# PathoJev: Efficient Pathology Decisions from Shared Visual Context

**Authors:** Chengyang Zhang, Wenchuan Zhang, Bo Li, Xinyu Liu, Jiaming Yang, Mengran Li, Jie Chen, Wei Ju, Yuhao Yi, Hong Bu, Jiancheng Lv  
**Lab:** Sichuan University · **Published:** 29 Oct 2026  
**Links:** [arXiv](https://arxiv.org/abs/2610.40752) · [PDF](https://arxiv.org/pdf/2610.40752) · [Code](https://github.com/ChyaZhang/PathoJev) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2610.40752.html)

## Key numbers

- **77.2%**: the 9B and 27B models achieve mean accuracies of 77.2% and 78.1%
- **9.1×**: the 9B model runs 9.1x faster than SlideChat
- **6.9×**: Shared context accelerates eight-question workloads by 6.9x and 5.9x over separate prefix encoding

## Abstract

Pathology workflows require multiple visual judgments about the same slide, from identifying tumor type to assessing grade and invasion. Generating and parsing a separate textual response for each structured question adds latency between visual evidence and usable decisions. Building on the System One paradigm, we introduce PathoJev, a pathology model that directly predicts answer probabilities for whole-slide and region-level assessment. PathoJev encodes each slide once and evaluates independent question suffixes in batches over the shared visual context. Reusing the hybrid backbone's recurrent states further limits the computation that grows with slide-context length for each additional question. Across three whole-slide benchmarks, the 9B and 27B models achieve mean accuracies of 77.2% and 78.1%, exceeding SlideChat by 5.4 and 6.3 percentage points. Both scales also maintain competitive accuracy on five ROI benchmarks. On WSI-Bench, the 9B model runs 9.1x faster than SlideChat. Shared context accelerates eight-question workloads by 6.9x and 5.9x over separate prefix encoding, rising to 11.2x and 8.9x for 64 questions. These results provide a reusable probabilistic interface for combining multiple visual judgments in whole-slide and local tissue assessment. Code: https://github.com/ChyaZhang/PathoJev.

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Notes written by AI from the paper, numbers copied from the abstract. Check the paper before you rely on them. From AI Papers Daily.