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Paper Citation Record · LEDGER

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation

As of 14 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2507.07274.

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

pith.paper-citation-record.v1
2507.07274 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:48:33.976690Z

measured 35 of 35 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 277c9638-a88a-4a02-87e7-9deb261c118b · outbound

This paper cites The multilingual mind: A survey of multilingual reasoning in language models,.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation The multilingual mind: A survey of multilingual reasoning in language models,

Reference 1

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Observation 8a75e55a-b008-4d3b-ab5f-4bb173f0d609 · outbound

This paper cites All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages

Reference 2

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Observation c551ffdf-920a-4161-849c-5baf8f99d96f · outbound

This paper cites VQA: Visual question answering,.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation VQA: Visual question answering,

Reference 3

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Observation 929370b6-b0f7-4d9a-b377-fd99cf095e72 · outbound

This paper cites A-okvqa: A benchmark for visual question answering using world knowledge,.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation A-okvqa: A benchmark for visual question answering using world knowledge,

Reference 4

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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.

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Observation 279c0d45-01b8-4d56-ad4e-8c6d8bfe1cdd · outbound

This paper cites Clevr: A diagnostic dataset for compositional language and elementary visual reasoning,.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation Clevr: A diagnostic dataset for compositional language and elementary visual reasoning,

Reference 5

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Observation 5fa37962-cbec-4b83-88b5-d0861fca06e5 · outbound

This paper cites Microsoft coco: Common objects in context,.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation Microsoft coco: Common objects in context,

Reference 6

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Observation a7e782d7-faa7-460d-bf21-1d92d0e59f42 · outbound

This paper cites From recognition to cognition: Visual commonsense reasoning,.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation From recognition to cognition: Visual commonsense reasoning,

Reference 7

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

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Observation 15c61380-9922-4123-9e5e-d741028ebd3e · outbound

This paper cites The State and Fate of Linguistic Diversity and Inclusion in the NLP World.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation The State and Fate of Linguistic Diversity and Inclusion in the NLP World

Reference 8

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Observation 51094169-c967-4b44-a7fb-770020cd3348 · outbound

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

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities

Reference 9

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Observation 50918f8a-5ef0-4397-819a-e86829ef4563 · outbound

This paper cites AlignMMBench: Evaluating Chinese Multimodal Alignment in Large Vision-Language Models.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation AlignMMBench: Evaluating Chinese Multimodal Alignment in Large Vision-Language Models

Reference 10

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Observation c3f09204-2769-4efa-899e-0c4db9ea4ec5 · outbound

This paper cites Seed-bench: Benchmarking multimodal large language models,.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation Seed-bench: Benchmarking multimodal large language models,

Reference 11

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Observation 63ac98c7-6851-4384-b7b9-b667751a7308 · outbound

This paper cites EXAMS-V: A Multi-Discipline Multilingual Multimodal Exam Benchmark for Evaluating Vision Language Models.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation EXAMS-V: A Multi-Discipline Multilingual Multimodal Exam Benchmark for Evaluating Vision Language Models

Reference 12

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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.

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Observation 7e1ac5e5-6ef0-4e24-b8ba-ba45e928ebd4 · outbound

This paper cites MVL-SIB: A Massively Multilingual Vision-Language Benchmark for Cross-Modal Topical Matching.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation MVL-SIB: A Massively Multilingual Vision-Language Benchmark for Cross-Modal Topical Matching

Reference 13

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Observation 2b3083bb-29df-47a1-8522-a90132a8fb6a · outbound

This paper cites BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models

Reference 14

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Observation 0d53b239-64c7-46f8-a2f4-8e6176809506 · outbound

This paper cites Humanibench: A human-centric framework for large multimodal models evaluation,.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation Humanibench: A human-centric framework for large multimodal models evaluation,

Reference 15

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

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Observation 62b2fd80-270e-4193-8b53-625b0e05961d · outbound

This paper cites Multimodal large language models: A survey,.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation Multimodal large language models: A survey,

Reference 16

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Observation ad48293e-7269-4dea-b663-5e103da3bdea · outbound

This paper cites WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training

Reference 17

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Observation 7b16a71d-6410-4603-b61d-8ef28a8d5853 · outbound

This paper cites UNKs Everywhere: Adapting Multilingual Language Models to New Scripts.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation UNKs Everywhere: Adapting Multilingual Language Models to New Scripts

Reference 18

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Observation 81a3cdb7-2380-4b82-9951-46950f8a72c6 · outbound

This paper cites Afriberta: Towards viable multilingual language models for low-resource languages,.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation Afriberta: Towards viable multilingual language models for low-resource languages,

Reference 19

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Observation d9d8502c-ae0f-46f7-bd65-750a500d16f6 · outbound

This paper cites mgpt: Few-shot learners go multilingual,.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation mgpt: Few-shot learners go multilingual,

Reference 20

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Source-reported events for the cited work

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Observation 4b4ece51-957f-4b4f-aebf-f8ab015fc60d · outbound

This paper cites XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation

Reference 21

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local_arxiv, observed 2026-08-06T18:48:34.349496Z

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Observation 97df9763-0cb6-4173-82f6-1a58a82dd509 · outbound

This paper cites Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation

Reference 22

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Observation c44afdc6-6ba8-410d-b38b-c7bc0dac977e · outbound

This paper cites MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages

Reference 23

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Observation 2e76ae1f-5719-4e86-980d-ff8d5620f2bf · outbound

This paper cites Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models

Reference 24

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Observation 3c6900d3-dca9-4623-ba67-affddec11ae2 · outbound

This paper cites Parameter-efficient fine-tuning in large language models: a survey of methodologies,.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation Parameter-efficient fine-tuning in large language models: a survey of methodologies,

Reference 25

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Observation 3a3271ef-5442-4a65-90a4-b237afed50c9 · outbound

This paper cites A review on fairness in machine learning,.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation A review on fairness in machine learning,

Reference 26

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

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Observation fd0a7165-9efb-49c7-9d8a-056d88e9b548 · outbound

This paper cites Gemma 3 Technical Report.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation Gemma 3 Technical Report

Reference 27

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Observation 7a09bfa7-79ce-44c4-a1aa-8db432405174 · outbound

This paper cites The Llama 3 Herd of Models.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation The Llama 3 Herd of Models

Reference 28

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Observation fc151baf-d6ea-45d1-a5f8-7f3a888f11ba · outbound

This paper cites Phi-4 Technical Report.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation Phi-4 Technical Report

Reference 29

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source=pdf_text observed=2026-08-06T18:48:33.031590Z digest=sha256:bc73689846b755ac2801875c5d32b1b98df68a04c1549e28179c807c2e043e0e

Observation ad2a30f7-53b3-495d-8165-c3099d406ff4 · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 30

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Observation e3cf893e-f8fd-41ba-a380-45c3befcdc7f · outbound

This paper cites GPT-4o System Card,.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation GPT-4o System Card,

Reference 31

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verified fuzzy
raw_fallback, observed 2026-08-06T18:48:35.474644Z

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 c1f2bc30-1a1b-4b2e-a0b4-9a2a90ac7962 · outbound

This paper cites Gemini 2.0 Flash,.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation Gemini 2.0 Flash,

Reference 32

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verified fuzzy
raw_fallback, observed 2026-08-06T18:48:35.306649Z

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 b9938523-afee-4aa7-8a1f-c75efda0c47a · outbound

This paper cites Aya vision: Expanding the worlds ai can see,.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation Aya vision: Expanding the worlds ai can see,

Reference 33

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verified fuzzy
raw_fallback, observed 2026-08-06T18:48:35.127169Z

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 8d0f73e4-1e86-497a-a4e2-4b586011f8e9 · outbound

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

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 34

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation d96814f0-2dc5-4d6c-89b7-f4da3d6dc835 · outbound

This paper cites Fair enough: Develop and assess a fair-compliant dataset for large language model training?,.

LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation Fair enough: Develop and assess a fair-compliant dataset for large language model training?,

Reference 35

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raw_fallback, observed 2026-08-06T18:48:34.920942Z

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source=pdf_text observed=2026-08-06T18:48:33.976690Z digest=sha256:fe77869e067ec87e1e5143ec438cb58dc68d841b10bf0f74cd6f222ca6ec9fb0

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