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Paper Citation Record · LEDGER

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens

As of 9 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2509.03025.

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

pith.paper-citation-record.v1
2509.03025 v2

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T11:15:21.942968Z

measured 38 of 38 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

38 of 38 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved38
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9fd290ec-c49a-40cd-b8f8-6719d6653d6c · outbound

This paper cites online" 'onlinestring :=.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens online" 'onlinestring :=

Reference 1

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source=arxiv_source observed=2026-08-05T11:15:19.075015Z digest=sha256:6acc519ce08ad9c86caf195002faf5fe8aa96db39fc6b6344046e313ca547cb7

Observation e45fc0c7-f005-4cff-9752-54d6a36ae26c · outbound

This paper cites write newline.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens write newline

Reference 2

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source=arxiv_source observed=2026-08-05T11:15:19.174853Z digest=sha256:03ed49b8d880c44f34f297089a941b52017ed5c1fb85e89220f3af055f5056c9

Observation c5f706e8-2913-4c18-b3fa-61d69a653ed0 · outbound

This paper cites Qwen Technical Report.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Qwen Technical Report

Reference 3

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source=arxiv_source observed=2026-08-05T11:15:19.240348Z digest=sha256:5b487433720d5e107b7ebb3cb970d42d9912dd89557afa7d005e78f81fcc4ebc

Observation 140fb18e-6e13-4e70-a939-961a84c4f13c · outbound

This paper cites Qwen2.5-VL Technical Report.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Qwen2.5-VL Technical Report

Reference 4

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source=arxiv_source observed=2026-08-05T11:15:19.314821Z digest=sha256:14b5cc3c1d5c7e79f0e192df67c180e2710a5a6a1944e7f02f0cb265109b3a9c

Observation cd8921a2-197e-4cf1-9d0a-57d397894c8e · outbound

This paper cites an unresolved cited work.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Unresolved cited work

Reference 5

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

source=arxiv_source observed=2026-08-05T11:15:19.380664Z digest=sha256:84c0fe833cc1df608e73c5653e5314fcac7fbe460a0b8a43c0d61b199b6554e4

Observation 35a7840b-e8c0-432e-bf23-50501b317411 · outbound

This paper cites DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models

Reference 6

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source=arxiv_source observed=2026-08-05T11:15:19.424872Z digest=sha256:48f225ab21cff302f26659296d0a0084056d486b6270cba67bdfcbaa366e154d

Observation 44ec3d41-53c9-4d3c-8cf3-2c365e167fb9 · outbound

This paper cites Knowledge Neurons in Pretrained Transformers.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Knowledge Neurons in Pretrained Transformers

Reference 7

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source=arxiv_source observed=2026-08-05T11:15:19.454823Z digest=sha256:788f5307f01e93367bb82b62fc82f0b2b54bfc68e21dfc414d1dacb694e610e0

Observation 17eee560-6975-4853-a26b-84775a191a84 · outbound

This paper cites InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning

Reference 8

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source=arxiv_source observed=2026-08-05T11:15:19.504122Z digest=sha256:6f7655492352c34cf9a2d55cd1f94f1df2377dd7a10ab3eaeab3de2bc7329b2d

Observation 59f77bcd-852c-426f-9682-fe5aca9f3dda · outbound

This paper cites an unresolved cited work.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Unresolved cited work

Reference 9

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source=arxiv_source observed=2026-08-05T11:15:19.565754Z digest=sha256:9cf11ea49ba3920db3ed3dd7b7512e05cdaa7e83f2e5ebdaa31822bc762ebdbf

Observation d7359f5e-7531-4a10-af42-269294a0aa84 · outbound

This paper cites Transformer Feed-Forward Layers Are Key-Value Memories.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Transformer Feed-Forward Layers Are Key-Value Memories

Reference 10

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source=arxiv_source observed=2026-08-05T11:15:19.605919Z digest=sha256:f3ac470aca8b077754654d56332c30ac74092d400a0c9fa362a8b66c43957579

Observation 1782759a-7914-4003-aa50-63b0dcad429e · outbound

This paper cites Probing Image-Language Transformers for Verb Understanding.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Probing Image-Language Transformers for Verb Understanding

Reference 11

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source=arxiv_source observed=2026-08-05T11:15:19.644943Z digest=sha256:79e556f8709fcbe5ef18d1118e80dd73bab860abad0298b5d523c128c79817ba

Observation 0db8c516-3916-4fea-9e08-cb79509663a9 · outbound

This paper cites an unresolved cited work.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Unresolved cited work

Reference 12

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source=arxiv_source observed=2026-08-05T11:15:19.694872Z digest=sha256:2fe1ceced215abb75460c6b47466ca7fd4d53c59152fa56386da435daf96832b

Observation 0611921d-785c-4fd9-8f12-d12affa32ca2 · outbound

This paper cites GPT-4o System Card.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens GPT-4o System Card

Reference 13

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source=arxiv_source observed=2026-08-05T11:15:19.754749Z digest=sha256:daa73972f197b58db564566f35eb1958f79042a25290289f1e2fc30e96b12196

Observation 1a6324c4-8b7e-40c2-8468-3355e158e5e2 · outbound

This paper cites an unresolved cited work.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Unresolved cited work

Reference 14

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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.

source=arxiv_source observed=2026-08-05T11:15:19.804814Z digest=sha256:f9364c7513f142f9d4f5bcba17cf763796d040ff5fec332ece19af9e4e27eabc

Observation faca7dc7-0aac-4bb6-a22c-8eb1a89a9a9d · outbound

This paper cites an unresolved cited work.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Unresolved cited work

Reference 15

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source=arxiv_source observed=2026-08-05T11:15:19.854741Z digest=sha256:5445feefe69dba095ad12cf5f000224cb801e29a3267b578a227ceafafce5b39

Observation b2259f4f-248c-46b6-8d3b-3d7633350378 · outbound

This paper cites SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

Reference 16

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source=arxiv_source observed=2026-08-05T11:15:19.883714Z digest=sha256:c6705e21cfcb868bdcf6c5b3b5a0f9ef488a8e2682bd9b42b6a819aaeed5ccdd

Observation f00b1747-322d-42c3-bb09-34d3a32b087e · outbound

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

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Evaluating Object Hallucination in Large Vision-Language Models

Reference 17

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source=arxiv_source observed=2026-08-05T11:15:19.944745Z digest=sha256:e4107e65c2c345c6a65aa9c28742f370d5455f695bcc164321568845e9c56163

Observation 38e01bcd-ac2a-4ab4-b67f-4f7cdd981f9f · outbound

This paper cites an unresolved cited work.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Unresolved cited work

Reference 18

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source=arxiv_source observed=2026-08-05T11:15:19.994745Z digest=sha256:448bec6d508bb893cca33db97587a18bf6118f311ef8f136984292b6df72cc1c

Observation f8be3dd9-6ea5-4dbe-bf08-433eb8793830 · outbound

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

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens A Survey on Hallucination in Large Vision-Language Models

Reference 19

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source=arxiv_source observed=2026-08-05T11:15:20.044747Z digest=sha256:4245e69b86d374ef3e0dceb491f6dd4073bb6b9b8e4b99cd333f254a89f683ab

Observation 144a6987-07ee-4b8c-b105-220ba8b7da46 · outbound

This paper cites an unresolved cited work.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Unresolved cited work

Reference 20

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

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Observation 169ebd7b-e84c-4887-862f-b6acefb25a32 · outbound

This paper cites an unresolved cited work.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Unresolved cited work

Reference 21

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source=arxiv_source observed=2026-08-05T11:15:20.160134Z digest=sha256:fca07a0937aa3565c09f6800f91a599db165eb42b0b30e9e8b7bdf592052efc8

Observation 2928d33c-cad8-4816-bf5a-895bed7b7a40 · outbound

This paper cites an unresolved cited work.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Unresolved cited work

Reference 22

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

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Observation c54c8f8d-a42c-4b8e-8b60-b067497164a0 · outbound

This paper cites Pedregosa, G.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Pedregosa, G

Reference 23

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source=arxiv_source observed=2026-08-05T11:15:20.240136Z digest=sha256:1a63130342159613e5d35e12f77d8887b4c83c03d3526fe67db8162cdc9cd2a7

Observation 48882dc4-c906-4319-960a-5922bcdc8ce2 · outbound

This paper cites an unresolved cited work.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Unresolved cited work

Reference 24

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Observation c3df7717-3dd9-4829-b436-27c4197ecb02 · outbound

This paper cites Object Hallucination in Image Captioning.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Object Hallucination in Image Captioning

Reference 25

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source=arxiv_source observed=2026-08-05T11:15:20.314879Z digest=sha256:dbfc5dfc4d3a6cb2061048ba007f62a357591d51664c6b0c538a058666f53979

Observation 6b519b3e-4458-4a35-8011-0fae9ce3e2aa · outbound

This paper cites GLU Variants Improve Transformer.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens GLU Variants Improve Transformer

Reference 26

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source=arxiv_source observed=2026-08-05T11:15:20.386250Z digest=sha256:b952b5ff252561de85c713237f7a4d95963a18f63db026e95aa2ae4674881025

Observation 37df8272-efe6-4b51-ae33-154245d5e5fc · outbound

This paper cites Aligning Large Multimodal Models with Factually Augmented RLHF.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Aligning Large Multimodal Models with Factually Augmented RLHF

Reference 27

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source=arxiv_source observed=2026-08-05T11:15:20.468023Z digest=sha256:f09c3a0f5899927cdb87ef4efa0038ed8c1455d8fcd720b2931818b7e1412964

Observation 824d05ef-06ee-4c1c-bf5a-f50d716ff2d9 · outbound

This paper cites Gemma 3 Technical Report.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Gemma 3 Technical Report

Reference 28

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source=arxiv_source observed=2026-08-05T11:15:20.517348Z digest=sha256:bf0aa354fb1403c6a9700bb7301f20aab02357b44a997cea960dfa05565f664e

Observation 99ec4232-a442-4270-bb3f-e9012b2d1265 · outbound

This paper cites an unresolved cited work.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Unresolved cited work

Reference 29

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source=arxiv_source observed=2026-08-05T11:15:20.584749Z digest=sha256:d464092d1447ad346ba7a18633aa41a821ddc09b8fa697bfb9f3c4091a45ba53

Observation f0130519-1925-4915-80d6-1e6b840dbdb8 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 30

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no resolver link, observed 2026-08-05T11:15:20.624737Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-05T11:15:20.624737Z digest=sha256:3e5d6cb92da21fe1734d6c0f0890aeaacd1a04257d17549e02a963934256525b

Observation e4549958-45fc-402d-a459-108e2fd2e9cb · outbound

This paper cites an unresolved cited work.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Unresolved cited work

Reference 31

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no resolver link, observed 2026-08-05T11:15:20.664746Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T11:15:20.664746Z digest=sha256:cac55b8f274bf95aaa8ccaa98b04f70a956bf5436cbdac2224ee61f0b5929a7b

Observation bd5cfdaa-f2c7-4aef-ad97-981d4e6fc309 · outbound

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

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 32

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no resolver link, observed 2026-08-05T11:15:20.714796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T11:15:20.714796Z digest=sha256:9356aee6d62caa8ec118631891e86fc801fca179ef833ad1294fea3fa7f025bf

Observation d8c9da9f-a4d6-47db-b73e-c87f540b507f · outbound

This paper cites Evaluating and Analyzing Relationship Hallucinations in Large Vision-Language Models.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Evaluating and Analyzing Relationship Hallucinations in Large Vision-Language Models

Reference 33

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T11:15:20.794862Z digest=sha256:3e50f14255084c0f953d9ed929a1ba303bf05e4da5c6f7d69825ed2170658c3e

Observation 27605e66-b1cd-447e-b041-819fd3917b42 · outbound

This paper cites an unresolved cited work.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Unresolved cited work

Reference 34

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raw_fallback, observed 2026-08-05T11:15:25.564740Z

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.

source=arxiv_source observed=2026-08-05T11:15:21.744255Z digest=sha256:a436fc72e72f28d7267676533e5eb31d8b9e5377622a4072088273d9f5781046

Observation aab9b760-309d-4689-8ab9-86a6933752ee · outbound

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

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Reefknot: A Comprehensive Benchmark for Relation Hallucination Evaluation, Analysis and Mitigation in Multimodal Large Language Models

Reference 35

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no resolver link, observed 2026-08-05T11:15:21.804745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T11:15:21.804745Z digest=sha256:d0ad3591e3338f94b4444d383e611c64ddfd521adc156d854302411b4a08be27

Observation 1e0ac97f-52f7-46b0-b257-9ae5d5429c48 · outbound

This paper cites Analyzing and Mitigating Object Hallucination in Large Vision-Language Models.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Analyzing and Mitigating Object Hallucination in Large Vision-Language Models

Reference 36

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no resolver link, observed 2026-08-05T11:15:21.834742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T11:15:21.834742Z digest=sha256:75e8e8f005a433ae31cb4558f7791dc96eba2afe9eab824ab6276490aa3b0233

Observation c65b3636-dcb0-4787-afdd-3a7af49d5da9 · outbound

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

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 37

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T11:15:21.874924Z digest=sha256:bda87930adde58a76bb8a523d7c373f59b41bc95b40fe4577371aa7c60f9014b

Observation a5ee6cef-278a-4026-9b39-706473484ed7 · outbound

This paper cites IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decoding.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decoding

Reference 38

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no resolver link, observed 2026-08-05T11:15:21.942968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T11:15:21.942968Z digest=sha256:16ff04268cc0ab4175ca9a0c67d95391f4f7b014bed0f9bd23e7d5c938b357fd

Pith citing papers

No inbound Pith citation observations are available.