Paper Citation Record · LEDGER

Measuring Epistemic Humility in Multimodal Large Language Models

As of 14 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2509.09658.

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

pith.paper-citation-record.v1
2509.09658 v2

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T18:49:39.359035Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T04:38:00.482850Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T12:10:22.857104Z

Reference resolution

48 of 48 outbound references displayed

verified exact0
verified fuzzy0
unresolved48
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2c104604-99d4-459e-9b31-90b059fac6ce · outbound

This paper cites MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models.

Measuring Epistemic Humility in Multimodal Large Language Models MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.111522Z

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Unavailable: canonical work link unavailable.

Observation 7a4fc2c9-036f-4586-a17c-a5c2ea07f901 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Measuring Epistemic Humility in Multimodal Large Language Models Measuring Massive Multitask Language Understanding

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.117825Z

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Observation c9527e80-3e6e-485b-944c-106cc17fb4b6 · outbound

This paper cites https://arxiv.org/abs/2509.

Measuring Epistemic Humility in Multimodal Large Language Models https://arxiv.org/abs/2509

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.123404Z

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Observation 3e72ce9f-2169-4ecf-a594-f518133d0ea1 · outbound

This paper cites Philosophy and Phenomenological Research 94(3), 509–539 (2017).

Measuring Epistemic Humility in Multimodal Large Language Models Philosophy and Phenomenological Research 94(3), 509–539 (2017)

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.128849Z

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Observation d9719873-cfa2-4af6-b113-0b524a3509d6 · outbound

This paper cites The Journal of Positive Psychology 15(2), 155–170 (2020).

Measuring Epistemic Humility in Multimodal Large Language Models The Journal of Positive Psychology 15(2), 155–170 (2020)

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.134143Z

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Observation 7aeb92df-6c41-49d3-a6b9-ae035942950d · outbound

This paper cites In: European Conference on Com- puter Vision, pp.

Measuring Epistemic Humility in Multimodal Large Language Models In: European Conference on Com- puter Vision, pp

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.139759Z

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Observation 04f482d4-31ae-4029-a0aa-f1b2cb714d25 · outbound

This paper cites Advances in neural information processing systems36, 49250–49267 (2023).

Measuring Epistemic Humility in Multimodal Large Language Models Advances in neural information processing systems36, 49250–49267 (2023)

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.146678Z

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Observation e70ec8df-1f04-4e8d-836b-7a65585613cd · outbound

This paper cites Qwen2.5-VL Technical Report.

Measuring Epistemic Humility in Multimodal Large Language Models Qwen2.5-VL Technical Report

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.151597Z

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Observation 864c1b89-30b1-4162-bbd4-f1c5962b5d65 · outbound

This paper cites https://llava-vl.github.io/blog/ 2024-01-30-llava-next/.

Measuring Epistemic Humility in Multimodal Large Language Models https://llava-vl.github.io/blog/ 2024-01-30-llava-next/

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.156841Z

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Observation 2a62decb-1362-4af6-b498-f16a4c5b4267 · outbound

This paper cites In: Pro- ceedings of the Computer Vision and Pattern Recognition Conference, pp.

Measuring Epistemic Humility in Multimodal Large Language Models In: Pro- ceedings of the Computer Vision and Pattern Recognition Conference, pp

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.161925Z

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Observation 3c522ad6-caf0-478a-a615-3dc11257c2bf · outbound

This paper cites DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding.

Measuring Epistemic Humility in Multimodal Large Language Models DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.166954Z

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Observation 4fee22be-3be4-4ec8-b4fa-cd5049c793de · outbound

This paper cites InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models.

Measuring Epistemic Humility in Multimodal Large Language Models InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.172950Z

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Observation f0e841c4-a012-4c13-b2b3-5ea64ae74fae · outbound

This paper cites arXiv e-prints, 2407 (2024).

Measuring Epistemic Humility in Multimodal Large Language Models arXiv e-prints, 2407 (2024)

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.179161Z

Source-reported events for the cited work

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Observation e007c5f3-f26b-48c8-8d64-70271b96a6ba · outbound

This paper cites Phi-4 Technical Report.

Measuring Epistemic Humility in Multimodal Large Language Models Phi-4 Technical Report

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.184149Z

Source-reported events for the cited work

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Observation e02be18e-3e16-4771-bd53-398ebac320c2 · outbound

This paper cites Gemma 3 Technical Report.

Measuring Epistemic Humility in Multimodal Large Language Models Gemma 3 Technical Report

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.189635Z

Source-reported events for the cited work

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Observation bec5736f-fd3a-46b8-9992-1f53373fb093 · outbound

This paper cites Advances in Neural Information Processing Systems37, 87310–87356 (2024).

Measuring Epistemic Humility in Multimodal Large Language Models Advances in Neural Information Processing Systems37, 87310–87356 (2024)

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.194651Z

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Observation 374a927e-9cfd-4f37-ada0-6ea3e790d8b3 · outbound

This paper cites Pixtral 12B.

Measuring Epistemic Humility in Multimodal Large Language Models Pixtral 12B

Reference 17

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Observation 887a9bfd-dcdf-4a24-99f5-8af7eb53e7af · outbound

This paper cites Building and better understanding vision-language models: insights and future directions.

Measuring Epistemic Humility in Multimodal Large Language Models Building and better understanding vision-language models: insights and future directions

Reference 18

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Observation de9ad62c-5e59-4149-b379-19b061b1f3eb · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Measuring Epistemic Humility in Multimodal Large Language Models In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.210824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53aec416-a58f-4094-8e9d-41da5ae12f38 · outbound

This paper cites Ovis: Structural Embedding Alignment for Multimodal Large Language Model.

Measuring Epistemic Humility in Multimodal Large Language Models Ovis: Structural Embedding Alignment for Multimodal Large Language Model

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.216124Z

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Observation 4d257517-7fcc-4fae-ba0e-77b5f9c88cd8 · outbound

This paper cites Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search.

Measuring Epistemic Humility in Multimodal Large Language Models Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.220967Z

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Observation da279752-465b-4be7-9251-0e71f6996679 · outbound

This paper cites R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization.

Measuring Epistemic Humility in Multimodal Large Language Models R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.225964Z

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Observation 3b86c7c0-8c79-46f9-972a-6835decb9909 · outbound

This paper cites arXiv preprint arXiv:2505.14677 (2025).

Measuring Epistemic Humility in Multimodal Large Language Models arXiv preprint arXiv:2505.14677 (2025)

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.230894Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27e28219-6103-4299-9829-ef88fe3faeea · outbound

This paper cites LLaVA-CoT: Let Vision Language Models Reason Step-by-Step.

Measuring Epistemic Humility in Multimodal Large Language Models LLaVA-CoT: Let Vision Language Models Reason Step-by-Step

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.235545Z

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Observation d1f28b5b-82f9-46e8-af40-2d89eddcdbe0 · outbound

This paper cites R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimization.

Measuring Epistemic Humility in Multimodal Large Language Models R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimization

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.240888Z

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Observation 17c5539e-fac0-45ac-a0a3-fb078f287372 · outbound

This paper cites GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning.

Measuring Epistemic Humility in Multimodal Large Language Models GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.245932Z

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Unavailable: canonical work link unavailable.

Observation d8efb1a1-a16b-4598-a07d-c219e935cd2b · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Measuring Epistemic Humility in Multimodal Large Language Models Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.250935Z

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Observation c2cf5114-b492-4bf4-9e75-99a4b7363879 · outbound

This paper cites Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning.

Measuring Epistemic Humility in Multimodal Large Language Models Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.256180Z

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Observation f0db9edc-6391-4b59-aed1-aa5da3eb403c · outbound

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Measuring Epistemic Humility in Multimodal Large Language Models Unresolved cited work

Reference 29

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Observation 3b1420af-fd05-433b-898a-1656bd84c374 · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Measuring Epistemic Humility in Multimodal Large Language Models In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.265944Z

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Observation 45d7c96d-3eb9-4896-a615-c594a8a50d7d · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Measuring Epistemic Humility in Multimodal Large Language Models In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.270755Z

Source-reported events for the cited work

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Observation 06303beb-710c-4410-b6d8-481a5b830006 · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Measuring Epistemic Humility in Multimodal Large Language Models In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.275401Z

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Observation 6dc31240-7cae-4fc1-a29d-a668e1ca993c · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Measuring Epistemic Humility in Multimodal Large Language Models In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.280231Z

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Observation f777adf6-3eaf-4d01-9ee8-3332ee5c858c · outbound

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Measuring Epistemic Humility in Multimodal Large Language Models In: Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pp

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.285433Z

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Observation 1e7dcb10-4c53-46c0-9184-8707c702b8e3 · outbound

This paper cites In: Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Measuring Epistemic Humility in Multimodal Large Language Models In: Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.290601Z

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Unavailable: canonical work link unavailable.

Observation ae22c641-878c-4414-97a3-0198f1d3edf4 · outbound

This paper cites In: Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Measuring Epistemic Humility in Multimodal Large Language Models In: Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.295730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ba47e0b-cff9-499c-875a-e7abfa9edccc · outbound

This paper cites Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding.

Measuring Epistemic Humility in Multimodal Large Language Models Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.300900Z

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Observation ed3aaf15-bc50-4651-95cd-27e192709173 · outbound

This paper cites ACM computing surveys 55(12), 1–38 (2023).

Measuring Epistemic Humility in Multimodal Large Language Models ACM computing surveys 55(12), 1–38 (2023)

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.307133Z

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Observation 5b85c666-b45a-4994-943d-745511365d06 · outbound

This paper cites A Survey on Hallucination in Large Vision-Language Models.

Measuring Epistemic Humility in Multimodal Large Language Models A Survey on Hallucination in Large Vision-Language Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.311898Z

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Observation 427cbd89-ce35-49e3-99a0-a3f91511c1a7 · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Measuring Epistemic Humility in Multimodal Large Language Models In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.316857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcc3050a-fa84-45c5-820f-e5172451f106 · outbound

This paper cites In: European Conference on Computer Vision, pp.

Measuring Epistemic Humility in Multimodal Large Language Models In: European Conference on Computer Vision, pp

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.321669Z

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Observation 59435987-101f-430d-9c59-ffc0a1f19e2e · outbound

This paper cites Reefknot: A Comprehensive Benchmark for Relation Hallucination Evaluation, Analysis and Mitigation in Multimodal Large Language Models.

Measuring Epistemic Humility in Multimodal Large Language Models Reefknot: A Comprehensive Benchmark for Relation Hallucination Evaluation, Analysis and Mitigation in Multimodal Large Language Models

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.326525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 029c42bc-da45-4ba3-a863-519c48bdbd07 · outbound

This paper cites Evaluating Object Hallucination in Large Vision-Language Models.

Measuring Epistemic Humility in Multimodal Large Language Models Evaluating Object Hallucination in Large Vision-Language Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.331610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1693eb2d-cf39-41c4-ad17-b9fe410ec8dc · outbound

This paper cites DASH: Detection and Assessment of Systematic Hallucinations of VLMs.

Measuring Epistemic Humility in Multimodal Large Language Models DASH: Detection and Assessment of Systematic Hallucinations of VLMs

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.337652Z

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Unavailable: canonical work link unavailable.

Observation 0dbb9abd-6e67-48b8-8aa3-e0bae43edbac · outbound

This paper cites CoRR (2024).

Measuring Epistemic Humility in Multimodal Large Language Models CoRR (2024)

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.343975Z

Source-reported events for the cited work

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Observation edbeefc7-ac7e-4515-b6b2-a6f81423b780 · outbound

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Measuring Epistemic Humility in Multimodal Large Language Models In: Proceedings of the 32nd ACM International Conference on Multime- dia, pp

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.349001Z

Source-reported events for the cited work

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Observation dca8c899-cc91-4d6c-a74f-0d147906c1bc · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Measuring Epistemic Humility in Multimodal Large Language Models In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.353997Z

Source-reported events for the cited work

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Observation d46b12ac-1e3a-4c19-a9f1-cff16e093d2b · outbound

This paper cites LongHalQA: Long-Context Hallucination Evaluation for MultiModal Large Language Models.

Measuring Epistemic Humility in Multimodal Large Language Models LongHalQA: Long-Context Hallucination Evaluation for MultiModal Large Language Models

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-04T18:49:39.359035Z

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Pith citing papers

Observation 7af1d40f-d900-43e1-9061-e0f5585b3eb4 · inbound

Learning from AVA: Early Lessons from a Curated and Trustworthy Generative AI for Policy and Development Research cites this paper.

Learning from AVA: Early Lessons from a Curated and Trustworthy Generative AI for Policy and Development Research Measuring Epistemic Humility in Multimodal Large Language Models

Reference 106

Resolution: verified exact; arxiv_id, observed 2026-05-26T03:04:04.573884Z

Source-reported events for the cited work

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