBench is a benchmark for evaluating literature-grounded plant marker evidence interpretation from full-text biological papers.<n>It spans four plant species and contains 5,550 sentence-level evidence instances annotated for marker-evidence validity, evidence type, and support strength.<n>We benchmark diverse open-weight and closed-source language models across species and prompting strategies.
  arXiv  Detail & Related papers  (2026-05-11T05:57:15Z)
• From Literature to Hypotheses: An AI Co-Scientist System for Biomarker-Guided Drug Combination Hypothesis Generation [4.281508114645598]
  CoDHy is an interactive, human-in-the-loop system for biomarker-guided drug combination hypothesis generation in cancer research.<n>It integrates structured biomedical databases and unstructured literature evidence into a task-specific knowledge graph.<n>Users can configure the scientific context, inspect intermediate results, and iteratively refine hypotheses.
  arXiv  Detail & Related papers  (2026-02-28T12:14:37Z)
• Biomedical reasoning in action: Multi-agent System for Auditable Biomedical Evidence Synthesis [19.14228623181563]
  We present M-Reason, a demonstration system for transparent, agent-based reasoning and evidence integration in the biomedical domain.<n>M-Reason leverages recent advances in large language models (LLMs) and modular agent orchestration to automate evidence retrieval, appraisal, and synthesis across diverse biomedical data sources.<n>An open, interactive user interface allows researchers to directly observe, explore and evaluate the multi-agent workflow.
  arXiv  Detail & Related papers  (2025-10-06T19:57:29Z)
• Combating Biomedical Misinformation through Multi-modal Claim Detection and Evidence-based Verification [11.555285143713315]
  CER (Combining Evidence and Reasoning) is a novel framework for biomedical fact-checking.<n>It integrates scientific evidence retrieval, reasoning via large language models, and supervised veracity prediction.<n>It effectively mitigates the risk of hallucinations, ensuring that generated outputs are grounded in verifiable, evidence-based sources.
  arXiv  Detail & Related papers  (2025-09-17T10:31:09Z)
• Causal Representation Learning from Multimodal Biomedical Observations [57.00712157758845]
  We develop flexible identification conditions for multimodal data and principled methods to facilitate the understanding of biomedical datasets.<n>Key theoretical contribution is the structural sparsity of causal connections between modalities.<n>Results on a real-world human phenotype dataset are consistent with established biomedical research.
  arXiv  Detail & Related papers  (2024-11-10T16:40:27Z)
• Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction [50.7901190642594]
  We propose BioKDN (Biomedical Knowledge Graph Denoising Network) for robust molecular interaction prediction. BioKDN refines the reliable structure of local subgraphs by denoising noisy links in a learnable manner. It maintains consistent and robust semantics by smoothing relations around the target interaction.
  arXiv  Detail & Related papers  (2023-12-09T07:08:00Z)
• EBOCA: Evidences for BiOmedical Concepts Association Ontology [55.41644538483948]
  This paper proposes EBOCA, an ontology that describes (i) biomedical domain concepts and associations between them, and (ii) evidences supporting these associations. Test data coming from a subset of DISNET and automatic association extractions from texts has been transformed to create a Knowledge Graph that can be used in real scenarios.
  arXiv  Detail & Related papers  (2022-08-01T18:47:03Z)

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.