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Paper Citation Record · LEDGER

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image

As of 5 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2607.19261.

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

pith.paper-citation-record.v1
2607.19261 v2

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measured 44 of 44 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-01T12:57:46.844001Z

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 f02a88b9-ad99-4f65-9378-68040959cc33 · outbound

This paper cites Decoding the Visual Attention of Pathologists to Reveal Their Level of Expertise,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image Decoding the Visual Attention of Pathologists to Reveal Their Level of Expertise,

Reference 1

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Observation b81a7b27-ba32-4303-af5f-ffa217aee575 · outbound

This paper cites A multimodal generative AI copilot for human pathology,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image A multimodal generative AI copilot for human pathology,

Reference 2

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Observation 0d28e8ee-0b38-42b3-bcfb-c9177a25ca17 · outbound

This paper cites PathAgent: Toward Interpretable Analysis of Whole-slide Pathology Images via Large Language Model-based Agentic Reasoning,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image PathAgent: Toward Interpretable Analysis of Whole-slide Pathology Images via Large Language Model-based Agentic Reasoning,

Reference 3

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Observation f9aec5db-54a5-4c66-8c25-d594a202a0df · outbound

This paper cites PathMMU: A Massive Multimodal Expert-Level Bench- mark for Understanding and Reasoning in Pathology,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image PathMMU: A Massive Multimodal Expert-Level Bench- mark for Understanding and Reasoning in Pathology,

Reference 4

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Observation ae4eb3ea-182c-4846-bdf2-1ba5f8b3c0a1 · outbound

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

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image WSI-LLaV A: A multimodal large language model for whole slide image,

Reference 5

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Observation ddd442d2-de51-4539-8555-8e71fd571864 · outbound

This paper cites The Cancer Genome Atlas Pan-Cancer analysis project,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image The Cancer Genome Atlas Pan-Cancer analysis project,

Reference 6

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Observation 8c132fc9-8b8d-4959-8b4b-1a0a26ac4c58 · outbound

This paper cites Update to GPT-5 system card: GPT-5.2,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image Update to GPT-5 system card: GPT-5.2,

Reference 7

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Observation 426443d2-663b-46a9-a1b7-54e3f6765c87 · outbound

This paper cites Gemini 3 Flash: Model Card,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image Gemini 3 Flash: Model Card,

Reference 8

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Observation 5c372c19-42c1-4a15-83eb-1a2ae3888886 · outbound

This paper cites Grok 4 Model Card,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image Grok 4 Model Card,

Reference 9

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source=pdf_text observed=2026-08-01T12:57:43.144641Z digest=sha256:371dcd92b822a1ae9c0c82a970aca79a5cee6b915b37a5cf8d89ca50f31d85e2

Observation caaa879c-5924-4647-84e8-4307d9356b73 · outbound

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

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling,

Reference 10

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source=pdf_text observed=2026-08-01T12:57:43.247431Z digest=sha256:ace2e8aab31546e4d88117a1efddd32e7052f01efe4743cfe974000bba557a72

Observation cfc32c5e-4adc-492a-9229-df9687d7a37d · outbound

This paper cites Qwen3.5: Towards Native Multimodal Agents,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image Qwen3.5: Towards Native Multimodal Agents,

Reference 11

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source=pdf_text observed=2026-08-01T12:57:43.346165Z digest=sha256:2a886686beb19b2cfb4bb20bf0a05a0b742bf75a16079b57c106999f252784f6

Observation 5334bcf7-ce3e-4786-b027-db239fb0217f · outbound

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

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image Qwen-VL: A Versatile Vision-Language Model for Under- standing, Localization, Text Reading, and Beyond,

Reference 12

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Observation 82fa2fa1-2580-419c-9003-b7beaa249c31 · outbound

This paper cites Kimi K2.5: Visual Agentic Intelligence,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image Kimi K2.5: Visual Agentic Intelligence,

Reference 13

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source=pdf_text observed=2026-08-01T12:57:43.588904Z digest=sha256:ade1f4ce0009566ce01f6727e67cdaba616a4592b7e474eb7f6ab4e09d83d4f4

Observation 9be7dacf-1a7f-4ed2-8b24-608304e1a784 · outbound

This paper cites The Llama 3 Herd of Models,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image The Llama 3 Herd of Models,

Reference 14

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source=pdf_text observed=2026-08-01T12:57:43.724296Z digest=sha256:28d3d02eb57dedb104f4987f93337ce7b030fc60235f87ae359eaa5925e805cd

Observation ebc8f1ae-aec4-4547-9f79-db1828108818 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone,

Reference 15

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source=pdf_text observed=2026-08-01T12:57:43.872785Z digest=sha256:77052e231e89782ed030e19db304304078973e63f721bf401c6f88155bf70f85

Observation 7b663544-49d8-4a12-b757-6b05276c858b · outbound

This paper cites Improved baselines with visual instruc- tion tuning,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image Improved baselines with visual instruc- tion tuning,

Reference 16

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source=pdf_text observed=2026-08-01T12:57:43.981733Z digest=sha256:3be5c7f22ee6f73f0302233aa253ce6957a16e07eecde94d2d00c645e6b3a23b

Observation 9cbf1a0c-60d1-4565-aa5a-d287480f02dc · outbound

This paper cites LLaV A-NeXT: Stronger llms supercharge multimodal capa- bilities in the wild,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image LLaV A-NeXT: Stronger llms supercharge multimodal capa- bilities in the wild,

Reference 17

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Observation 95975be7-4f13-459c-a25a-c6a472f789f8 · outbound

This paper cites MedGemma Technical Report,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image MedGemma Technical Report,

Reference 18

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Observation 786b1da7-8511-4f89-843d-344ee486b9bf · outbound

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

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning,

Reference 19

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source=pdf_text observed=2026-08-01T12:57:44.312312Z digest=sha256:0214eabb4be1d9fb4c978788357c5878ea456acd56d2638e4b8ed49d2fd71b2b

Observation 95036d4f-23c6-4175-8c71-c2e7075f6399 · outbound

This paper cites PathAsst: A Generative Foundation AI Assistant towards Artificial General Intelligence of Pathology,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image PathAsst: A Generative Foundation AI Assistant towards Artificial General Intelligence of Pathology,

Reference 20

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source=pdf_text observed=2026-08-01T12:57:44.399171Z digest=sha256:ed723a17dd3eba7931ae85f49d1ced1c9c5744c202f09427124791fcac1460ad

Observation ee8bb84c-f2f8-4905-95ec-1cd3e0d94191 · outbound

This paper cites Quilt-LLaV A: Visual instruction tuning by extracting localized narratives from open-source histopathology videos,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image Quilt-LLaV A: Visual instruction tuning by extracting localized narratives from open-source histopathology videos,

Reference 21

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source=pdf_text observed=2026-08-01T12:57:44.489419Z digest=sha256:7dd11ccc7e46b46356cd97e8de8d759c14564abe20848d942061dca6464a6145

Observation 43881afe-63b7-446f-96ff-072d7941a2c3 · outbound

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

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image LLaV A-med: Training a large language-and-vision assistant for biomedicine in one day,

Reference 22

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source=pdf_text observed=2026-08-01T12:57:44.611960Z digest=sha256:cdadc91f45546ee94d7470e52442df67711529111f1afe3b6bc01ce7c048ff8b

Observation c217c72c-6039-4c48-9fcf-75b276251389 · outbound

This paper cites Patho-R1: A Multimodal Reinforcement Learning-Based Pathology Expert Reasoner,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image Patho-R1: A Multimodal Reinforcement Learning-Based Pathology Expert Reasoner,

Reference 23

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Observation 0607994a-500b-4308-b597-bfc162b0e9b9 · outbound

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

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image SlideChat: A large vision-language assistant for whole- slide pathology image understanding,

Reference 24

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Observation d57d1802-9ef6-4033-bbf6-da7f9b9cd656 · outbound

This paper cites PathReasoner-R1: Instilling Structured Reasoning into Pathology Vision-Language Model via Knowledge-Guided Policy Optimization,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image PathReasoner-R1: Instilling Structured Reasoning into Pathology Vision-Language Model via Knowledge-Guided Policy Optimization,

Reference 25

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Observation 19e8c54e-8d98-4312-9a51-baf3972caf42 · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image ReAct: Synergizing Reasoning and Acting in Language Models,

Reference 26

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Observation af02db03-9061-4d9c-a970-137d6b6e139a · outbound

This paper cites A survey on large language model based autonomous agents,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image A survey on large language model based autonomous agents,

Reference 27

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Observation f8e6ec90-61cc-402a-a8fe-2dbea94caeb9 · outbound

This paper cites Interactive and ex- plainable region-guided radiology report generation,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image Interactive and ex- plainable region-guided radiology report generation,

Reference 28

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Observation 664fd24d-bca3-4c13-8f2c-d86e2a53916b · outbound

This paper cites CPathAgent: An Agent-based Foundation Model for Inter- pretable High-Resolution Pathology Image Analysis Mimicking Patholo- gists’ Diagnostic Logic,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image CPathAgent: An Agent-based Foundation Model for Inter- pretable High-Resolution Pathology Image Analysis Mimicking Patholo- gists’ Diagnostic Logic,

Reference 29

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Observation 6295f5b8-afab-4605-88a2-90b1966dbf3a · outbound

This paper cites A co-evolving agentic AI system for medical imaging analysis,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image A co-evolving agentic AI system for medical imaging analysis,

Reference 30

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Observation 203cfcd7-7877-485e-9bcd-e500f54bb8d0 · outbound

This paper cites MMNavAgent: Multi-Magnification WSI Navigation Agent for Clinically Consistent Whole-Slide Analysis,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image MMNavAgent: Multi-Magnification WSI Navigation Agent for Clinically Consistent Whole-Slide Analysis,

Reference 31

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Observation 5af87088-78e1-4b52-9aca-ac13671ad06b · outbound

This paper cites PathFound: An agentic multimodal model activating evidence-seeking pathological diagnosis,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image PathFound: An agentic multimodal model activating evidence-seeking pathological diagnosis,

Reference 32

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Observation 57c3412d-7174-44ca-b77b-ea7053aec7a6 · outbound

This paper cites Evidence-based diagnostic reasoning with multi- agent copilot for human pathology,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image Evidence-based diagnostic reasoning with multi- agent copilot for human pathology,

Reference 33

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Observation 442983d1-4842-4cfb-b873-a1869e960746 · outbound

This paper cites A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model,

Reference 34

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Observation dd54ef80-545c-44d6-a2b4-671ba0d0a656 · outbound

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

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image PathVQA: 30000+ Questions for Medical Visual Question Answering,

Reference 35

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Observation 76bd10b2-30f2-4156-920e-4baad5303960 · outbound

This paper cites OmniPathoVQA: Benchmarking pathology vision– language models with Encyclopedia-scale knowledge,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image OmniPathoVQA: Benchmarking pathology vision– language models with Encyclopedia-scale knowledge,

Reference 36

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source=pdf_text observed=2026-08-01T12:57:45.884864Z digest=sha256:e0dded3a66522c5a3648bd01664f06f64aee914b01b6c11a4eec083f276552d1

Observation caaf48f4-97ce-4965-86b0-a69895f60783 · outbound

This paper cites WSI-VQA: Interpreting Whole Slide Images by Generative Visual Question Answering,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image WSI-VQA: Interpreting Whole Slide Images by Generative Visual Question Answering,

Reference 37

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source=pdf_text observed=2026-08-01T12:57:45.972255Z digest=sha256:f3cbb039971db1f5774a1f3a21b5c0e085144b363b6051986727d4aa5dd684da

Observation 9998cc2e-7411-497f-b99d-7114e6a57b91 · outbound

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

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image Pathbench: Advancing the benchmark of large multimodal models for pathology image understanding at patch and whole slide level,

Reference 38

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source=pdf_text observed=2026-08-01T12:57:46.068137Z digest=sha256:6d3e4eba1222e5be35dab71b46ad3d192c9ddec024f60ccb38feb440dd75b169

Observation 4780341c-47a4-4e50-a036-bf7816c4feee · outbound

This paper cites HealthAgentBench: A Unified Benchmark Suite of Realistic Agentic Healthcare Environments for Challenging Frontier AI Agents,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image HealthAgentBench: A Unified Benchmark Suite of Realistic Agentic Healthcare Environments for Challenging Frontier AI Agents,

Reference 39

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source=pdf_text observed=2026-08-01T12:57:46.177258Z digest=sha256:ad889005ed76711593d7acdc8e3f55e003e4721d0ebc4a83b126a896af484398

Observation 86321642-0c3a-4b7e-8372-bff721b44e4d · outbound

This paper cites Sentence-transformers/all-MiniLM-L6-v2,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image Sentence-transformers/all-MiniLM-L6-v2,

Reference 40

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source=pdf_text observed=2026-08-01T12:57:46.321792Z digest=sha256:79300c589d2101b0d7d685057005651e4fd40a7b21670bc7eb638b213f46a6ec

Observation dd46d53b-3d0b-40f3-9f06-08cb7481b781 · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks,

Reference 41

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source=pdf_text observed=2026-08-01T12:57:46.439936Z digest=sha256:5973f61652556ae665a979e7884f73502248402ac8083239c3362d4659750e19

Observation fabc6bf9-2790-4f11-8714-7018b4f4f4e6 · outbound

This paper cites Llama 3.2 vision model card,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image Llama 3.2 vision model card,

Reference 42

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source=pdf_text observed=2026-08-01T12:57:46.578985Z digest=sha256:b035292253fe1d930a1e2068f6434d927f3aaad14cba2db06387321215c2a212

Observation 6bba8361-9611-40aa-854c-7b55bef3ae90 · outbound

This paper cites Qwen2.5-VL Technical Report,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image Qwen2.5-VL Technical Report,

Reference 43

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source=pdf_text observed=2026-08-01T12:57:46.691450Z digest=sha256:35f657ae429e636328c674f9051305eed0f687353c2831a1bb5d765a931adbcb

Observation 500cf8eb-b9d5-4109-bbda-8b70cb5a31d3 · outbound

This paper cites DeepSeek-V4: Towards Highly Efficient Million- Token Context Intelligence,.

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image DeepSeek-V4: Towards Highly Efficient Million- Token Context Intelligence,

Reference 44

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source=pdf_text observed=2026-08-01T12:57:46.844001Z digest=sha256:0c336a19cfb59f9f9af793356316b28102f8780a8f1168570277d1397e5c4d99

Pith citing papers

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