Paper Citation Record · LEDGER

On the robustness of multimodal language model towards distractions

As of 8 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 2 inbound Pith citation observations for arXiv:2502.09818.

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

pith.paper-citation-record.v1
2502.09818 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T20:25:58.596793Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T01:00:27.572834Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T11:11:17.919527Z

Reference resolution

37 of 37 outbound references displayed

verified exact0
verified fuzzy23
unresolved13
parse uncertain1
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d37f96c1-4c41-4b8c-919a-0a402e321173 · outbound

This paper cites Vqa: Visual question answering.

On the robustness of multimodal language model towards distractions Vqa: Visual question answering

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:25:59.350990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c5f01393-06f9-4c4a-8a5f-d819ccc290c6 · outbound

This paper cites Choquette- Choo, Matthew Jagielski, Irena Gao, Anas Awadalla, Pang Wei Koh, Daphne Ippolito, Katherine Lee, Florian Tramer, and Ludwig Schmidt.

On the robustness of multimodal language model towards distractions Choquette- Choo, Matthew Jagielski, Irena Gao, Anas Awadalla, Pang Wei Koh, Daphne Ippolito, Katherine Lee, Florian Tramer, and Ludwig Schmidt

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:25:59.331941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b75241f2-2493-4802-9562-c21873821f9d · outbound

This paper cites How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites.

On the robustness of multimodal language model towards distractions How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T20:25:58.396754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dffe2634-4015-4213-af49-7a35f2e7c4c4 · outbound

This paper cites HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding.

On the robustness of multimodal language model towards distractions HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T20:25:58.403744Z

Source-reported events for the cited work

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Observation 1e68e2c4-f4ec-469a-9f33-44081099c8c6 · outbound

This paper cites Instructblip: Towards general- purpose vision-language models with instruction tuning,.

On the robustness of multimodal language model towards distractions Instructblip: Towards general- purpose vision-language models with instruction tuning,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T20:25:58.410363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0cede9a2-b671-4098-82ae-3f68818a795a · outbound

This paper cites How Robust is Google's Bard to Adversarial Image Attacks?.

On the robustness of multimodal language model towards distractions How Robust is Google's Bard to Adversarial Image Attacks?

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T20:25:58.416124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fb5e3ff-151b-4fce-97c8-024e0321cc11 · outbound

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

On the robustness of multimodal language model towards distractions MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T20:25:58.422649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32aa141f-b92e-476b-97fd-c2eb6c6fdb1d · outbound

This paper cites Phi3v-finetuning, 2023.

On the robustness of multimodal language model towards distractions Phi3v-finetuning, 2023

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:25:59.300025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 19b751bb-1b39-44f4-8568-0f24ab94da08 · outbound

This paper cites HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models.

On the robustness of multimodal language model towards distractions HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T20:25:58.434448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8e5ac55-2ec6-4eed-b9b3-ba4cff84de8e · outbound

This paper cites Cogvlm2: Visual language models for image and video understanding, 2024.

On the robustness of multimodal language model towards distractions Cogvlm2: Visual language models for image and video understanding, 2024

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:25:59.281755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 16072865-e658-44f3-ad2a-1c9ca4557ae9 · outbound

This paper cites Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages.

On the robustness of multimodal language model towards distractions Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T20:25:58.446123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbc59deb-19da-45ee-865c-468d971daa7f · outbound

This paper cites Gqa: A new dataset for real-world visual reasoning and compositional question answering.

On the robustness of multimodal language model towards distractions Gqa: A new dataset for real-world visual reasoning and compositional question answering

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:25:59.262535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4a7358af-0757-499b-bb12-dfbab7ebdd5b · outbound

This paper cites Open- clip, 2021.

On the robustness of multimodal language model towards distractions Open- clip, 2021

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T20:25:58.458116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3ac1863-e170-4d35-8487-75b3139b806d · outbound

This paper cites Adversarial examples for evaluating math word problem solvers.

On the robustness of multimodal language model towards distractions Adversarial examples for evaluating math word problem solvers

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:25:59.230192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 69848a13-51bb-4a10-a771-a3e89c5162d9 · outbound

This paper cites LISA: Reasoning Segmentation via Large Language Model.

On the robustness of multimodal language model towards distractions LISA: Reasoning Segmentation via Large Language Model

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T20:25:58.469758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b327ad1f-67ed-4043-8270-8c0e367b89bb · outbound

This paper cites Evaluating object hallucination in large vision-language models.

On the robustness of multimodal language model towards distractions Evaluating object hallucination in large vision-language models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T20:25:58.476188Z

Source-reported events for the cited work

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Observation 010bf48e-4ce5-44fe-a456-0a2d80a7dd25 · outbound

This paper cites Visual instruction tuning.

On the robustness of multimodal language model towards distractions Visual instruction tuning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:25:59.200014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2394962e-aa9d-4613-86c3-49bfbe126f3e · outbound

This paper cites MMBench: Is Your Multi-modal Model an All-around Player?.

On the robustness of multimodal language model towards distractions MMBench: Is Your Multi-modal Model an All-around Player?

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T20:25:58.486806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38ffbaa5-1e8d-4562-aeaa-3b5fac7cd4cd · outbound

This paper cites Learn to explain: Multimodal reasoning via thought chains for science question answering.

On the robustness of multimodal language model towards distractions Learn to explain: Multimodal reasoning via thought chains for science question answering

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:25:59.177301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cd7b49c2-a6e2-43bb-9766-1022caeef332 · outbound

This paper cites Mathvista: Evaluating mathemat- ical reasoning of foundation models in visual contexts, 2024.

On the robustness of multimodal language model towards distractions Mathvista: Evaluating mathemat- ical reasoning of foundation models in visual contexts, 2024

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:25:59.157478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 06812f5e-2460-458d-828b-3204904a853d · outbound

This paper cites Understanding zero-shot adversarial robust- ness for large-scale models.

On the robustness of multimodal language model towards distractions Understanding zero-shot adversarial robust- ness for large-scale models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:25:59.136750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3b5c2d96-6aef-4ffc-a56e-fd306bc2d6c2 · outbound

This paper cites Gpt-4v(ision) system card.

On the robustness of multimodal language model towards distractions Gpt-4v(ision) system card

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T20:25:58.508674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc9d91b8-b302-4cf8-aba5-62fb66f13ecf · outbound

This paper cites Gpt-3.5 turbo.

On the robustness of multimodal language model towards distractions Gpt-3.5 turbo

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:25:59.102629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 403be801-f927-4acd-b089-efe41a4ba526 · outbound

This paper cites Are nlp models really able to solve simple math word problems? In NAACL-HLT, 2021.

On the robustness of multimodal language model towards distractions Are nlp models really able to solve simple math word problems? In NAACL-HLT, 2021

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:25:59.081273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b32d299b-6207-43d8-96f0-8fead312d049 · outbound

This paper cites Homepage.

On the robustness of multimodal language model towards distractions Homepage

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:25:59.061065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3439da96-30d7-4e0b-b9f1-8b278ed8d560 · outbound

This paper cites Visual adversarial examples jailbreak aligned large language models.

On the robustness of multimodal language model towards distractions Visual adversarial examples jailbreak aligned large language models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:25:59.008222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f6299b28-b9e6-4049-91a2-2ea929020757 · outbound

This paper cites Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer.

On the robustness of multimodal language model towards distractions Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:25:58.986614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a5ce974a-f315-43a0-a3e6-d0e34f3137da · outbound

This paper cites On the adversarial robustness of multi-modal foundation models, 2023.

On the robustness of multimodal language model towards distractions On the adversarial robustness of multi-modal foundation models, 2023

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:25:58.967524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f81d975d-aed2-4629-8323-c292deccde64 · outbound

This paper cites Robust clip: Unsupervised ad- versarial fine-tuning of vision embeddings for robust large vision-language models.

On the robustness of multimodal language model towards distractions Robust clip: Unsupervised ad- versarial fine-tuning of vision embeddings for robust large vision-language models

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:25:58.948610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 87b13a6c-1f33-46f3-b4c0-dc1caf2df972 · outbound

This paper cites Large language models can be easily distracted by irrelevant context, 2023.

On the robustness of multimodal language model towards distractions Large language models can be easily distracted by irrelevant context, 2023

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:25:58.928046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4a016203-6941-4686-b224-43f566834c56 · outbound

This paper cites Towards vqa models that can read.

On the robustness of multimodal language model towards distractions Towards vqa models that can read

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:25:58.908306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a31188e5-e4eb-4d69-bb43-306c9759323f · outbound

This paper cites Unsplash api.

On the robustness of multimodal language model towards distractions Unsplash api

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:25:58.889018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 88d427a4-54cc-4ea6-92a4-42de4e6d29f4 · outbound

This paper cites Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution, 2024.

On the robustness of multimodal language model towards distractions Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution, 2024

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:25:58.870325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 511b0599-cc63-4731-94ce-6bdcecf8d2ce · outbound

This paper cites MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities.

On the robustness of multimodal language model towards distractions MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T20:25:58.585610Z

Source-reported events for the cited work

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Observation c0365983-3f1b-44a0-9ff2-b663f50f370d · outbound

This paper cites On evaluating ad- versarial robustness of large vision-language models, 2023.

On the robustness of multimodal language model towards distractions On evaluating ad- versarial robustness of large vision-language models, 2023

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:25:58.851745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 02225cbf-15c6-4914-b4b6-8fdb52c6be58 · outbound

This paper cites Analyzing and mitigating object hallucination in large vision-language models.

On the robustness of multimodal language model towards distractions Analyzing and mitigating object hallucination in large vision-language models

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:25:58.827053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f4d9cd83-44e4-40e9-bdf3-23e12f2c026c · outbound

This paper cites an unresolved cited work.

On the robustness of multimodal language model towards distractions Unresolved cited work

Reference 2024

Resolution: parse uncertain; raw_fallback, observed 2026-08-07T20:25:59.041438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation 1d1532fb-9e7f-4bea-bd60-4c445ac19072 · inbound

Can Large Multimodal Models Actively Recognize Faulty Inputs? A Systematic Evaluation Framework of Their Input Scrutiny Ability cites this paper.

Can Large Multimodal Models Actively Recognize Faulty Inputs? A Systematic Evaluation Framework of Their Input Scrutiny Ability On the robustness of multimodal language model towards distractions

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T01:00:27.572834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a79769f4-ea2d-41a4-b959-8c00f88e3b89 · inbound

When Silence Matters: The Impact of Irrelevant Audio on Text Reasoning in Large Audio-Language Models cites this paper.

When Silence Matters: The Impact of Irrelevant Audio on Text Reasoning in Large Audio-Language Models On the robustness of multimodal language model towards distractions

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-18T11:11:17.922848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.