Paper Citation Record · LEDGER

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models

As of 11 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2501.08443.

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

pith.paper-citation-record.v1
2501.08443 v3

Coverage vector

measured 44 of 44 reference resolution

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measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

44 of 44 outbound references displayed

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External citation measurements

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Outbound references

Observation be73d4bd-ef57-42ee-874f-f9a60d38f61d · outbound

This paper cites Improved baselines with visual instruction tuning.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Improved baselines with visual instruction tuning

Reference 1

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Observation 8490bfac-f1e6-4a41-aa93-ec56dabbd854 · outbound

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

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Instructblip: Towards general-purpose vision-language models with instruction tuning

Reference 2

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Observation d701c504-7e14-4aa5-8d31-d043187974f5 · outbound

This paper cites MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning

Reference 3

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Observation 89922604-efeb-46ff-9162-fa71417b4beb · outbound

This paper cites Llava-med: Training a large language-and-vision assistant for biomedicine in one day.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Llava-med: Training a large language-and-vision assistant for biomedicine in one day

Reference 4

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Observation 8d25a3c1-cc19-4567-a9b9-fb99a4f20347 · outbound

This paper cites Talk2bev: Language-enhanced bird’s- 29 eye view maps for autonomous driving.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Talk2bev: Language-enhanced bird’s- 29 eye view maps for autonomous driving

Reference 5

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Observation cf590dee-02c1-45e6-80ce-e2caa7310f03 · outbound

This paper cites What can VLMs do for zero-shot embodied task planning? In ICML 2024 Workshop on LLMs and Cognition , 2024.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models What can VLMs do for zero-shot embodied task planning? In ICML 2024 Workshop on LLMs and Cognition , 2024

Reference 6

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Observation 2fef787f-f3b7-4cc9-b564-136d50c348cf · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 7

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Observation 2a057600-aee0-43cd-aabc-e2ed182e82ea · outbound

This paper cites Learning transferable visual models from natural language supervision.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Learning transferable visual models from natural language supervision

Reference 8

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Observation 74fb2fdd-49a9-4c36-b160-f9baa2d2b086 · outbound

This paper cites Sig- moid loss for language image pre-training.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Sig- moid loss for language image pre-training

Reference 9

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Observation e70968f1-08fb-4f95-9d73-b04acc9a92c0 · outbound

This paper cites A Survey on Hallucination in Large Vision-Language Models.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models A Survey on Hallucination in Large Vision-Language Models

Reference 10

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Observation 3c60e1f1-274a-4bfa-914c-0e559bffa61d · outbound

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

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Eyes wide shut? exploring the visual shortcomings of multimodal llms

Reference 11

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Observation 653fdad4-a124-4cb3-b982-d97938d745f8 · outbound

This paper cites Teaching matters: Investigating the role of supervision in vision transformers.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Teaching matters: Investigating the role of supervision in vision transformers

Reference 12

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Observation af3d2fe9-75c3-4303-b35a-1ba8b32c0ed8 · outbound

This paper cites What do Vision Transformers Learn? A Visual Exploration.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models What do Vision Transformers Learn? A Visual Exploration

Reference 13

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Observation 90f23e13-0e69-404d-b768-e6734529e72e · outbound

This paper cites Dense Connector for MLLMs.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Dense Connector for MLLMs

Reference 14

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Observation 8246b6d9-140e-4581-9051-2b84db952a05 · outbound

This paper cites MMFuser: Multimodal Multi-Layer Feature Fuser for Fine-Grained Vision-Language Understanding.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models MMFuser: Multimodal Multi-Layer Feature Fuser for Fine-Grained Vision-Language Understanding

Reference 15

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Observation 4631b653-1938-4e6c-b03f-3a27ec22ea07 · outbound

This paper cites MoME: Mixture of Multimodal Experts for Generalist Multimodal Large Language Models.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models MoME: Mixture of Multimodal Experts for Generalist Multimodal Large Language Models

Reference 16

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Observation 898b8b5a-0402-4615-9ee9-2154ab66256a · outbound

This paper cites MoVA: Adapting Mixture of Vision Experts to Multimodal Context.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models MoVA: Adapting Mixture of Vision Experts to Multimodal Context

Reference 17

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Observation f3580f57-c49a-4d0c-a359-49cfc8d51145 · outbound

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

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs

Reference 18

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Observation d7d7f11d-e134-46bf-b1f3-369914cb3555 · outbound

This paper cites V?: Guided visual search as a core mechanism in multimodal llms.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models V?: Guided visual search as a core mechanism in multimodal llms

Reference 19

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This paper cites Divide, Conquer and Combine: A Training-Free Framework for High-Resolution Image Perception in Multimodal Large Language Models.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Divide, Conquer and Combine: A Training-Free Framework for High-Resolution Image Perception in Multimodal Large Language Models

Reference 20

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Observation b02f5a5f-4c2b-4d89-96db-5a0f22b816c8 · outbound

This paper cites Evaluating Object Hallucination in Large Vision-Language Models.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Evaluating Object Hallucination in Large Vision-Language Models

Reference 21

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Observation 7b31f994-3db9-4df9-8637-ec7370a7d065 · outbound

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

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 22

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Observation bdfdb95f-bec3-4c6a-ae64-c86047a500fd · outbound

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

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Gqa: A new dataset for real- world visual reasoning and compositional question answering

Reference 23

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Observation 33bbe69a-0068-40b3-8dc3-4d50962adb11 · outbound

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

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Seed-bench: Benchmarking multimodal large language models

Reference 24

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Observation 2ff97242-887e-4944-a538-9141a8ab09a1 · outbound

This paper cites Mmbench: Is your multi-modal model an all-around player? In European Conference on Computer Vision, pages 216–233.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Mmbench: Is your multi-modal model an all-around player? In European Conference on Computer Vision, pages 216–233

Reference 25

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This paper cites MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts

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

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi

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This paper cites A diagram is worth a dozen images.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models A diagram is worth a dozen images

Reference 28

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Observation a46e26cb-fe04-4b59-869d-dffdb9d6281a · outbound

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

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Learn to explain: Mul- timodal reasoning via thought chains for science question answering

Reference 29

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Observation 1fbbf652-46d6-4fd8-b1d8-d0fa655c23b0 · outbound

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Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning

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Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Docvqa: A dataset for vqa on document images

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This paper cites OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models

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This paper cites Towards vqa models that can read.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Towards vqa models that can read

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Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Grok-1.5 vision preview

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Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Gaussian Error Linear Units (GELUs)

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Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Mpnet: Masked 34 and permuted pre-training for language understanding

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Observation 6965958c-569f-4dcd-ac64-8d22f287c9e9 · outbound

This paper cites Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality

Reference 37

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Observation ef5fe6bd-c25c-41eb-b6bb-58a347ec8ae2 · outbound

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Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Decoupled Weight Decay Regularization

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Observation 2b03c9c9-6175-447d-b0a2-a922ed74190e · outbound

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Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Vizwiz grand challenge: Answering visual questions from blind people

Reference 39

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Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities

Reference 40

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This paper cites mplug-owl2: Revolutionizing multi-modal large lan- guage model with modality collaboration.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models mplug-owl2: Revolutionizing multi-modal large lan- guage model with modality collaboration

Reference 41

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Observation 08e6b8ce-f8e3-4346-be79-123858363622 · outbound

This paper cites LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model

Reference 42

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This paper cites Obelics: An open web-scale fil- tered dataset of interleaved image-text documents.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Obelics: An open web-scale fil- tered dataset of interleaved image-text documents

Reference 43

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Observation b4dce60f-e2f2-4024-b239-f0c80c3ef830 · outbound

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

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 44

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