Paper Citation Record · LEDGER

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning

As of 9 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2507.17539.

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

pith.paper-citation-record.v1
2507.17539 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:51:09.912275Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

verified exact3
verified fuzzy18
unresolved18
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8a110f70-fa89-4d3a-a7c8-600766682e81 · outbound

This paper cites https://www.kaggle.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning https://www.kaggle

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:11.858142Z

Source-reported events for the cited work

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Observation 30641fbc-c84a-469f-a3e7-8cd7a5208266 · outbound

This paper cites GPT-4 Technical Report.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning GPT-4 Technical Report

Reference 2

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Source-reported events for the cited work

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Observation 0c236104-9893-4689-a7c6-8fd7bd7c6b19 · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 3

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This paper cites Qwen2.5-VL Technical Report.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning Qwen2.5-VL Technical Report

Reference 4

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Observation e01d200f-64f2-460e-9ccd-df269371953f · outbound

This paper cites BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T14:51:09.575442Z

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Observation dbda4ea0-345f-4754-8794-444549350d65 · outbound

This paper cites GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T14:51:09.584692Z

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Observation c728c3a4-46c2-4d27-adb5-8f0d98c3ef10 · outbound

This paper cites Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T14:51:09.599410Z

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Observation 10b3db82-1f54-4ef2-8a1b-3c9127c2f951 · outbound

This paper cites Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:11.831072Z

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Observation 2d5e42b4-71ef-4846-99c4-5532ac791a22 · outbound

This paper cites Feedback on a publicly distributed image database: the mes- sidor database.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning Feedback on a publicly distributed image database: the mes- sidor database

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:11.776895Z

Source-reported events for the cited work

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Observation 8d947747-51f6-46b6-8a22-0da0b03bb8e7 · outbound

This paper cites Vlmevalkit: An open-source toolkit for evaluating large multi-modality models.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning Vlmevalkit: An open-source toolkit for evaluating large multi-modality models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T14:51:09.630987Z

Source-reported events for the cited work

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Observation 48c496c3-5b79-47cf-828b-60d08fc885df · outbound

This paper cites A density-based algorithm for discovering clusters in large spatial databases with noise.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning A density-based algorithm for discovering clusters in large spatial databases with noise

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:11.708342Z

Source-reported events for the cited work

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Observation 95c7979d-c478-4b31-b838-3754015fba41 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 12

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Source-reported events for the cited work

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Observation ae004e9e-4ffc-414c-8da7-09221e0218c8 · outbound

This paper cites GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist Collaboration.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist Collaboration

Reference 13

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Observation bb410425-b97e-4d66-b669-3eff77872b00 · outbound

This paper cites nnu-net: a self-configuring method for deep learning-based biomedical image segmentation.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning nnu-net: a self-configuring method for deep learning-based biomedical image segmentation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:11.676594Z

Source-reported events for the cited work

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Observation cd55d735-47b3-47bf-9053-e7529cf17c71 · outbound

This paper cites Cataract dataset.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning Cataract dataset

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:11.641504Z

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Observation 442cdd74-bc24-4c71-af40-070c65477f1d · outbound

This paper cites Machine learn for glaucoma, 2018.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning Machine learn for glaucoma, 2018

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:11.594770Z

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Observation 885674ca-4214-4ea3-bb52-5fc2a06de907 · outbound

This paper cites an unresolved cited work.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning Unresolved cited work

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d2575e56-6150-4d3c-b704-121b55d062ce · outbound

This paper cites Inte- grated image-based deep learning and language models for primary diabetes care.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning Inte- grated image-based deep learning and language models for primary diabetes care

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:11.454936Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5ac1da87-6deb-4df2-bf72-71d2eb7e8566 · outbound

This paper cites Applications of deep learning in fundus images: A review.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning Applications of deep learning in fundus images: A review

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:11.414629Z

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Observation 8bacb87c-e481-4cec-ab32-258917010cad · outbound

This paper cites GMAI-VL & GMAI-VL-5.5M: A Large Vision-Language Model and A Comprehensive Multimodal Dataset Towards General Medical AI.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning GMAI-VL & GMAI-VL-5.5M: A Large Vision-Language Model and A Comprehensive Multimodal Dataset Towards General Medical AI

Reference 20

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Source-reported events for the cited work

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Observation 2f581897-944b-4e0f-a6d3-83ae68eff166 · outbound

This paper cites VisionUnite: A Vision-Language Foundation Model for Ophthalmology Enhanced with Clinical Knowledge.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning VisionUnite: A Vision-Language Foundation Model for Ophthalmology Enhanced with Clinical Knowledge

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-06T14:51:10.564461Z

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Observation daa75123-3fc5-435e-bf8e-2d8ddc24a037 · outbound

This paper cites Improved baselines with visual instruction tuning.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning Improved baselines with visual instruction tuning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:11.367408Z

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Observation 1976a5ae-663f-417a-821e-5c63be37d090 · outbound

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Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning Visual instruction tuning

Reference 23

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This paper cites Brset: a brazilian multilabel oph- thalmological dataset of retina fundus photos.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning Brset: a brazilian multilabel oph- thalmological dataset of retina fundus photos

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:11.268614Z

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Observation bf239752-01ba-4f24-9aab-caa27d8b1106 · outbound

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Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning Unresolved cited work

Reference 25

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Observation e65a7dae-c1d4-4110-afcf-791861f4daeb · outbound

This paper cites Indian diabetic retinopathy image dataset (idrid): a database for diabetic retinopathy screening research.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning Indian diabetic retinopathy image dataset (idrid): a database for diabetic retinopathy screening research

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:11.189944Z

Source-reported events for the cited work

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Observation 2d7744a4-689b-45f0-baa8-4c518a9fb767 · outbound

This paper cites Development and validation of a multimodal multitask vision foundation model for generalist ophthalmic artificial intelligence.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning Development and validation of a multimodal multitask vision foundation model for generalist ophthalmic artificial intelligence

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:11.119805Z

Source-reported events for the cited work

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Observation f95efed6-0b60-4769-9314-ac376656c591 · outbound

This paper cites EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysis.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysis

Reference 28

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

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning Gemini: A Family of Highly Capable Multimodal Models

Reference 29

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This paper cites Towards generalist biomedical ai.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning Towards generalist biomedical ai

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:11.091170Z

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Observation 023d8454-96d4-4a4a-97a0-8e866b234180 · outbound

This paper cites Interpretable Bilingual Multimodal Large Language Model for Diverse Biomedical Tasks.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning Interpretable Bilingual Multimodal Large Language Model for Diverse Biomedical Tasks

Reference 31

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Observation f1469811-c0e4-453d-a45a-c297e044486e · outbound

This paper cites Enhancing Diagnostic Accuracy in Rare and Common Fundus Diseases with a Knowledge-Rich Vision-Language Model.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning Enhancing Diagnostic Accuracy in Rare and Common Fundus Diseases with a Knowledge-Rich Vision-Language Model

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-06T14:51:10.337879Z

Source-reported events for the cited work

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Observation 8cc811b4-92ce-4254-ac28-8a79be4fefc4 · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 33

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Observation 6e8c93da-db17-4a35-972e-9d56171c62b3 · outbound

This paper cites Mm-retinal: Knowledge-enhanced foun- dational pretraining with fundus image-text expertise.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning Mm-retinal: Knowledge-enhanced foun- dational pretraining with fundus image-text expertise

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:11.042568Z

Source-reported events for the cited work

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Observation 60298160-bb4a-4d03-9e2c-2e47c5625230 · outbound

This paper cites MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine

Reference 35

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Observation a0b01b89-2e25-4f44-9154-afae47716257 · outbound

This paper cites Vilref: An expert knowledge enabled vision-language retinal foundation model.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning Vilref: An expert knowledge enabled vision-language retinal foundation model

Reference 36

Resolution: verified exact; raw_fallback, observed 2026-08-06T14:51:10.128451Z

Source-reported events for the cited work

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Observation 8ecae783-eeae-4516-a322-ebc9aa1d68dc · outbound

This paper cites Automorph: automated retinal vascu- lar morphology quantification via a deep learning pipeline.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning Automorph: automated retinal vascu- lar morphology quantification via a deep learning pipeline

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:10.996885Z

Source-reported events for the cited work

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Observation 63faca54-ccbe-4b40-b6fd-af346e2d00c5 · outbound

This paper cites A foundation model for generalizable disease detection from retinal images.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning A foundation model for generalizable disease detection from retinal images

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:10.954955Z

Source-reported events for the cited work

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Observation ee99be9a-466b-4cd5-a45e-22010c0ea874 · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T14:51:09.912275Z

Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.