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PathoArgus: Advancing Evidence-Grounded Long-Context Visual Reasoning across Gigapixel Whole-Slide and Multi-Slide Case Contexts

Research Medical/Healthcare AI

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Representative image for PathoArgus: Advancing Evidence-Grounded Long-Context Visual Reasoning across Gigapixel Whole-Slide and Multi-Slide Case Contexts

Merged summary

TL;DR - PathoArgus-Bench evaluates whether pathology AI systems genuinely ground answers in gigapixel whole-slide and multi-slide evidence rather than exploiting textual priors. Results show that conventional question-level accuracy substantially overstates reliable evidence-based reasoning.

  • The benchmark contains 22,078 four-choice questions from 4,913 patients across 15 TCGA projects, spanning six pathology capabilities and three evidence-demand levels.
  • Evidence State Quartets test grounding by holding question text fixed while moving, replacing, or removing the target slide set.
  • GPT-5.6 achieved 57.09% overall accuracy but answered all four states correctly in only 19 of 483 quartets (3.93% QExact).
  • The proposed fixed-budget PathoArgus reader reached 50.39% overall accuracy but only 1.86% QExact, indicating that better context access alone does not ensure consistent evidence use.

Sources (1)

PathoArgus: Advancing Evidence-Grounded Long-Context Visual Reasoning across Gigapixel Whole-Slide and Multi-Slide Case Contexts

arXiv cs.CV Bowen Liu, Qixiang Zhang, Xiaomeng Li 2026-08-18 arXiv:2608.17607
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-08 14:14:23.108002 UTC

TL;DR - PathoArgus-Bench evaluates whether pathology AI systems genuinely ground answers in gigapixel whole-slide and multi-slide evidence rather than exploiting textual priors. Results show that conventional question-level accuracy substantially overstates reliable evidence-based reasoning.

  • The benchmark contains 22,078 four-choice questions from 4,913 patients across 15 TCGA projects, spanning six pathology capabilities and three evidence-demand levels.
  • Evidence State Quartets test grounding by holding question text fixed while moving, replacing, or removing the target slide set.
  • GPT-5.6 achieved 57.09% overall accuracy but answered all four states correctly in only 19 of 483 quartets (3.93% QExact).
  • The proposed fixed-budget PathoArgus reader reached 50.39% overall accuracy but only 1.86% QExact, indicating that better context access alone does not ensure consistent evidence use.
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