Contamination, Prior, or Evidence? Decomposing and Training Evidence Use in Whole-Slide Vision-Language Models

arXiv:2609.32185 · cs.CV, cs.LG · Submitted 2026-09-26 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Accuracy — 45× in this paper · explained in 1 episode
• Benchmark — 40× in this paper · explained in 2 episodes
• Foundation Model — 8× in this paper · explained in 3 episodes
• Hallucination — 6× in this paper · explained in 3 episodes
• Reinforcement Learning — 5× in this paper · explained in 4 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• Multi-Agent System — 2× in this paper · explained in 6 episodes
• Provenance — 2× in this paper · explained in 4 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• AUROC — 1× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Adversarial Robustness — 1× in this paper · explained in 3 episodes
• Alignment Methods — 1× in this paper · explained in 1 episode
• Cancer Genome Atlas — 1× in this paper
• Evidence-Weighted Accuracy — 1× in this paper
• External Validation — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Multi-agent Collaboration — 1× in this paper · explained in 2 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes
• Visual Grounding — 1× in this paper · explained in 2 episodes

Sources

• Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering
• LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training
• Qwen3-VL Technical Report
• Qwen2.5-VL Technical Report
• Are We on the Right Way for Evaluating Large Vision-Language Models?
• SlideChat: A Large Vision-Language Assistant for Whole-Slide Pathology Image Understanding
• LongNet: Scaling Transformers to 1,000,000,000 Tokens
• PathFinder: A Multi-Modal Multi-Agent System for Medical Diagnostic Decision-Making Applied to Histopathology
• PathVQA: 30000+ Questions for Medical Visual Question Answering
• Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding
• NaturalBench: Evaluating Vision-Language Models on Natural Adversarial Samples
• WSI-LLaVA: A Multimodal Large Language Model for Whole Slide Image
• Improved Baselines with Visual Instruction Tuning
• WSI-Agents: A Collaborative Multi-Agent System for Multi-Modal Whole Slide Image Analysis
• MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learning
• Enhancing Pathological VLMs with Cross-scale Reasoning
• PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology
• CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology
• CPathAgent: An Agent-based Foundation Model for Interpretable High-Resolution Pathology Image Analysis Mimicking Pathologists' Diagnostic Logic
• mDPO: Conditional Preference Optimization for Multimodal Large Language Models

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