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Paper Citation Record · LEDGER

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation

As of 18 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2506.07202.

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

pith.paper-citation-record.v1
2506.07202 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:43:44.008852Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T08:36:21.863358Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T08:43:15.762081Z

Reference resolution

42 of 42 outbound references displayed

  • verified exact0
  • verified fuzzy15
  • unresolved26
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ca4abf32-e965-4329-8ddb-2d501f7288dd · outbound

This paper cites Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs

Reference 1

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 461a6f64-d357-4671-838d-54ffc7894c6f · outbound

This paper cites Are We on the Right Way for Evaluating Large Vision-Language Models?.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation Are We on the Right Way for Evaluating Large Vision-Language Models?

Reference 2

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Observation ff8bb995-86a5-47ab-a9af-6e3f4a808755 · outbound

This paper cites Microsoft COCO Captions: Data Collection and Evaluation Server.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation Microsoft COCO Captions: Data Collection and Evaluation Server

Reference 3

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Observation c7bd01bc-355d-4378-8430-4d52309701ae · outbound

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

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

Reference 4

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source=pdf_text observed=2026-08-07T05:43:38.541820Z digest=sha256:a5fc2bb2193669d76f4d5961d1576d6f44e3296fee7939a44273a05a918e8aa9

Observation 6e3b7325-fb3c-403b-9538-584a770ebc6b · outbound

This paper cites Le, Sergey Levine, and Yi Ma.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation Le, Sergey Levine, and Yi Ma

Reference 5

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T05:43:38.711427Z digest=sha256:a702c1faa2ada0cfe17f511c1e78eab774337df6b03d2edd22214a8890632ea2

Observation a5dce37d-d9c2-4d0b-888a-cbfc4ac61195 · outbound

This paper cites Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus

Reference 6

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source=pdf_text observed=2026-08-07T05:43:38.885301Z digest=sha256:8e00789656ad0e7aefdaf633f28a358acc9ab63ec8033fcef7556a8623099915

Observation b6d79c24-7fe5-41ca-af19-30b895ee88de · outbound

This paper cites Complex Video Reasoning and Robustness Evaluation Suite (CVRR-ES).

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation Complex Video Reasoning and Robustness Evaluation Suite (CVRR-ES)

Reference 7

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raw_fallback, observed 2026-08-07T05:43:48.912141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:43:39.042400Z digest=sha256:d8566d56cf8f48c050e4971192285198f1fecf3af99a194f2d05879ac9cab909

Observation 145fe6b8-60a8-4bd3-a933-6aa0a5eaad46 · outbound

This paper cites NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes

Reference 8

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source=pdf_text observed=2026-08-07T05:43:39.255176Z digest=sha256:f844039097393b455640597cf5b1c9ced2c620360ab675278473c5ccec3d7c02

Observation 18484495-cf17-4f71-b15a-4214b86012b5 · outbound

This paper cites Video-R1: Reinforcing Video Reasoning in MLLMs.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation Video-R1: Reinforcing Video Reasoning in MLLMs

Reference 9

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source=pdf_text observed=2026-08-07T05:43:39.375817Z digest=sha256:123f881b49d77cdd571583f230f927bafe363198bd2ee90cd846d9659997eedf

Observation 7f864f67-399d-4d94-8d52-7712ddc73784 · outbound

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

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 10

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source=pdf_text observed=2026-08-07T05:43:39.449895Z digest=sha256:747bc5daf6f4af18cfbca48bfff44a58921993d7c20413e5567c5f66d3985be8

Observation dc258f49-a426-4b23-bc29-558dde71b26f · outbound

This paper cites SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models

Reference 11

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source=pdf_text observed=2026-08-07T05:43:39.543588Z digest=sha256:50475d61e0dd5b930b9cb572cd50c66ab37903b60a7296fe35d64f7fd50bebeb

Observation 373f9932-70b9-42e9-8684-9ed355346882 · outbound

This paper cites Time travel in LLMs: Tracing data contamination in large language models.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation Time travel in LLMs: Tracing data contamination in large language models

Reference 12

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raw_fallback, observed 2026-08-07T05:43:48.603675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:43:39.598573Z digest=sha256:7acb0ab4012a9b34bceee6557f3831378004a3cf5f2bbb0b8ccc10d5e05e3c3f

Observation 6dad6892-dcbe-4f3c-8327-94cc60b97c7b · outbound

This paper cites Goodfellow, Jonathon Shlens, and Christian Szegedy.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation Goodfellow, Jonathon Shlens, and Christian Szegedy

Reference 13

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raw_fallback, observed 2026-08-07T05:43:48.325245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:43:39.737219Z digest=sha256:610cb7a55660c3485cc0a6ee8e18f87251d8eacee1185ce017b35d54a85e6f2f

Observation 03daa9c5-f96a-43e8-944a-775293501894 · outbound

This paper cites Flat minima.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation Flat minima

Reference 14

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raw_fallback, observed 2026-08-07T05:43:48.054042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:43:39.855755Z digest=sha256:648d4c7fb4608971ff929a6edf79a5d84cb4fd5905fe5d5a58032c087f1a7858

Observation 296a0b41-3fd2-40f9-ba56-9bbb5d0eb0b1 · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 15

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source=pdf_text observed=2026-08-07T05:43:40.028594Z digest=sha256:a34d8fe6b3b7d5247deb84f6da72615f1ca268b464c748c51b354f7639ce81ed

Observation 16b0f9da-ef7e-455e-a53e-c4a85c7e0b23 · outbound

This paper cites On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima

Reference 16

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source=pdf_text observed=2026-08-07T05:43:40.205852Z digest=sha256:efc9ac2719b5492ecab0911baec8ae44cdbb2dd924d8ffac9309735112a7cadf

Observation d940628d-e01b-4cad-acd2-5bf23370c18f · outbound

This paper cites LLaV A-OneVision: Easy visual task transfer.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation LLaV A-OneVision: Easy visual task transfer

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:43:40.352290Z digest=sha256:abdfa7567d5f0d3f62b39bba8f97edf5a84b7fa958685e11c91f7d40c76ec110

Observation c7cc41dc-b9de-4638-aa37-b8f895cd19a0 · outbound

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

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

Reference 18

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source=pdf_text observed=2026-08-07T05:43:40.501360Z digest=sha256:0e6c4e74312a32ae6b974f2ce9c62c40bba0a5f5e5b2d8031ceb55f975943485

Observation 56be8d9d-0980-44d9-8a08-da5276953aa3 · outbound

This paper cites VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement Fine-Tuning.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement Fine-Tuning

Reference 19

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source=pdf_text observed=2026-08-07T05:43:40.648654Z digest=sha256:2b8429e2ed02422b76ba9d6584ca31a1728e27168d5d01c9adc123c42a3e891d

Observation 954ca561-503e-4eec-9ed3-dd3d1fe530e5 · outbound

This paper cites Visual Instruction Tuning.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation Visual Instruction Tuning

Reference 20

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source=pdf_text observed=2026-08-07T05:43:40.744698Z digest=sha256:5511f136f44543e6c979922eec95b82795ac8c927d129b21054ea7e10f0e5c07

Observation a1214211-d31e-42b3-811c-b63afed3d312 · outbound

This paper cites Visual instruction tuning.Advances in neural information processing systems, 36, 2024.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation Visual instruction tuning.Advances in neural information processing systems, 36, 2024

Reference 21

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source=pdf_text observed=2026-08-07T05:43:40.944687Z digest=sha256:2dbe2c7dec122f18a9e4d8d18a9b648ed7af2200280156dedbd51ed6f9bef93c

Observation 655f1235-d44d-4cb3-89c3-0231e9e6d2cd · outbound

This paper cites On the robustness of multimodal language model towards distractions, 2025.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation On the robustness of multimodal language model towards distractions, 2025

Reference 22

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:43:41.115259Z digest=sha256:6a89df43654780324ac604ed03ac1be095eb6576732807c4ef56d3f6761110cc

Observation 9bc5acdd-12dc-415d-97c4-5eeadbcac686 · outbound

This paper cites Is your video language model a reliable judge? In The Thirteenth International Conference on Learning Representations, 2025.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation Is your video language model a reliable judge? In The Thirteenth International Conference on Learning Representations, 2025

Reference 23

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:43:41.231453Z digest=sha256:af0d87bff75923b18d157dd842129cf09eba9a6fd56fe5a47abf99683217dbf4

Observation 786f99bd-9ae8-4afc-a830-939e58122de1 · outbound

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

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation MMBench: Is Your Multi-modal Model an All-around Player?

Reference 24

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source=pdf_text observed=2026-08-07T05:43:41.387231Z digest=sha256:eb6fda15cc2f40739015060bae451bb23ecf4064f71793d5b1357aca04507484

Observation 3002a829-5539-413f-8145-f53522016021 · outbound

This paper cites The Llama 3 herd of models.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation The Llama 3 herd of models

Reference 25

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:43:41.529067Z digest=sha256:82dffecb5a53a669ff762db35621ca68bf8517f3bfa1c0bb1f18b84586a06b04

Observation 719807d8-f564-4bf8-84ec-85ffd002dfec · outbound

This paper cites Ok-vqa: A visual question answering benchmark requiring external knowledge.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation Ok-vqa: A visual question answering benchmark requiring external knowledge

Reference 26

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source=pdf_text observed=2026-08-07T05:43:41.682613Z digest=sha256:3402512beadcc09e465d59fa23eec4a0c567716e622ed16202dfc75c76de4f7b

Observation 7c71a775-0782-4af8-98a8-9bb109f52d3c · outbound

This paper cites MM-Eureka: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation MM-Eureka: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning

Reference 27

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source=pdf_text observed=2026-08-07T05:43:41.824761Z digest=sha256:4993120c24b6ee129c2471c2ca7dc953f750b3260103fafcfc4b4be877cd9208

Observation 5588a6fb-1bff-444c-affa-7cb2e6634c9a · outbound

This paper cites Introducing GPT-4.1.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation Introducing GPT-4.1

Reference 28

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raw_fallback, observed 2026-08-07T05:43:46.605726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:43:42.007117Z digest=sha256:eea24a2cc2c1ab41705c6d9977e1f32961393dd00d35cda12599ca1400c80ae2

Observation e6db3cb7-00f5-467c-a8e7-ea121436aac4 · outbound

This paper cites Introducing o3 and o4-mini: Our smartest models yet.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation Introducing o3 and o4-mini: Our smartest models yet

Reference 29

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raw_fallback, observed 2026-08-07T05:43:46.340798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:43:42.126854Z digest=sha256:9860ba0ab542a238a5e5c6b2004fac9475b8243852dac2f70dfe8f6239cc7384

Observation b7293655-e8c3-47b1-beba-d9e66f85cc81 · outbound

This paper cites Chatterji, Faisal Ladhak, and Tatsunori Hashimoto.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation Chatterji, Faisal Ladhak, and Tatsunori Hashimoto

Reference 30

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raw_fallback, observed 2026-08-07T05:43:45.997943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:43:42.312172Z digest=sha256:9a7e91e3144548cdaf5ccf52abbc3f7d76712936ca066fd3f4924582e6821dd3

Observation e33612d5-bdef-4b89-8fd5-1966ba17a24f · outbound

This paper cites Qwen2.5-VL Technical Report.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation Qwen2.5-VL Technical Report

Reference 31

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raw_fallback, observed 2026-08-07T05:43:45.644329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:43:42.449448Z digest=sha256:48b5a86dbbd13ca18dec75ac4f3b05f9d68aee715656747d3154d5f69f56e5ec

Observation 7fdd844e-eb3c-416b-9c9b-7bf7065b460d · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation LLaMA: Open and Efficient Foundation Language Models

Reference 32

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source=pdf_text observed=2026-08-07T05:43:42.595992Z digest=sha256:7606e479afd0a19fbc97d3ec5465b0df37dc9d7579d4dc6363e5380f2342b255

Observation 5d741959-b5bc-45b1-a7a8-54dbea542666 · outbound

This paper cites VL-Rethinker: Incentivizing Self-Reflection of Vision-Language Models with Reinforcement Learning.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation VL-Rethinker: Incentivizing Self-Reflection of Vision-Language Models with Reinforcement Learning

Reference 33

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source=pdf_text observed=2026-08-07T05:43:42.737932Z digest=sha256:78ba0d46446d9c1f23dfae34d3b4595a9a3dc15a8371546ec0ffebb681c96e8f

Observation 83b791cf-4563-4bac-b69b-e823938961cd · outbound

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

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 34

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source=pdf_text observed=2026-08-07T05:43:42.884189Z digest=sha256:3f4a2db53d6137796ce18d0dd2f6a3dcf6f972a18365da9836a0964691753bf3

Observation fc628e12-ea92-4ca2-8b7b-a2916806fa5f · outbound

This paper cites InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling

Reference 35

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Observation 49db2cd3-1be6-43e4-9ca3-4059d3f4df2b · outbound

This paper cites Realworldqa.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation Realworldqa

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-07T05:43:45.404107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation f5299504-2ffd-4225-86e8-3561e2128509 · outbound

This paper cites Dynamic multimodal evaluation with flexible complexity by vision-language bootstrapping.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation Dynamic multimodal evaluation with flexible complexity by vision-language bootstrapping

Reference 37

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verified fuzzy
raw_fallback, observed 2026-08-07T05:43:45.090141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 170e434f-ad70-4ad2-8b5b-37a9c533797f · outbound

This paper cites A Survey on Multimodal Large Language Models.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation A Survey on Multimodal Large Language Models

Reference 38

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no resolver link, observed 2026-08-07T05:43:43.489140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e027e542-f702-40f6-9e38-0c9c8015b887 · outbound

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

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities

Reference 39

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no resolver link, observed 2026-08-07T05:43:43.659170Z

Source-reported events for the cited work

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Observation 5879e056-8321-4c4d-9c80-1ed495a4e8ba · outbound

This paper cites Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T05:43:43.761515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation bcc4f788-8ab4-443c-b4bf-217c5acd20de · outbound

This paper cites DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks

Reference 41

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unresolved
no resolver link, observed 2026-08-07T05:43:43.859281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:43:43.859281Z digest=sha256:c0ea98422fce2969fab5544a85fadd0fe03870dc6d466eccadd401bfa3e9ed9c

Observation 678d5dee-677b-490a-9f62-21926e94ddac · outbound

This paper cites reasoning MLLMs,.

Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation reasoning MLLMs,

Reference 42

Resolution
malformed identifier
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:43:44.008852Z digest=sha256:391b22daff1ed5190d5efa34da35d761dcca21474884cf35a65b38cbeac02b38

Pith citing papers

Observation 87a9aca1-b6d3-44d1-a386-294b3273e171 · inbound

DMC-CF: Dynamic Multimodal CounterFactual QA benchmark for Causal Reasoning cites this paper.

DMC-CF: Dynamic Multimodal CounterFactual QA benchmark for Causal Reasoning Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-06-29T08:43:15.763580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-29T08:36:21.863358Z digest=sha256:a400be6c2e75f40281191f5beaea2bc9f496775f91cde359096c6b1e984c9d2b