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Paper Citation Record · LEDGER

MLLMs are Deeply Affected by Modality Bias

As of 14 August 2026, this Paper Citation Record lists 89 of 89 outbound references and 17 inbound Pith citation observations for arXiv:2505.18657.

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

pith.paper-citation-record.v1
2505.18657 v1

Coverage vector

measured 89 of 89 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:30:22.310402Z

measured 106 of 106 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 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:35:43.369407Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T11:59:50.223756Z

Reference resolution

89 of 89 outbound references displayed

  • verified exact0
  • verified fuzzy45
  • unresolved44
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation abadf357-7af2-4e8b-9948-53262f65cd1f · outbound

This paper cites Qwen2.5-VL Technical Report.

MLLMs are Deeply Affected by Modality Bias Qwen2.5-VL Technical Report

Reference 1

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source=pdf_text observed=2026-08-07T14:30:21.429352Z digest=sha256:c62b0d5a276dea8482f98a23e05419519dc1e79a1dfd2a95d0bc8d91f474d515

Observation de9914c2-3abb-45f0-ac95-d09d3c005fa0 · outbound

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

MLLMs are Deeply Affected by Modality Bias Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 2

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source=pdf_text observed=2026-08-07T14:30:21.459812Z digest=sha256:6091f517755e1ee8a41c7dc77bf7cbe72abf192ce13bde7bb31c8d4aeffb83fd

Observation 1f977bd7-d0b0-4f54-af21-f70a07258c6f · outbound

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

MLLMs are Deeply Affected by Modality Bias InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

Reference 3

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source=pdf_text observed=2026-08-07T14:30:21.513272Z digest=sha256:13c87207881c27447165d61d2dd98c9a1e2ed17c1aaf97473865eb6849173e22

Observation 84836bc0-651d-46b3-8fbf-c737d4b9694f · outbound

This paper cites Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling.

MLLMs are Deeply Affected by Modality Bias Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

Reference 4

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source=pdf_text observed=2026-08-07T14:30:21.584728Z digest=sha256:d3e4426646349fb3f4064486737ddac68f762f4c109cd7365a012045eab75252

Observation 04bf98c0-6cca-40e7-8fd8-c244a6a200fe · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

MLLMs are Deeply Affected by Modality Bias Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 5

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source=pdf_text observed=2026-08-07T14:30:21.589921Z digest=sha256:f8c079cfdb19b9801edc484d96618bb0368820c4522e14d49488a154f9ba54a3

Observation b188da2d-cf46-4df9-b104-023a1c6c1498 · outbound

This paper cites GPT-4o System Card.

MLLMs are Deeply Affected by Modality Bias GPT-4o System Card

Reference 6

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source=pdf_text observed=2026-08-07T14:30:21.593162Z digest=sha256:c0db7cf469383e37021bcf2abe4b5b8fa86451c8e7bcb0a5db791be4ada4f039

Observation fae684da-628a-46ff-8b47-8eb63d68578f · outbound

This paper cites Tactile sensing—from humans to humanoids,.

MLLMs are Deeply Affected by Modality Bias Tactile sensing—from humans to humanoids,

Reference 7

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source=pdf_text observed=2026-08-07T14:30:21.607933Z digest=sha256:6075f8bf3546bd557027cd28a241a08b13f5ae9a46556ff6c1b55cab3072e593

Observation e8a1874e-b341-4cd8-bf36-191136222c82 · outbound

This paper cites Novel tactile sensor technology and smart tactile sensing systems: A review,.

MLLMs are Deeply Affected by Modality Bias Novel tactile sensor technology and smart tactile sensing systems: A review,

Reference 8

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source=pdf_text observed=2026-08-07T14:30:21.697018Z digest=sha256:eff153a3d30286bfac53da6fc21ca53dfa8c7e5d763ea36aad99aad6658eee1d

Observation 3a8fc65a-4764-44a9-81ce-3d7e7ed403c0 · outbound

This paper cites Recent progress in technologies for tactile sensors,.

MLLMs are Deeply Affected by Modality Bias Recent progress in technologies for tactile sensors,

Reference 9

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source=pdf_text observed=2026-08-07T14:30:21.755734Z digest=sha256:2f4921a674ac4c0e8bdef4dc836b9522b3811202bf9727fd4335a4d93bc86e49

Observation c6b63d81-4111-4b67-8bc6-659793d4b861 · outbound

This paper cites Event-based vision: A survey,.

MLLMs are Deeply Affected by Modality Bias Event-based vision: A survey,

Reference 10

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source=pdf_text observed=2026-08-07T14:30:21.819291Z digest=sha256:032f73d2bcc31bcc1e5b27c648279eb6bac08a271c81993164718b999246e456

Observation f1f0f181-d3aa-4f30-b2ed-01657d64df5f · outbound

This paper cites Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks.

MLLMs are Deeply Affected by Modality Bias Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks

Reference 11

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source=pdf_text observed=2026-08-07T14:30:21.878801Z digest=sha256:1db002b37a6f647174d3425a69a5869b4a6d4ada741e42d7791af32e4433d0b9

Observation 3d2aacf3-1a2b-4646-827a-f296279d0144 · outbound

This paper cites High speed and high dynamic range video with an event camera,.

MLLMs are Deeply Affected by Modality Bias High speed and high dynamic range video with an event camera,

Reference 12

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source=pdf_text observed=2026-08-07T14:30:21.976724Z digest=sha256:8aa9dfd60995b7cb40069a02391daec92e4d490ddd03bed9b2d1ab9ccd129668

Observation 8803649e-f34f-46a7-9ee5-7b12445399f1 · outbound

This paper cites 360sfuda++: Towards source-free uda for panoramic segmentation by learning reliable category prototypes,.

MLLMs are Deeply Affected by Modality Bias 360sfuda++: Towards source-free uda for panoramic segmentation by learning reliable category prototypes,

Reference 13

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source=pdf_text observed=2026-08-07T14:30:22.058249Z digest=sha256:9aa76c0c6e471e8311e6e80a82ca11ef0c2c5d0844c083c1530c0eefb5258aed

Observation af7dc34a-6b02-4acd-8edf-02aad04864be · outbound

This paper cites Semantics distortion and style matter: Towards source-free uda for panoramic segmentation,.

MLLMs are Deeply Affected by Modality Bias Semantics distortion and style matter: Towards source-free uda for panoramic segmentation,

Reference 14

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source=pdf_text observed=2026-08-07T14:30:22.075625Z digest=sha256:baa689feffb9fbb9dec2ae291c0643084042ab05bd3695fb2cb1b4550dadaaa0

Observation 368ebb22-04b4-414d-8dbf-4589bb639cce · outbound

This paper cites Omnisam: Omnidirectional segment anything model for uda in panoramic semantic segmentation,.

MLLMs are Deeply Affected by Modality Bias Omnisam: Omnidirectional segment anything model for uda in panoramic semantic segmentation,

Reference 15

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source=pdf_text observed=2026-08-07T14:30:22.078642Z digest=sha256:e871ac0a9eb4908d019422995ea84440ab1e5680d5fda30d986862e6bfc2cd6a

Observation ad6c8bab-f38a-40d1-b620-5993180b1833 · outbound

This paper cites Mmbench: Is your multi-modal model an all-around player?,.

MLLMs are Deeply Affected by Modality Bias Mmbench: Is your multi-modal model an all-around player?,

Reference 16

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source=pdf_text observed=2026-08-07T14:30:22.081758Z digest=sha256:151279fdff6eac0cc7dfba6e4a2ee2dd44b33acfc19ba16d517c92bcae97e836

Observation 10368d55-3909-45c7-a04e-6ec4ff1c7b8f · outbound

This paper cites Mmbench-video: A long- form multi-shot benchmark for holistic video understanding,.

MLLMs are Deeply Affected by Modality Bias Mmbench-video: A long- form multi-shot benchmark for holistic video understanding,

Reference 17

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source=pdf_text observed=2026-08-07T14:30:22.084531Z digest=sha256:5c543a08a53bfb5643020ed46427d4ce1c5749f6420103c205168a02ca86b45c

Observation 6d86b2eb-c66e-42bc-a795-70e1a17855fe · outbound

This paper cites Docvqa: A dataset for vqa on document images,.

MLLMs are Deeply Affected by Modality Bias Docvqa: A dataset for vqa on document images,

Reference 18

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raw_fallback, observed 2026-08-07T14:30:23.231183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.087528Z digest=sha256:483556c57abc2af25ba25300971df63cffdb99ed333126672cbdd10eb4d552c9

Observation 0f72bb53-9575-40fc-8521-b66c9bf026c8 · outbound

This paper cites A Survey on Multimodal Benchmarks: In the Era of Large AI Models.

MLLMs are Deeply Affected by Modality Bias A Survey on Multimodal Benchmarks: In the Era of Large AI Models

Reference 19

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source=pdf_text observed=2026-08-07T14:30:22.090211Z digest=sha256:2f9108a8fc15094555292922e58c0cd250e0a620a90af6e1cd4de0d595bfe08d

Observation 7e2f3ed1-726d-445c-be3e-c0fba0554758 · outbound

This paper cites A Survey on Benchmarks of Multimodal Large Language Models.

MLLMs are Deeply Affected by Modality Bias A Survey on Benchmarks of Multimodal Large Language Models

Reference 20

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source=pdf_text observed=2026-08-07T14:30:22.093509Z digest=sha256:7cb4234e48e3eed09a9608199a1f699cff2360200d5c835ea76d5ef867f8e2ff

Observation 22a4348b-32aa-45d7-840f-981ad9e234c0 · outbound

This paper cites Visual Prompting in Multimodal Large Language Models: A Survey.

MLLMs are Deeply Affected by Modality Bias Visual Prompting in Multimodal Large Language Models: A Survey

Reference 21

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source=pdf_text observed=2026-08-07T14:30:22.096111Z digest=sha256:9e53f19b0319af3138d5586d394f434f87dd28efcc378a7fc6f755bae76173a3

Observation 240c9f7b-1905-4a45-85fa-6f8e67264a99 · outbound

This paper cites Survey of Adversarial Robustness in Multimodal Large Language Models.

MLLMs are Deeply Affected by Modality Bias Survey of Adversarial Robustness in Multimodal Large Language Models

Reference 22

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source=pdf_text observed=2026-08-07T14:30:22.098809Z digest=sha256:c05e8e274d35c597588ae5d5deb76abaaaf280f5bbe28510bfcddcc2ac770f7b

Observation 5dd9b372-1165-4af8-9407-018a5b441505 · outbound

This paper cites When Continue Learning Meets Multimodal Large Language Model: A Survey.

MLLMs are Deeply Affected by Modality Bias When Continue Learning Meets Multimodal Large Language Model: A Survey

Reference 23

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source=pdf_text observed=2026-08-07T14:30:22.101577Z digest=sha256:b579c54c9f49410012749abce76511f35fcc79cfd3fc1a11c9713fc75d357406

Observation 48a07fd0-6595-4230-a234-f4278ffaa336 · outbound

This paper cites Debiasing Multimodal Large Language Models via Penalization of Language Priors.

MLLMs are Deeply Affected by Modality Bias Debiasing Multimodal Large Language Models via Penalization of Language Priors

Reference 24

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source=pdf_text observed=2026-08-07T14:30:22.104648Z digest=sha256:905fe9555aee76276d2753d2118a14aab74daa51f6882df5cf33523ed94eea78

Observation 930a19f3-df9e-4833-8bc6-83e4ba5c06bf · outbound

This paper cites Assessing modality bias in video question answering benchmarks with multimodal large language models,.

MLLMs are Deeply Affected by Modality Bias Assessing modality bias in video question answering benchmarks with multimodal large language models,

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.107563Z digest=sha256:dcc835a0d8dd2c95a474ec7204fa86743a041973a3baae504b7dadcaf3883b69

Observation 75e16f0a-368a-4d2e-ade3-b2407f7b5c43 · outbound

This paper cites Eyes wide shut? exploring the visual shortcomings of multimodal llms,.

MLLMs are Deeply Affected by Modality Bias Eyes wide shut? exploring the visual shortcomings of multimodal llms,

Reference 26

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source=pdf_text observed=2026-08-07T14:30:22.110240Z digest=sha256:79283484034c28045cca2226e5d939b7c0983aa3b523a571c4380fb3995c78d6

Observation bd31e67a-1889-4193-a03b-1846439542d8 · outbound

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

MLLMs are Deeply Affected by Modality Bias Are We on the Right Way for Evaluating Large Vision-Language Models?

Reference 27

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source=pdf_text observed=2026-08-07T14:30:22.113116Z digest=sha256:7a047267520097801f6b1c548476561ffc97b0b3652f1d68ab0ee47f643fba63

Observation fdd05ac9-8660-40a5-8796-0e0a62eae093 · outbound

This paper cites Quantifying and Mitigating Unimodal Biases in Multimodal Large Language Models: A Causal Perspective.

MLLMs are Deeply Affected by Modality Bias Quantifying and Mitigating Unimodal Biases in Multimodal Large Language Models: A Causal Perspective

Reference 28

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source=pdf_text observed=2026-08-07T14:30:22.115900Z digest=sha256:47395fdd2954c976a6df763f8b9bda8caaaa954dd5a8b2df8451c16ef4914ac1

Observation 32ab4fa7-1ee6-4b7f-b712-ece5487f0cad · outbound

This paper cites MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark.

MLLMs are Deeply Affected by Modality Bias MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark

Reference 29

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source=pdf_text observed=2026-08-07T14:30:22.118528Z digest=sha256:8a7a40b8c146337e71939e23403701dffbf30c60a437e0d322e0d64300b9d7fc

Observation e9eeb4cc-c199-42b3-a5d3-e11791352b31 · outbound

This paper cites Multimodal learning with transformers: A survey,.

MLLMs are Deeply Affected by Modality Bias Multimodal learning with transformers: A survey,

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.121281Z digest=sha256:936ffa5af090be0402ae68b45c770d3dd996a74ff42d10108291e269f2a56675

Observation 785f5e72-992b-497b-9e28-1af5ce9120e7 · outbound

This paper cites A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets,.

MLLMs are Deeply Affected by Modality Bias A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets,

Reference 31

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raw_fallback, observed 2026-08-07T14:30:23.195082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 1efb1648-6f3f-4ad4-9f34-c66904626dfd · outbound

This paper cites A review on methods and applications in multimodal deep learning,.

MLLMs are Deeply Affected by Modality Bias A review on methods and applications in multimodal deep learning,

Reference 32

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verified fuzzy
raw_fallback, observed 2026-08-07T14:30:23.183537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.127154Z digest=sha256:5ebacdef6e1bd50932c8e715885afabbbf3d2aa81425f4b40bcadf37d17589be

Observation d748db31-5f83-40eb-996d-6fc45ace69dd · outbound

This paper cites Learning modality-agnostic representation for semantic segmentation from any modalities,.

MLLMs are Deeply Affected by Modality Bias Learning modality-agnostic representation for semantic segmentation from any modalities,

Reference 33

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raw_fallback, observed 2026-08-07T14:30:23.173138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.129748Z digest=sha256:d299135e277dd74cfc2ec78d1959e759306544fe7ab998f8dbcd395dadbb5e21

Observation 920b8fe1-f8ca-4dd9-bc5d-82c150b38cb0 · outbound

This paper cites MemorySAM: Memorize Modalities and Semantics with Segment Anything Model 2 for Multi-modal Semantic Segmentation.

MLLMs are Deeply Affected by Modality Bias MemorySAM: Memorize Modalities and Semantics with Segment Anything Model 2 for Multi-modal Semantic Segmentation

Reference 34

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source=pdf_text observed=2026-08-07T14:30:22.133352Z digest=sha256:ad5f9de04073bb6a6a85e437a02e3ccd3cd5e101a9b5cd93f230773c39982d91

Observation d3cd5a6f-8cde-4b9e-9714-82afa793bde4 · outbound

This paper cites Cafuser: Condition-aware multimodal fusion for robust semantic perception of driving scenes,.

MLLMs are Deeply Affected by Modality Bias Cafuser: Condition-aware multimodal fusion for robust semantic perception of driving scenes,

Reference 35

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raw_fallback, observed 2026-08-07T14:30:23.162814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.136221Z digest=sha256:91e60c0f760c810f9ed8c59d85f971e5a3d0bef64c66169d2d5a5461e6d296b4

Observation e6e58c7d-e301-4082-9d52-5b2b22c37770 · outbound

This paper cites Multimodality represen- tation learning: A survey on evolution, pretraining and its applications,.

MLLMs are Deeply Affected by Modality Bias Multimodality represen- tation learning: A survey on evolution, pretraining and its applications,

Reference 36

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raw_fallback, observed 2026-08-07T14:30:23.150498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.139263Z digest=sha256:6126dd9f2b6be0d221f8caeb3281e285e6fc924101cf573e21ec949406bbd16b

Observation c4427475-67fb-4abf-a814-295a0c98e5d5 · outbound

This paper cites Enhancing multimodal cooperation via sample-level modality valuation,.

MLLMs are Deeply Affected by Modality Bias Enhancing multimodal cooperation via sample-level modality valuation,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:23.138981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.142122Z digest=sha256:cfb6064bcbf1a1dee68157f9ae2468ba28441bca6b9d3ed04e3bf4ddb7b0823e

Observation b25be5e1-2a98-4a37-8d5e-64e3a28b5c96 · outbound

This paper cites Centering the value of every modality: Towards efficient and resilient modality-agnostic semantic segmentation,.

MLLMs are Deeply Affected by Modality Bias Centering the value of every modality: Towards efficient and resilient modality-agnostic semantic segmentation,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:23.127895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.144730Z digest=sha256:ed1af2b0b270a96a40fc25f3967283cd38f8f2cff66afdfcddbf5cb82153a08e

Observation 9eaf1cb3-d80c-4765-8697-b2d87d7cbae4 · outbound

This paper cites Reducing Unimodal Bias in Multi-Modal Semantic Segmentation with Multi-Scale Functional Entropy Regularization.

MLLMs are Deeply Affected by Modality Bias Reducing Unimodal Bias in Multi-Modal Semantic Segmentation with Multi-Scale Functional Entropy Regularization

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-07T14:30:22.147211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:30:22.147211Z digest=sha256:369f6f1ec93023ef8c2d18f2e35353b037042c640842eb563e2dfbcfa73218ad

Observation 32bd4f51-859d-493b-a30d-7056d89fb687 · outbound

This paper cites Learning Robust Anymodal Segmentor with Unimodal and Cross-modal Distillation.

MLLMs are Deeply Affected by Modality Bias Learning Robust Anymodal Segmentor with Unimodal and Cross-modal Distillation

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T14:30:22.150289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:30:22.150289Z digest=sha256:b9ccd078cb762cbc550a0130099c38f63cadcf6218e20b9a7c473854652fb3cf

Observation 4240287c-4dd6-43b6-9b7e-95bc67605209 · outbound

This paper cites Balanced multimodal learning via on-the-fly gradient modulation,.

MLLMs are Deeply Affected by Modality Bias Balanced multimodal learning via on-the-fly gradient modulation,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:23.114742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.153165Z digest=sha256:a31a64e614412a07c505d29386cea814c73e49c36f526df17d6ab4ac4bc8bd6b

Observation 7f854eed-ce46-418b-a9f9-82957d64b9ec · outbound

This paper cites Clip the bias: How useful is balancing data in multimodal learning?,.

MLLMs are Deeply Affected by Modality Bias Clip the bias: How useful is balancing data in multimodal learning?,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:23.103890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.155716Z digest=sha256:26d175b7d808a6d22b43bfd8fee926c587e2008b9e2c70f110dc65d4114afb99

Observation d983c414-9160-48e0-bcd9-99226fe98920 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion,.

MLLMs are Deeply Affected by Modality Bias Learning transferable visual models from natural language supervi- sion,

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-07T14:30:22.158369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:30:22.158369Z digest=sha256:e626eeee2e459c4ad24bc516b924cd453f4676e52dfcd9bdbd2f9e71ba372bb4

Observation 5281160e-64e4-46c1-8726-11206bfeb0c3 · outbound

This paper cites Clippo: Image-and-language understanding from pixels only,.

MLLMs are Deeply Affected by Modality Bias Clippo: Image-and-language understanding from pixels only,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:23.086597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.161631Z digest=sha256:7f155031c755ead7b837478d1265b448c4da07671368daacc292f27e68070637

Observation 3863a0eb-aab7-41a9-b071-513a6915dc96 · outbound

This paper cites Clip-kd: An empirical study of clip model distillation,.

MLLMs are Deeply Affected by Modality Bias Clip-kd: An empirical study of clip model distillation,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:23.075659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.164266Z digest=sha256:90a2cf0e0689a976234b6351e29e791eaf6722ab4ed373c1d763fb8397fe674d

Observation 17f011f8-4d95-4edc-9b26-86e7847d4924 · outbound

This paper cites Tinyclip: Clip distillation via affinity mimicking and weight inheritance,.

MLLMs are Deeply Affected by Modality Bias Tinyclip: Clip distillation via affinity mimicking and weight inheritance,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:23.065651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.167042Z digest=sha256:74d85d4574c606ee9d80fe3b03aae96cbfacd5615fa52616e8b549fbb2a20dcb

Observation 0565e097-cf6d-45be-9d94-5b72c4772f89 · outbound

This paper cites An inverse scaling law for clip training,.

MLLMs are Deeply Affected by Modality Bias An inverse scaling law for clip training,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:23.055966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.169708Z digest=sha256:b8f15332ceaef9990239adaa9263fa1bea83e512215f4ebb71beaf507b9b5ea0

Observation eed1ba1d-0733-439f-838c-a82bbfebd2ce · outbound

This paper cites BalanceBenchmark: A Survey for Multimodal Imbalance Learning.

MLLMs are Deeply Affected by Modality Bias BalanceBenchmark: A Survey for Multimodal Imbalance Learning

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-07T14:30:22.172299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:30:22.172299Z digest=sha256:1cc7b7c5cc134a05a89a7263b307ad4a85a860ad01e64aadbe81fbc0aa55d73c

Observation 54f1252b-063a-4c80-abec-02d1ee4a54bc · outbound

This paper cites Facilitating multimodal classification via dynamically learning modality gap,.

MLLMs are Deeply Affected by Modality Bias Facilitating multimodal classification via dynamically learning modality gap,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:23.045629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.175121Z digest=sha256:1f749ae492ee0adb180c34d2388f5434d11f58b11f8e9399de6addf615ae420a

Observation 226d7d21-0215-4242-98e2-d50a08ebf7c9 · outbound

This paper cites Benchmarking Multi-modal Semantic Segmentation under Sensor Failures: Missing and Noisy Modality Robustness.

MLLMs are Deeply Affected by Modality Bias Benchmarking Multi-modal Semantic Segmentation under Sensor Failures: Missing and Noisy Modality Robustness

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-07T14:30:22.178436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:30:22.178436Z digest=sha256:678b9248dcf1a139e91a2eb09199162d29e89d398a1543a1b1841f837d6e507b

Observation a9af95a0-7df2-4c73-8114-69135c20ee99 · outbound

This paper cites The Curse of Multi-Modalities: Evaluating Hallucinations of Large Multimodal Models across Language, Visual, and Audio.

MLLMs are Deeply Affected by Modality Bias The Curse of Multi-Modalities: Evaluating Hallucinations of Large Multimodal Models across Language, Visual, and Audio

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-07T14:30:22.181322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:30:22.181322Z digest=sha256:f66ae880d1b0277d6da54476b5ebb62a3440b022e20a59ec152bc00e6f762708

Observation eba14ae4-04a6-4dd1-8bd0-27b3e1fc5453 · outbound

This paper cites On modality bias recognition and reduction,.

MLLMs are Deeply Affected by Modality Bias On modality bias recognition and reduction,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:23.035177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.184498Z digest=sha256:116f8c6df2902355a3c18ac1cabbf0d44ebf2697da83aa11a36969f97ad6c570

Observation 6c3f4aba-7103-404e-ad0e-401272a7a362 · outbound

This paper cites Cross modality bias in visual question answering: A causal view with possible worlds vqa,.

MLLMs are Deeply Affected by Modality Bias Cross modality bias in visual question answering: A causal view with possible worlds vqa,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:23.024112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.187223Z digest=sha256:65e94d9ced6db2099ba1b1579ca324942bef1b90885e568ae8dc0cf0065cf001

Observation b1fb5883-1613-44da-b211-b78203584a3a · outbound

This paper cites Counterfactual vqa: A cause-effect look at language bias,.

MLLMs are Deeply Affected by Modality Bias Counterfactual vqa: A cause-effect look at language bias,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:23.013324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.189752Z digest=sha256:8e67872f3668b3b29fe72b8c7ec51938ee15a3537eb46a4d08087f1b586151f3

Observation 5fd3e92d-26e7-48b8-8e13-a7016f6e868c · outbound

This paper cites Removing bias in multi-modal classifiers: Regularization by maximizing functional entropies,.

MLLMs are Deeply Affected by Modality Bias Removing bias in multi-modal classifiers: Regularization by maximizing functional entropies,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:23.003308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.192626Z digest=sha256:5f64c81c9b3d2b016f798923535848be0a7bd6f13193cbe0c458d3c3a1d967f0

Observation 6c265d65-7454-45ba-939e-f633f23abb2d · outbound

This paper cites Overcoming language priors in visual question answering with adversarial regularization,.

MLLMs are Deeply Affected by Modality Bias Overcoming language priors in visual question answering with adversarial regularization,

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:22.992584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.195085Z digest=sha256:08296ee2dd317d004dc1c01338ffa0e58429b601d8eb62fd2f54cb1b7d6609f4

Observation 95f1802f-089e-4d52-a589-a641b2e3f5bc · outbound

This paper cites VLind-Bench: Measuring Language Priors in Large Vision-Language Models.

MLLMs are Deeply Affected by Modality Bias VLind-Bench: Measuring Language Priors in Large Vision-Language Models

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-07T14:30:22.197925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:30:22.197925Z digest=sha256:4676603a541151e6be166ca4e03c5f8f259590a1eb66222db0e2abef34e333db

Observation d09c89d4-3718-46f9-a9c6-a21ae99ffe63 · outbound

This paper cites Insight Over Sight: Exploring the Vision-Knowledge Conflicts in Multimodal LLMs.

MLLMs are Deeply Affected by Modality Bias Insight Over Sight: Exploring the Vision-Knowledge Conflicts in Multimodal LLMs

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-07T14:30:22.201170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:30:22.201170Z digest=sha256:81ea84828d0d1993da6ca3259950493bb6f6786ef173c9fc04de8cd4a8d803c9

Observation ed7e82ee-c4df-406b-bb6d-e744c18b684d · outbound

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

MLLMs are Deeply Affected by Modality Bias Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi,

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:22.982040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.204220Z digest=sha256:7f21403d7b4f49ea11c990050122dadb4246f7e28e2614349eee1c12093a01c7

Observation bf7b0c12-2d21-4c55-8a2c-fd6b55594d57 · outbound

This paper cites Mmicl: Empowering vision-language model with multi-modal in-context learning,.

MLLMs are Deeply Affected by Modality Bias Mmicl: Empowering vision-language model with multi-modal in-context learning,

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:22.970869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.206978Z digest=sha256:6a0d9b3a9874602b7ac5360d2ad430bf6e8734d533c9b38cfef0f8e2f4f62d30

Observation b2929ed7-5c6a-43f6-9111-44acd1117ea8 · outbound

This paper cites Strengthening multimodal large language model with bootstrapped preference optimization,.

MLLMs are Deeply Affected by Modality Bias Strengthening multimodal large language model with bootstrapped preference optimization,

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:22.958519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.209706Z digest=sha256:358c3c6d2cc65e11ae0ed5f471579edc24306e4096478287798d6c7f16ef7352

Observation 145abb58-7810-4d10-9917-8b14b04e042c · outbound

This paper cites Paying more attention to image: A training-free method for alleviating hallucination in lvlms,.

MLLMs are Deeply Affected by Modality Bias Paying more attention to image: A training-free method for alleviating hallucination in lvlms,

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:22.946545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.212686Z digest=sha256:3e64581fcdf9b53baddc0af90146eebefb475287b13930b264271883e4faa709

Observation 2793178a-cee2-4fa3-bc9a-0a090975cfb8 · outbound

This paper cites Looking Beyond Text: Reducing Language bias in Large Vision-Language Models via Multimodal Dual-Attention and Soft-Image Guidance.

MLLMs are Deeply Affected by Modality Bias Looking Beyond Text: Reducing Language bias in Large Vision-Language Models via Multimodal Dual-Attention and Soft-Image Guidance

Reference 63

Resolution
unresolved
no resolver link, observed 2026-08-07T14:30:22.216120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:30:22.216120Z digest=sha256:a4a5d66ab9a091afb91d0175bd1f580990350890cca82f097b28b1ac5d27c627

Observation aa27677a-e313-40c2-a46b-f9467ef70160 · outbound

This paper cites Debiasing Multimodal Large Language Models via Noise-Aware Preference Optimization.

MLLMs are Deeply Affected by Modality Bias Debiasing Multimodal Large Language Models via Noise-Aware Preference Optimization

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-07T14:30:22.219763Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:30:22.219763Z digest=sha256:0078bd12f6b2180485c6d9b221fa110eea8c282576e2518332d413852cc11596

Observation 4b25bda4-5890-417e-abb3-c5147a9a2c1a · outbound

This paper cites The Devil Is in the Details: Tackling Unimodal Spurious Correlations for Generalizable Multimodal Reward Models.

MLLMs are Deeply Affected by Modality Bias The Devil Is in the Details: Tackling Unimodal Spurious Correlations for Generalizable Multimodal Reward Models

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-07T14:30:22.222904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:30:22.222904Z digest=sha256:3f6db73d243c8ea8c0011322d3c2357197addcbdf40fab5722a7e6a18904ca4b

Observation a66f15ad-6718-4f94-91b5-1bae655a8ab8 · outbound

This paper cites DeepSeek-V3 Technical Report.

MLLMs are Deeply Affected by Modality Bias DeepSeek-V3 Technical Report

Reference 66

Resolution
unresolved
no resolver link, observed 2026-08-07T14:30:22.226582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:30:22.226582Z digest=sha256:a742f916be3e76ff55cc58b51c841eec90eea032adbf22049cd74cf66c13137d

Observation 24dbfffb-92e8-4f8b-96f4-5184825983ce · outbound

This paper cites DeepSeek LLM: Scaling Open-Source Language Models with Longtermism.

MLLMs are Deeply Affected by Modality Bias DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-07T14:30:22.229737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:30:22.229737Z digest=sha256:6da808ef8a7ac0f5185b8771e75e6d8c06b76e6dad8fe665caa59e86542fee4d

Observation dcbe360a-bf55-4d60-bf25-1a2c58305fd8 · outbound

This paper cites A Comprehensive Overview of Large Language Models.

MLLMs are Deeply Affected by Modality Bias A Comprehensive Overview of Large Language Models

Reference 68

Resolution
unresolved
no resolver link, observed 2026-08-07T14:30:22.233235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:30:22.233235Z digest=sha256:ced9ce4604a4dcc6485fd7236d4e3b57ae8833471dfe221b0483f47a7d8c6d67

Observation f0b93cc6-1b3c-47e6-ae68-aaa8e7e9b50d · outbound

This paper cites Masked jigsaw puzzle: A versatile position embedding for vision transformers,.

MLLMs are Deeply Affected by Modality Bias Masked jigsaw puzzle: A versatile position embedding for vision transformers,

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:22.936660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.237361Z digest=sha256:917aa63e16f126efd466292008b7fa34d3c16eb076b93bc288683662e27a288a

Observation 05080f11-c309-4a05-9822-8a3690c70c96 · outbound

This paper cites A survey on vision transformer,.

MLLMs are Deeply Affected by Modality Bias A survey on vision transformer,

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:22.926169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.240687Z digest=sha256:705ed11dc77791d18c8ff0f3987b0a2536f11e8b1fc583f77b6838cbfefab911

Observation ac964b04-73d0-4318-bc75-bf67cf661dbf · outbound

This paper cites Bringing masked autoencoders explicit contrastive properties for point cloud self-supervised learning,.

MLLMs are Deeply Affected by Modality Bias Bringing masked autoencoders explicit contrastive properties for point cloud self-supervised learning,

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:22.916354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.243960Z digest=sha256:1ea48fb772d5e585010dd644f7d632c6601319ec707aa75293b5b7dba648d2e6

Observation c05644ac-d74b-40e8-849c-e84eeae62b23 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows,.

MLLMs are Deeply Affected by Modality Bias Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 72

Resolution
unresolved
no resolver link, observed 2026-08-07T14:30:22.247725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:30:22.247725Z digest=sha256:f63d796bce236a6e702d2e8980cca58d2b0684ee0a9ce6d5389c487fa9a2bd79

Observation b6b99f08-5030-4543-9808-8fbf73ca4cab · outbound

This paper cites Sharing key semantics in transformer makes efficient image restoration,.

MLLMs are Deeply Affected by Modality Bias Sharing key semantics in transformer makes efficient image restoration,

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:22.900881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.250976Z digest=sha256:89a0555956f9565454d717ba4f3dbf3cc045793c482ab58c468a9d66e511d2df

Observation f595a5dd-322b-441f-8fe8-2f54e1c5e178 · outbound

This paper cites Learning disentangled identifiers for action-customized text-to-image generation,.

MLLMs are Deeply Affected by Modality Bias Learning disentangled identifiers for action-customized text-to-image generation,

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:22.889983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.255003Z digest=sha256:119a75aa384ba48749742e7b94f2f722aa2e822520714da20ebb8258bab541ff

Observation 94e2c6fc-7000-47c3-ac38-11650580962c · outbound

This paper cites Languagebind: Extending video-language pretraining to n-modality by language-based semantic alignment,.

MLLMs are Deeply Affected by Modality Bias Languagebind: Extending video-language pretraining to n-modality by language-based semantic alignment,

Reference 75

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:22.877388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.259050Z digest=sha256:9fb9dcc04ce1df7e08bf4aa622ee2b2d8c1cca823a1d73e9f499a40e4f584dad

Observation b179d7a7-2263-4f13-963a-9ca3b55cd60e · outbound

This paper cites Unibind: Llm-augmented unified and balanced representation space to bind them all,.

MLLMs are Deeply Affected by Modality Bias Unibind: Llm-augmented unified and balanced representation space to bind them all,

Reference 76

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:22.865706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.262913Z digest=sha256:5414cc45aa3a4dcf7d1950d38b0b40d2ef269b90f3074e568d8a4cc250161ce4

Observation 8f91d17d-86f8-4764-b193-44e7b3378a20 · outbound

This paper cites Optimizing intersection-over-union in deep neural networks for image segmentation,.

MLLMs are Deeply Affected by Modality Bias Optimizing intersection-over-union in deep neural networks for image segmentation,

Reference 77

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:22.853693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.266455Z digest=sha256:5d3d58e87e2a925236896ad77306bc291667f87a818805883964c681802b6eb2

Observation 73692348-413f-4060-a6f0-372b6fd8976e · outbound

This paper cites Generalized inter- section over union: A metric and a loss for bounding box regression,.

MLLMs are Deeply Affected by Modality Bias Generalized inter- section over union: A metric and a loss for bounding box regression,

Reference 78

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:22.839603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.270817Z digest=sha256:200c4d114ffa314085461db03cb647a3f98800679f01b96532c6a6358eae4f0f

Observation e0ca48bf-d094-477e-96f4-6d9151494b36 · outbound

This paper cites Subjective and objective quality assessment for image restoration: A critical survey,.

MLLMs are Deeply Affected by Modality Bias Subjective and objective quality assessment for image restoration: A critical survey,

Reference 79

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:22.826616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.274373Z digest=sha256:31f05d924f3582c453c7d6200fcaffeb6d1ee4e0ba1b8f52ee95b0eb8eb060eb

Observation 5d79f5b9-2bf3-49e1-8b1f-412a534e7363 · outbound

This paper cites Lvlm-ehub: A comprehensive evaluation benchmark for large vision-language models,.

MLLMs are Deeply Affected by Modality Bias Lvlm-ehub: A comprehensive evaluation benchmark for large vision-language models,

Reference 80

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:22.815042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.278422Z digest=sha256:c9046fe8b923e3880eeb467c2b7bc1f6262b88ec5b9b084e325cffbe90ea6935

Observation 99d06a1f-5917-49aa-a3b3-de98a6edb5aa · outbound

This paper cites Valor: Vision-audio-language omni-perception pretraining model and dataset,.

MLLMs are Deeply Affected by Modality Bias Valor: Vision-audio-language omni-perception pretraining model and dataset,

Reference 81

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:22.801518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.281980Z digest=sha256:c6707f06b1e60597c286e2c8dc8c670df2bfcd0db1f2df4d2f70b51ab4c7e445

Observation e1842fcb-53cd-434f-a681-75f6eb0718f1 · outbound

This paper cites Multimodal visual-tactile representation learning through self-supervised contrastive pre-training,.

MLLMs are Deeply Affected by Modality Bias Multimodal visual-tactile representation learning through self-supervised contrastive pre-training,

Reference 82

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:22.789295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.285837Z digest=sha256:76e12884984299c8b5347ab49ee5c7ccd1fc2afa22086997b5938ac3afcca573

Observation cac625a7-724d-458f-9e85-53d7e383c7e6 · outbound

This paper cites OmniBind: Teach to Build Unequal-Scale Modality Interaction for Omni-Bind of All.

MLLMs are Deeply Affected by Modality Bias OmniBind: Teach to Build Unequal-Scale Modality Interaction for Omni-Bind of All

Reference 83

Resolution
unresolved
no resolver link, observed 2026-08-07T14:30:22.289194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:30:22.289194Z digest=sha256:e61a3482d7f6a05cb127ce16e7cf55c507618b1007ff0ccbc0dd538e587ab4ba

Observation 47d56575-b2a1-4521-855c-d042382911fd · outbound

This paper cites Soft robotic hand with tactile palm-finger coordination,.

MLLMs are Deeply Affected by Modality Bias Soft robotic hand with tactile palm-finger coordination,

Reference 84

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:22.775871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.292730Z digest=sha256:f44f3b4dc36fab633dfc86e34cc11f56d27449ab20b55ac079ccd85489fddb47

Observation 03946064-751d-4780-bb07-0fedf7bfaaa5 · outbound

This paper cites Categorizing robots by performance fitness into the tree of robots,.

MLLMs are Deeply Affected by Modality Bias Categorizing robots by performance fitness into the tree of robots,

Reference 85

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:22.762667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.296123Z digest=sha256:ffa04e9ace58b541b13e29f96544ce075e3f977b5ff5642b765d643da2fb7e86

Observation b8d77fdc-ab92-4c86-90f1-9c9553170a94 · outbound

This paper cites Vision-based tactile sensor design using physically based rendering,.

MLLMs are Deeply Affected by Modality Bias Vision-based tactile sensor design using physically based rendering,

Reference 86

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:22.751464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.299865Z digest=sha256:2403022cca39d0ee55827e3c5f7da8d5f2fad8476e23ccb3445f839235a28c97

Observation 6f6640db-e917-4cd2-8627-48c3af45b849 · outbound

This paper cites Bi-vla: Vision-language-action model-based system for bimanual robotic dexterous manipulations,.

MLLMs are Deeply Affected by Modality Bias Bi-vla: Vision-language-action model-based system for bimanual robotic dexterous manipulations,

Reference 87

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:22.739766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.303293Z digest=sha256:82a0cbc022d5317d9d23a6027397ff05115149a0154eb3a836d4fd28a957041c

Observation 6cc13352-ba83-4d59-9730-629bee828a3b · outbound

This paper cites A practical tutorial on explainable ai techniques,.

MLLMs are Deeply Affected by Modality Bias A practical tutorial on explainable ai techniques,

Reference 88

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:22.728345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.307032Z digest=sha256:fbdd9fff9f0026598eb1d250636a526bda51c6803f0bc8c061526ad28272e1c2

Observation 27247ff8-dfd5-4b37-9541-3ca6845d5170 · outbound

This paper cites Explainable ai (xai): Core ideas, techniques, and solutions,.

MLLMs are Deeply Affected by Modality Bias Explainable ai (xai): Core ideas, techniques, and solutions,

Reference 89

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:30:22.716378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T14:30:22.310402Z digest=sha256:aa9681430f42a86987cf5cd568e9ef0bc9101743d0b95d9d3cf31d93203d2ca9

Pith citing papers

Observation 409490f8-ec1c-4261-8af4-a4482362a558 · 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 MLLMs are Deeply Affected by Modality Bias

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-06T01:00:28.861351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T01:00:28.861351Z digest=sha256:8e91980230224fe9bffbb702e3cb54e34f958389787258be696c205dff213417

Observation 44f4ebe2-070c-4bb0-85d9-e8365943b253 · inbound

Omnidirectional Spatial Modeling from Correlated Panoramas cites this paper.

Omnidirectional Spatial Modeling from Correlated Panoramas MLLMs are Deeply Affected by Modality Bias

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-05T11:56:37.791379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T11:56:37.791379Z digest=sha256:e816cb4356843fe1fbaaafa7eee67565849385341e7067fec0a1ef6b2c3a26e8

Observation 163ddd2a-a86a-413e-84b0-168791481c7c · inbound

When Vision Overrides Language: Evaluating and Mitigating Counterfactual Failures in VLAs cites this paper.

When Vision Overrides Language: Evaluating and Mitigating Counterfactual Failures in VLAs MLLMs are Deeply Affected by Modality Bias

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-02T22:14:31.631473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T22:14:31.631473Z digest=sha256:3fa63162c6e983e59535ac52fd4f905233fdbd3e381269db02d80447e7e36cb3

Observation fbd1d54a-dd4f-4ddb-b55d-2cb5b89c675f · inbound

Token-Efficient Multimodal Reasoning via Image Prompt Packaging cites this paper.

Token-Efficient Multimodal Reasoning via Image Prompt Packaging MLLMs are Deeply Affected by Modality Bias

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-13T21:53:19.601782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-13T21:52:25.713970Z digest=sha256:b0321d7fc562b222a2ae5eaf6892dff95242fccafea8a3022afc50ce9a011468

Observation f8d109ff-404d-49b4-b09b-18a6839cf17d · inbound

A Systematic Study of Cross-Modal Typographic Attacks on Audio-Visual Reasoning cites this paper.

A Systematic Study of Cross-Modal Typographic Attacks on Audio-Visual Reasoning MLLMs are Deeply Affected by Modality Bias

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-13T17:38:02.902544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-13T17:34:10.089555Z digest=sha256:95dab5e8fad94b6fe435746a37131cf0a786777b431d5c0c2bdfda11c5b06996

Observation b07b222e-4d13-46a3-a610-d57a13e7a1b0 · inbound

Beyond Text-Dominance: Understanding Modality Preference of Omni-modal Large Language Models cites this paper.

Beyond Text-Dominance: Understanding Modality Preference of Omni-modal Large Language Models MLLMs are Deeply Affected by Modality Bias

Reference 49

Resolution
verified exact
arxiv_id, observed 2026-05-10T07:11:53.732553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-05-10T07:02:02.752466Z digest=sha256:0bcbdc905879fe0889a1f86f6bf229a57c575aaf9a05a20b3b372137172ead50

Observation 4a808afb-0e03-4f25-96d0-9de968dac8e9 · inbound

MiMIC: Mitigating Visual Modality Collapse in Universal Multimodal Retrieval While Avoiding Semantic Misalignment cites this paper.

MiMIC: Mitigating Visual Modality Collapse in Universal Multimodal Retrieval While Avoiding Semantic Misalignment MLLMs are Deeply Affected by Modality Bias

Reference 112

Resolution
verified exact
arxiv_id, observed 2026-05-11T14:21:07.562485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-05-09T21:56:38.004300Z digest=sha256:bb33fe90a32d06bb6bfed88eb8da10ae9967a8279276ada4f8143695f065d3d5

Observation 977cd533-9d06-4171-ad7e-30545c81c955 · inbound

Do Composed Image Retrieval Benchmarks Require Multimodal Composition? cites this paper.

Do Composed Image Retrieval Benchmarks Require Multimodal Composition? MLLMs are Deeply Affected by Modality Bias

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-20T21:23:44.429486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-20T21:20:49.609788Z digest=sha256:37762063efcfa35d1b95378fd19cc075301f6e66d5491d0539347d1d42805ff4

Observation cc28aa76-c4c2-43ba-8e65-61703fccfcf6 · inbound

Semantic Generative Tuning for Unified Multimodal Models cites this paper.

Semantic Generative Tuning for Unified Multimodal Models MLLMs are Deeply Affected by Modality Bias

Reference 86

Resolution
metadata mismatch
arxiv_id, observed 2026-05-20T11:33:14.210167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-20T11:32:24.007847Z digest=sha256:e8785b959fea589dc8ba3bf08382f51def54cd532be78acef2f47c8c47348f0e

Observation 69715a54-940a-4a56-bd7d-4de3a12c796a · inbound

Semantic Generative Tuning for Unified Multimodal Models cites this paper.

Semantic Generative Tuning for Unified Multimodal Models MLLMs are Deeply Affected by Modality Bias

Reference 86

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T18:35:00.347720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-30T18:31:10.578558Z digest=sha256:a6c50761bd5a959d6c03f15bd38bc34027cd8d828fbaed6827701d0ab2273e04

Observation 762b4ad1-b48e-40dd-95e3-99e53e76a9a2 · inbound

Measuring Cross-Modal Synergy: A Benchmark for VLM Explainability cites this paper.

Measuring Cross-Modal Synergy: A Benchmark for VLM Explainability MLLMs are Deeply Affected by Modality Bias

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-05-22T06:06:08.750971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-22T06:05:34.039507Z digest=sha256:e5198a1a2991c3c61ffea2a5309fc4d216daf8beb9de18a901051fd8cf372a9f

Observation 7a9952f2-aa1f-416c-a298-703ef7fd3898 · inbound

Sci-Rho: A Multilingual Visually-Grounded Symbolic Benchmark for STEM Problems cites this paper.

Sci-Rho: A Multilingual Visually-Grounded Symbolic Benchmark for STEM Problems MLLMs are Deeply Affected by Modality Bias

Reference 78

Resolution
verified exact
arxiv_id, observed 2026-07-02T20:47:22.882322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-06-27T20:11:02.445626Z digest=sha256:04a650876383499a1c5a98453c7de33aad7bf482047ff66cee5f96a72b9db8e7

Observation 4c0022df-3301-495b-a119-c24451d8c3f6 · inbound

Pareto LoRA: Mitigating Modality Imbalance in Unified Multimodal Models via Pareto-Optimal Gradient Integration cites this paper.

Pareto LoRA: Mitigating Modality Imbalance in Unified Multimodal Models via Pareto-Optimal Gradient Integration MLLMs are Deeply Affected by Modality Bias

Reference 46

Resolution
verified exact
arxiv_id, observed 2026-07-03T17:58:47.380374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-27T03:31:02.911020Z digest=sha256:3e36d97c33c7f64d98441590d594965dfcd5ea82c367388265c7ac7d9f41cd68

Observation d21d4326-f5ba-4049-a2d6-e75804a0f5a1 · inbound

Vesta: A Generalist Embodied Reasoning Model cites this paper.

Vesta: A Generalist Embodied Reasoning Model MLLMs are Deeply Affected by Modality Bias

Reference 161

Resolution
verified exact
arxiv_id, observed 2026-07-04T04:29:35.710669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-26T16:55:12.518255Z digest=sha256:f2e6b6fade62459f9df964f0e7cf436418eaf92ed2a0ba7b500983c73adec631

Observation 9f8ed3fd-eddc-4c01-be3d-3b7158916600 · inbound

CAAD: Contrastive Audio-Aware Distillation for Efficient Speech Language Models cites this paper.

CAAD: Contrastive Audio-Aware Distillation for Efficient Speech Language Models MLLMs are Deeply Affected by Modality Bias

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-07-04T11:59:50.226221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-26T07:28:54.731659Z digest=sha256:887c2a0ee5d79550a2165be7cb1503b73ea0aeb2a2c5bfe4e342d24a3a145762

Observation 2428e129-1e3e-41a7-a55b-7e035829ebef · inbound

Allocation Before Ranking: Decoupled Token Compression for OmniLLMs cites this paper.

Allocation Before Ranking: Decoupled Token Compression for OmniLLMs MLLMs are Deeply Affected by Modality Bias

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-04T23:23:39.041606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T23:23:39.041606Z digest=sha256:d1128affa409854d78bcde8265dd35cb48efbae98ca8fb56d8784f0dfc8f493b

Observation bd0c3c6f-90de-49e1-b67a-77a01b36c8f5 · inbound

Structure-Preserving Projection for Mitigating Modality Bias in LLM-Based Sequential Recommendation cites this paper.

Structure-Preserving Projection for Mitigating Modality Bias in LLM-Based Sequential Recommendation MLLMs are Deeply Affected by Modality Bias

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-14T04:35:43.369407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T04:35:43.369407Z digest=sha256:20d52acf7c04178ecc228d7dced7e9c6fd73177914336664d95c9f1a21c6af30