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Paper Citation Record · LEDGER

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis

As of 5 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2607.23794.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.23794 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-30T12:15:47.450859Z

measured 44 of 44 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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44 of 44 outbound references displayed

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Outbound references

Observation 878ff716-20b3-4a23-9700-3f478e076894 · outbound

This paper cites Towards a general-purpose foundation model for computational pathology,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Towards a general-purpose foundation model for computational pathology,

Reference 1

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Observation 69d634b3-f896-4be4-b9a0-ae7d731e9432 · outbound

This paper cites Cost-effective instruction learning for pathology vision and language analysis,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Cost-effective instruction learning for pathology vision and language analysis,

Reference 2

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Observation 14d5be11-0701-47a4-be48-7f6c5811f653 · outbound

This paper cites Quilt-llava: Visual instruction tuning by ex- tracting localized narratives from open-source histopathology videos,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Quilt-llava: Visual instruction tuning by ex- tracting localized narratives from open-source histopathology videos,

Reference 3

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Observation 4b806383-a15b-448f-9e01-06b533758a12 · outbound

This paper cites Patho-r1: A multimodal reinforcement learning-based pathology expert reasoner,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Patho-r1: A multimodal reinforcement learning-based pathology expert reasoner,

Reference 4

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Observation 67782d64-5244-4485-8913-8d328a18492e · outbound

This paper cites Pathreasoner-r1: Instilling structured reasoning into pathology vision-language model via knowledge-guided policy opti- mization,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Pathreasoner-r1: Instilling structured reasoning into pathology vision-language model via knowledge-guided policy opti- mization,

Reference 5

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Observation b62c09b8-fbbf-4923-accb-59c1f7596b5b · outbound

This paper cites Pathlens: A lightweight multimodal reasoner for in-depth pathology insights,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Pathlens: A lightweight multimodal reasoner for in-depth pathology insights,

Reference 6

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Observation 65bb18ca-a5aa-44b0-91ce-83ac50cb7190 · outbound

This paper cites Slidechat: A large vision-language assistant for whole- slide pathology image understanding,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Slidechat: A large vision-language assistant for whole- slide pathology image understanding,

Reference 7

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Observation 72c65daa-182d-4f5e-b4e0-d247f4e65c6d · outbound

This paper cites Wsi-llava: A multimodal large language model for whole slide image,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Wsi-llava: A multimodal large language model for whole slide image,

Reference 8

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Observation b991f856-d14e-403c-98b8-fd974b44459d · outbound

This paper cites Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the digital pathology association,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the digital pathology association,

Reference 9

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source=pdf_text observed=2026-07-30T12:15:44.904924Z digest=sha256:a4319424e81f8105d07801e4e30c0787cfcad06efa2bb1c92484d924e6e14bc1

Observation 66516ef9-daed-4106-a691-c7aa32ddae1f · outbound

This paper cites Pathologist-level interpretable whole-slide cancer diagnosis with deep learning,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Pathologist-level interpretable whole-slide cancer diagnosis with deep learning,

Reference 10

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Observation 04b374cf-c525-4453-b269-6f5b2781f62b · outbound

This paper cites Multi-scale domain-adversarial multiple-instance cnn for cancer subtype classification with unannotated histopathological images,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Multi-scale domain-adversarial multiple-instance cnn for cancer subtype classification with unannotated histopathological images,

Reference 11

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Observation 898a9a9f-40fa-48aa-ae4a-ee7fc556d196 · outbound

This paper cites Time-lapsed, large-volume, high-resolution intrav- ital imaging for tissue-wide analysis of single cell dynamics,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Time-lapsed, large-volume, high-resolution intrav- ital imaging for tissue-wide analysis of single cell dynamics,

Reference 12

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Observation 76bb41eb-279f-4c6c-b0fc-f8707679dbbf · outbound

This paper cites A stepwise approach to fine needle aspiration cytology of lymph nodes,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis A stepwise approach to fine needle aspiration cytology of lymph nodes,

Reference 13

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source=pdf_text observed=2026-07-30T12:15:45.139933Z digest=sha256:c4e366c1fb954f9e33d730d055e5e2fb77e7d9d9aa5397dc18e31b90ff218f6b

Observation 41b12278-34d2-4e3a-b82e-30b4186077d3 · outbound

This paper cites PathVQA: 30000+ Questions for Medical Visual Question Answering.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis PathVQA: 30000+ Questions for Medical Visual Question Answering

Reference 14

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Observation f1a0746c-24f5-4142-96e9-c866609a71bf · outbound

This paper cites Pathmmu: A massive multimodal expert- level benchmark for understanding and reasoning in pathology,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Pathmmu: A massive multimodal expert- level benchmark for understanding and reasoning in pathology,

Reference 15

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Observation e25042e0-a6b7-4f98-beea-18eb8a1fbfaa · outbound

This paper cites Pathbench: Advancing the bench- mark of large multimodal models for pathology image understanding at patch and whole slide level,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Pathbench: Advancing the bench- mark of large multimodal models for pathology image understanding at patch and whole slide level,

Reference 16

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source=pdf_text observed=2026-07-30T12:15:45.416455Z digest=sha256:88f9a0be798472fc41584c0a991062e9efa0ff3c48874ddd70f2b8e60ac2fc4c

Observation 658eda9e-180d-48fb-aa3d-37c6ad5a7761 · outbound

This paper cites Wsi-vqa: Interpreting whole slide images by generative visual question answering,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Wsi-vqa: Interpreting whole slide images by generative visual question answering,

Reference 17

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Observation e3c179d2-b0ca-4199-9406-1edb26a6f507 · outbound

This paper cites Breaking the visual shortcuts in multimodal knowledge- based visual question answering,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Breaking the visual shortcuts in multimodal knowledge- based visual question answering,

Reference 18

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Observation f7a4733e-075f-43f6-a4bb-e20391b12951 · outbound

This paper cites A negative case analysis of visual grounding methods for VQA,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis A negative case analysis of visual grounding methods for VQA,

Reference 19

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Observation 34c0963c-a45b-4b84-80a1-50b098ec32ae · outbound

This paper cites Don’t just assume; look and answer: Overcoming priors for visual question answering,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Don’t just assume; look and answer: Overcoming priors for visual question answering,

Reference 20

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Observation 643b5d9a-5726-47f2-8400-c634f557c221 · outbound

This paper cites Mirage: The illusion of visual understanding,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Mirage: The illusion of visual understanding,

Reference 21

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Observation b3793fa5-d2ce-4533-913e-1b6f1ac9c41b · outbound

This paper cites Enhancing Pathological VLMs with Cross-scale Reasoning.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Enhancing Pathological VLMs with Cross-scale Reasoning

Reference 22

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Observation 51df303c-b54f-4c98-9607-f5ed69743461 · outbound

This paper cites Visual instruction tuning,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Visual instruction tuning,

Reference 23

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Observation 3ae3f376-de8f-4b8a-92af-aee26eea5c2e · outbound

This paper cites Qwen2.5-VL Technical Report.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Qwen2.5-VL Technical Report

Reference 24

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Observation 8b95ccae-3e06-4833-a803-8bfc42a0d2cd · outbound

This paper cites Qwen3-VL Technical Report.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Qwen3-VL Technical Report

Reference 25

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source=pdf_text observed=2026-07-30T12:15:45.989441Z digest=sha256:4f8d46cdc4ea61e970aae25e6c5557bf424686b41ed81f748cae6ba2521fb96c

Observation aa4cef6b-442a-4620-8e20-cc3bd0bc2cf8 · outbound

This paper cites InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency

Reference 26

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Observation f0fc1003-4edd-4c8c-b635-4e2aea0a570f · outbound

This paper cites Llava-med: Training a large language-and-vision assistant for biomedicine in one day,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Llava-med: Training a large language-and-vision assistant for biomedicine in one day,

Reference 27

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source=pdf_text observed=2026-07-30T12:15:46.194750Z digest=sha256:294a4edc8ad9142856a5799c371cf2c35874210b9b1b7f22d9ac5d73032ba442

Observation 18de4598-5879-4bad-8197-b86e765aaef9 · outbound

This paper cites Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning

Reference 28

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Observation ad9fef86-1629-4411-bc13-560affc40db4 · outbound

This paper cites HuatuoGPT, towards Taming Language Model to Be a Doctor.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis HuatuoGPT, towards Taming Language Model to Be a Doctor

Reference 29

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Observation 5a120dbb-c0f5-4499-a6c5-d945f3d8c9c0 · outbound

This paper cites Pathasst: A generative foundation ai assistant towards artificial general intelligence of pathology,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Pathasst: A generative foundation ai assistant towards artificial general intelligence of pathology,

Reference 30

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Observation a11c3056-4774-468b-bdcf-14c32421a434 · outbound

This paper cites Tcgabiolinks: an r/bioconductor package for integrative analysis of tcga data,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Tcgabiolinks: an r/bioconductor package for integrative analysis of tcga data,

Reference 31

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Observation 851fac01-f0d6-40b2-a64b-7c6f3a0d4311 · outbound

This paper cites OpenAI GPT-5 System Card.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis OpenAI GPT-5 System Card

Reference 32

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source=pdf_text observed=2026-07-30T12:15:46.541466Z digest=sha256:6bec88fbe8fc3557c8acff81f1f79a61c504b286222c66fd163a9ef1a2ad57c1

Observation f92e19af-a499-46a0-a142-1bf274ab3db7 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Gemini: A Family of Highly Capable Multimodal Models

Reference 33

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Observation bd052d8c-2856-49d8-9fdb-f476e25972be · outbound

This paper cites Qwen3-max: Just scale it,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Qwen3-max: Just scale it,

Reference 34

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Observation 36d32ae2-2382-4276-b005-c1bf07e92a36 · outbound

This paper cites LIMO: Less is More for Reasoning.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis LIMO: Less is More for Reasoning

Reference 35

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Observation 24c3a196-d1e5-49c5-a41d-af4c945b019d · outbound

This paper cites Lima: Less is more for alignment,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Lima: Less is more for alignment,

Reference 36

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Observation 085e5997-73c8-4dbc-8d97-7d97997397d2 · outbound

This paper cites Qwen3.5: Towards native multimodal agents,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Qwen3.5: Towards native multimodal agents,

Reference 37

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source=pdf_text observed=2026-07-30T12:15:46.994730Z digest=sha256:a1810c2fa4831fa7e02c837e2027ff549d46ed87d6f8321bee31d5b60c1838ef

Observation aed23b5c-e745-415a-b6b3-938495a5684a · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 38

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source=pdf_text observed=2026-07-30T12:15:47.126184Z digest=sha256:00eb9ab006cc215fcc0d2423910d1627030fa6778c9417bfba255d7bdd94bb27

Observation 38a6600d-e2b8-4c86-bd0e-470aeb0b9fee · outbound

This paper cites MiMo-VL Technical Report.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis MiMo-VL Technical Report

Reference 39

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no resolver link, observed 2026-07-30T12:15:47.193300Z

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source=pdf_text observed=2026-07-30T12:15:47.193300Z digest=sha256:93701ce23030d6836cda629a9ad5f54a86bed17e66ae4b1c9e43b0ca22b2203e

Observation 68353b25-51cd-4232-8db9-8f3c19d66c22 · outbound

This paper cites Qoq-med: Building multimodal clinical foundation models with domain-aware grpo training,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Qoq-med: Building multimodal clinical foundation models with domain-aware grpo training,

Reference 40

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source=pdf_text observed=2026-07-30T12:15:47.254915Z digest=sha256:e29393b96f31b7105689872cff4effc86fa80ae792d67e1eb59804b8d4f8e7c5

Observation 1d7a5cf1-6da0-4ce0-a2e8-4b284ea049bb · outbound

This paper cites Medvlthinker: Simple baselines for multimodal med- ical reasoning,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Medvlthinker: Simple baselines for multimodal med- ical reasoning,

Reference 41

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source=pdf_text observed=2026-07-30T12:15:47.329711Z digest=sha256:94649c2b0adeeb74143e2f466d91ba3308dfd31a84cb48e60e89a9a07524e046

Observation ab068233-2047-4b50-8e07-15606eb1e655 · outbound

This paper cites Octomed: Data recipes for state-of-the-art multi- modal medical reasoning,.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Octomed: Data recipes for state-of-the-art multi- modal medical reasoning,

Reference 42

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source=pdf_text observed=2026-07-30T12:15:47.379656Z digest=sha256:c2c5d3ba29bba56adfe9f9eaf0a9127dd673465f4b43b26c1b96f629288babaa

Observation 3ea17f7d-1c56-4131-bc2a-5e0ee45de35a · outbound

This paper cites HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation

Reference 43

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source=pdf_text observed=2026-07-30T12:15:47.450859Z digest=sha256:b326be341327828680a9c229208bb6d2784d5a1cd25a5b3d7a4efb9f6958cade

Observation f88243c9-666b-47f3-a5da-7aef515dee10 · outbound

This paper cites Available: https://qwen.ai/blog?id=qwen3.5.

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis Available: https://qwen.ai/blog?id=qwen3.5

Reference 2026

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source=pdf_text observed=2026-07-30T12:15:47.062863Z digest=sha256:e8088754a88e77b4a0546e938f3748ebb37b1eda4081756a47b670084cf41626

Pith citing papers

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