PathoSage: Towards Multi-Source Evidence Adjudication in Pathology via Experience-Aware Agentic Workflow
- URL: http://arxiv.org/abs/2606.07549v1
- Date: Mon, 18 May 2026 12:30:03 GMT
- Title: PathoSage: Towards Multi-Source Evidence Adjudication in Pathology via Experience-Aware Agentic Workflow
- Abstract summary: PathoSage is a framework that separates knowledge retrieval, evidence collection, and evidence adjudication for patch-level pathology reasoning.<n>Our results highlight explicit evidence adjudication and reliability-aware tool modeling as key ingredients for robust pathology agents.
- Score: 36.83789028433599
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advances in Multimodal Large Language Models (MLLMs) and agent workflows have shown strong promise for computational pathology, yet reliable patch-level reasoning remains challenging. End-to-end pathology MLLMs often hallucinate morphological features, while recent agentic systems usually merge tool outputs and retrieved knowledge into a shared context, making decisions vulnerable to conflicting evidence and context contamination. We propose PathoSage, a three-stage framework that explicitly separates knowledge retrieval, evidence collection, and evidence adjudication for patch-level pathology multimodal reasoning. Its core component, Structured Evidence Deliberation, independently evaluates heterogeneous evidence from tools, performs conflict analysis, and generates the final judgment in a fresh context to reduce anchoring bias. We further introduce a training-free Beta-Bernoulli experience system with continuous credit assignment to model long-term tool reliability and construct similarity-weighted priors for future tool use. Experiments show that PathoSage effectively mitigates VQA hallucinations and classifier disagreement, outperforming strong pathology MLLM and agentic baselines. Our results highlight explicit evidence adjudication and reliability-aware tool modeling as key ingredients for robust pathology agents.
Related papers
- DrugReason: Dynamic Multi-View Reasoning over Knowledge Graph and Language Evidence for Drug Repurposing [55.45112712244231]
DrugReason is a multi-view reasoning framework that integrates grounded KG reasoning with LLM-generated mechanistic inference for drug repurposing.<n> Experiments on PharmaDB, DDInter, and DrugBank show that DrugReason improves average performance over strong single-view reasoning baselines.
arXiv Detail & Related papers (2026-09-06T18:54:26Z) - Dementia Etiology Diagnosis via Collaborative Meta Knowledge Enhancement [81.27196033392559]
We propose a Collaborative Meta Knowledge Enhancement (COME) framework for dementia etiology diagnosis.<n>COME injects multi-center acquisition semantics, source identifiers, and modality indicators as heterogeneity-aware embeddings into a unified Transformer architecture for scale-up training.<n>Our method achieves state-of-the-art in-domain performance with a mean macro-averaged AUC of 85.62% and a 4.29-point gain over the strongest baseline.
arXiv Detail & Related papers (2026-07-24T04:26:47Z) - A Multi-Agent Audit Framework for High-Stakes Reasoning: Evaluation and Interpretability in Clinical Mental Health Screening [0.8758828205574085]
We propose a Multi-Agent Audit Framework that simulates a collaborative, multi-step verification process.<n>We empirically validate this architecture in the sensitive domain of clinical mental health screening.<n>Our framework decomposes the reasoning process into a Perception Agent, Knowledge Retrieval-Augmented Generation (RAG), Chain-of-Thought (CoT) clinical inference, and a critical Audit verification stage.
arXiv Detail & Related papers (2026-06-19T05:51:03Z) - Towards a Virtual Neuroscientist: Autonomous Neuroimaging Analysis via Multi-Agent Collaboration [53.772014300855375]
We introduce NIAgent, a multi-agent system for autonomous end-to-end neuroimaging analysis.<n>Unlike conventional flat tool-calling agents, NIAgent adopts a code-centric execution paradigm.<n>We propose a hierarchical verification framework for autonomous quality control, integrating cohort-level metric screening with agentic visual inspection.
arXiv Detail & Related papers (2026-05-10T06:30:19Z) - Strong Reasoning Isn't Enough: Evaluating Evidence Elicitation in Interactive Diagnosis [29.630872344186873]
Interactive medical consultation requires an agent to proactively elicit missing clinical evidence under uncertainty.<n>Existing evaluations largely remain static or outcome-centric, neglecting the evidence-gathering process.<n>We propose an interactive evaluation framework that explicitly models the consultation process using a simulated patient and a revsimulated reporter grounded in atomic evidences.
arXiv Detail & Related papers (2026-01-27T16:36:35Z) - Code-in-the-Loop Forensics: Agentic Tool Use for Image Forgery Detection [59.04089915447622]
ForenAgent is an interactive IFD framework that enables MLLMs to autonomously generate, execute, and refine Python-based low-level tools around the detection objective.<n>Inspired by human reasoning, we design a dynamic reasoning loop comprising global perception, local focusing, iterative probing, and holistic adjudication.<n>Experiments show that ForenAgent exhibits emergent tool-use competence and reflective reasoning on challenging IFD tasks.
arXiv Detail & Related papers (2025-12-18T08:38:44Z) - SurvAgent: Hierarchical CoT-Enhanced Case Banking and Dichotomy-Based Multi-Agent System for Multimodal Survival Prediction [49.355973075150075]
We introduce SurvAgent, the first hierarchical chain-of-thought (CoT)-enhanced multi-agent system for multimodal survival prediction.<n>SurvAgent consists of two stages: WSI-Gene CoT-Enhanced Case Bank Construction employs hierarchical analysis through Low-Magnification Screening, Cross-Modal Similarity-Aware Patch Mining, and Confidence-Aware Patch Mining for pathology images.<n>Dichotomy-Based Multi-Expert Agent Inference retrieves similar cases via RAG and integrates multimodal reports with expert predictions through progressive interval refinement.
arXiv Detail & Related papers (2025-11-20T18:41:44Z) - When One Modality Sabotages the Others: A Diagnostic Lens on Multimodal Reasoning [22.39245479538899]
We introduce modality sabotage, a diagnostic failure mode in which a high-confidence unimodal error overrides other evidence and misleads the fused result.<n>A model-agnostic evaluation layer treats each modality as an agent, producing candidate labels and a brief self-assessment used for auditing.<n>A simple fusion mechanism aggregates these outputs, exposing contributors (modalities supporting correct outcomes) and saboteurs (modalities that mislead)
arXiv Detail & Related papers (2025-11-04T18:20:13Z) - 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) - Diagnostics of cognitive failures in multi-agent expert systems using dynamic evaluation protocols and subsequent mutation of the processing context [0.0]
This work introduces a diagnostic framework for expert systems that not only evaluates but also facilitates the transfer of expert behaviour into LLM-powered agents.<n>We demonstrate the framework on a multi-agent recruiter-assistant system, showing that it uncovers latent cognitive failures.
arXiv Detail & Related papers (2025-09-18T19:08:03Z) - A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model [27.983545541209455]
We present SmartPath-R1, a versatile MLLM capable of simultaneously addressing both ROI-level and WSI-level tasks.<n>Our framework combines scale-dependent supervised fine-tuning and task-aware reinforcement fine-tuning, which circumvents the requirement for chain-of-thought supervision.
arXiv Detail & Related papers (2025-07-23T08:09:42Z) - Towards Long Context Hallucination Detection [49.195854802543714]
Large Language Models (LLMs) have demonstrated remarkable performance across various tasks.<n>They are prone to contextual hallucination, generating information that is either unsubstantiated or contradictory to the given context.<n>We propose a novel architecture that enables pre-trained encoder models, such as BERT, to process long contexts and effectively detect contextual hallucinations.
arXiv Detail & Related papers (2025-04-28T03:47:05Z)
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.