Paper Citation Record · LEDGER

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models

As of 9 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 3 inbound Pith citation observations for arXiv:2505.17316.

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

pith.paper-citation-record.v1
2505.17316 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:52:09.476244Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-12T04:35:49.701061Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

52 of 52 outbound references displayed

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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation fadc0ca7-aeda-4650-b84f-c888ac9a3105 · outbound

This paper cites Visual instruction tuning,.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Visual instruction tuning,

Reference 1

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Observation 29de9b4f-7a6c-45b0-ba66-65863a79d134 · outbound

This paper cites Improved baselines with visual instruction tuning,.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Improved baselines with visual instruction tuning,

Reference 2

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Observation 208045f1-7904-41c4-a316-77bcd68edc2c · outbound

This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,

Reference 3

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This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,

Reference 4

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Observation 5db061ba-bb92-4291-80e2-ec1c3ab061f2 · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 5

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Observation 98609cdd-071d-48fc-a888-967810b5d0dd · outbound

This paper cites Flamingo: a visual language model for few-shot learning,.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Flamingo: a visual language model for few-shot learning,

Reference 6

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Observation bc4869a1-3879-4476-9c1f-272048cb17a6 · outbound

This paper cites Language is not all you need: Aligning perception with language models,.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Language is not all you need: Aligning perception with language models,

Reference 7

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Observation 47117c81-1b56-4a5b-aae9-9bfb30599547 · outbound

This paper cites Multimodal Chain-of-Thought Reasoning in Language Models.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Multimodal Chain-of-Thought Reasoning in Language Models

Reference 8

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This paper cites Kimi k1.5: Scaling reinforcement learning with llms,.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Kimi k1.5: Scaling reinforcement learning with llms,

Reference 9

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Observation 61a44add-9445-4537-aaa6-470852bb6cc7 · outbound

This paper cites Multimodal transformer with multi-view visual representation for image captioning,.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Multimodal transformer with multi-view visual representation for image captioning,

Reference 10

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Observation 12569a1d-b954-4dba-951a-aa8085cbd105 · outbound

This paper cites Vqa: Visual question answering,.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Vqa: Visual question answering,

Reference 11

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Observation 8bb64434-aec4-491f-8c0d-89bf212f3a5b · outbound

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

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Eyes wide shut? exploring the visual shortcomings of multimodal llms,

Reference 12

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This paper cites GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest

Reference 13

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This paper cites Kosmos-2: Grounding Multimodal Large Language Models to the World.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Kosmos-2: Grounding Multimodal Large Language Models to the World

Reference 14

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This paper cites Glamm: Pixel grounding large multimodal model,.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Glamm: Pixel grounding large multimodal model,

Reference 15

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Observation 099610fd-b9dd-44de-855d-e0271fb59288 · outbound

This paper cites Hallucination of Multimodal Large Language Models: A Survey.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Hallucination of Multimodal Large Language Models: A Survey

Reference 16

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Observation d751b048-6e58-4c5e-9b9d-f1acb8e9589a · outbound

This paper cites Honeybee: Locality-enhanced projector for multi- modal llm,.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Honeybee: Locality-enhanced projector for multi- modal llm,

Reference 17

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Observation 3bcaf127-cac2-4f33-aa77-d8a119ec9186 · outbound

This paper cites The Platonic Representation Hypothesis.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models The Platonic Representation Hypothesis

Reference 18

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Observation 29f11f75-9d76-409a-8d86-51f6192b84f6 · outbound

This paper cites von Neumann,Mathematische Grundlagen der Quantenmechanik.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models von Neumann,Mathematische Grundlagen der Quantenmechanik

Reference 19

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This paper cites Linear algebraic structure of word senses, with applications to polysemy,.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Linear algebraic structure of word senses, with applications to polysemy,

Reference 20

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This paper cites Transformer visualization via dictionary learning: contextualized embedding as a linear superposition of transformer factors.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Transformer visualization via dictionary learning: contextualized embedding as a linear superposition of transformer factors

Reference 21

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Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Signal recovery from random measurements via orthogonal matching pursuit,

Reference 22

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Observation bc3cf9d0-2f91-4990-82cc-56bd5650f420 · outbound

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Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Recognize anything: A strong image tagging model,

Reference 23

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Observation 8fedc27c-4a1b-4068-8304-468498f53fef · outbound

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Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Grounding dino: Marrying dino with grounded pre-training for open-set object detection,

Reference 24

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Observation 69cb3100-d9ea-4117-95a9-8016afa821b8 · outbound

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Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Segment anything,

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This paper cites LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models

Reference 26

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This paper cites InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning

Reference 27

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Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models MIMIC-IT: Multi-Modal In-Context Instruction Tuning

Reference 28

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Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality

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This paper cites Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models

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This paper cites Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs

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Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Law of vision representation in mllms,

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Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Learning transferable visual models from natural language supervi- sion,

Reference 33

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Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Deciphering Cross-Modal Alignment in Large Vision-Language Models with Modality Integration Rate

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Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Towards Interpreting Visual Information Processing in Vision-Language Models

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Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Advancing Fine-Grained Visual Understanding with Multi-Scale Alignment in Multi-Modal Models

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Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models SEA: Supervised Embedding Alignment for Token-Level Visual-Textual Integration in MLLMs

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Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Honeybee: Locality-enhanced projector for multimodal llm,

Reference 38

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Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Microsoft coco: Common objects in context,

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Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Referitgame: Referring to objects in photographs of natural scenes,

Reference 40

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Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Generation and comprehension of unambiguous object descriptions,

Reference 41

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This paper cites Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic

Reference 42

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This paper cites Introducing idefics: An open reproduction of state-of-the-art visual language model,.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Introducing idefics: An open reproduction of state-of-the-art visual language model,

Reference 43

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

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 44

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Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Instruction Tuning with GPT-4

Reference 45

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Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models The Llama 3 Herd of Models

Reference 46

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Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:11.305627Z

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Observation 717aab0c-5678-414b-b5a8-09c0685b4fdd · outbound

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Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Towards vqa models that can read,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:11.043224Z

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Observation b5e92fd3-23ec-473a-ba67-f160ea063043 · outbound

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Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Gqa: A new dataset for real-world visual reasoning and compositional question answering,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:10.787542Z

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Observation 9143f5af-b801-45d4-b5fa-ced2989a23a3 · outbound

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Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Ocr-vqa: Visual question answering by reading text in images,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:10.559675Z

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Observation 894462a2-6627-4fe4-a7f7-ad0abbf5ec84 · outbound

This paper cites Visual genome: Connecting language and vision using crowdsourced dense image annotations,.

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Visual genome: Connecting language and vision using crowdsourced dense image annotations,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:10.298358Z

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Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models Unresolved cited work

Reference 1932

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Pith citing papers

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Latent Denoising Improves Visual Alignment in Large Multimodal Models cites this paper.

Latent Denoising Improves Visual Alignment in Large Multimodal Models Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models

Reference 39

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PluRule: A Benchmark for Moderating Pluralistic Communities on Social Media cites this paper.

PluRule: A Benchmark for Moderating Pluralistic Communities on Social Media Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models

Reference 39

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Text as Partial Constraint: Core-Residual Alignment for Robust Vision-Language Learning cites this paper.

Text as Partial Constraint: Core-Residual Alignment for Robust Vision-Language Learning Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models

Reference 18

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