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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=sha256:2d749342b8215c7fe2f427275fe1e77a698d883fba60d78b0a04de4fa2743588

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=sha256:4581a87fb5a6af54083fcecc139c8066fbe214048a2e735cca34925ba41bffdf

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=sha256:5dba41f5c83497d4b48234522ecdda5cbf922c9a68a721f1f8fb8fb3bb2d006a

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:b5b37fec57ec70f033f70ca2d90419cd59d913f70806ad48a98c8bc57228754e

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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source=arxiv_source observed=2026-08-05T11:15:19.380664Z digest=sha256:4f624c18bd3947c4db8cc0b121da04d1d9f78efdd35d65bedd326169975f0164

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:b94504aa26454fc9c35f0201919732346aab97f9439e56ce8941ae78d20dac0f

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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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:ae0cfe5e63b17828b41e5f31b450d4aebf955132141db15ac4e9fb84831d0e4a

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:c657d19ee9a82506b4f83db26c58c9bcb9a3a532c9298c88b8246dae7ee85aeb

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=sha256:9dc4ee57d2e0ca9cfb66415578addfb68b9621348fe77e0c31294e2027c97817

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:a4654b38eae246dafe878ec0c4be851573fc528c641d7935ea234e2016917e35

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=sha256:0cf4f7f8025209c4104114ef19395ec069e10397a4cf19eebfd664c2f936928a

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=sha256:9fbd3ab5d0d0da3d2baced23e0d1fb982617c09f09ca7a19b2c6a98389340b03

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=sha256:83494d3b72519b253fbde9fd6aaa76e05363cca8c0fd6eab6a53e01e2a630ecd

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:fb6f12670e8e386d1b822a329f5500809040a3dd3f298c327880c1dcdd6c562f

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=sha256:5ba5723c911a393e6574afa62bc730f18d721bdfa01723ececf63616f51ebdad

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:cd30ae9146e51fd4307deed6401eba51b1099ad9305ba60973b7bbb554a0f241

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:f70642c8126b5bae29e4e6104afa5e53e45dae6bdebbea7d56a790193be83a62

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:bc2d5d092904cd1c19059402706c37e422fc7a15477f0ba2bae9d5ac09f28190

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=sha256:615dc98e59fb696facc8db3b2f0bb54dbebcf03379c3713128db4afff3b1a49e

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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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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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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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=sha256:032a526ccd144a915a6bfe7dcd8b11812a44faee7ce2e2b066ce68f057a7b838

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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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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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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source=arxiv_source observed=2026-08-05T11:15:20.624737Z digest=sha256:3ebac0c89724a16f150ceed511b1dce30f7ae7ae76815a19983a1fd0e582ae64

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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source=arxiv_source observed=2026-08-05T11:15:20.664746Z digest=sha256:8be1374ba22543297febdc730c22b890a83ea886462af5b8c32f810cc85986f1

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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source=arxiv_source observed=2026-08-05T11:15:20.714796Z digest=sha256:4fa3cb720b1db1b8da9ac966927589f9bc7f95391951a216b806d9364f53f3f7

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=sha256:85a1c7d372c9b3c039642d9fd12c8548a85e47587382a435075e94c80c80c183

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:a54508993fc4de64826c1994a6c5d715dc1d11b739747ee8207c5b5372a3ae55

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

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source=arxiv_source observed=2026-08-05T11:15:21.804745Z digest=sha256:feea62ad8bd1bc21ba3882ec9b6e3104c88f5c8839cb5e156d4c294e2a738226

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=sha256:69d619793be96ed9b0573720046ece7d34f87c1025cbe722e146fdee0208fb6a

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

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source=arxiv_source observed=2026-08-05T11:15:21.874924Z digest=sha256:443570657e2dc5109fa1151b974f4af2d62d389db6ae4c070b12c6fbf0502044

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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source=arxiv_source observed=2026-08-05T11:15:21.942968Z digest=sha256:7f12f235d5fe58615332ca33cfa99dffb103540d11f07b53af089b7fc66bbbbc

Pith citing papers

No inbound Pith citation observations are available.