Paper Citation Record · LEDGER

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion

As of 15 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 2 inbound Pith citation observations for arXiv:2412.01289.

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

pith.paper-citation-record.v1
2412.01289 v2

Coverage vector

measured 56 of 56 reference resolution

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One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Source: arxiv_reference, observed 2026-07-02T16:27:09.401525Z

Reference resolution

56 of 56 outbound references displayed

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External citation measurements

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Outbound references

Observation e5020a5b-2820-412e-922d-8a01dcd07960 · outbound

This paper cites Llama 3 model card.

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Llama 3 model card

Reference 1

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Observation 579021a5-be40-4b14-b897-eb5b2b030ef5 · outbound

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

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Flamingo: a visual language model for few-shot learning

Reference 2

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

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 3

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Observation c84bf1a3-e5d7-4fe6-a94e-c2360674bb86 · outbound

This paper cites Token Merging: Your ViT But Faster.

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Token Merging: Your ViT But Faster

Reference 4

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This paper cites Honeybee: Locality-enhanced projector for multimodal llm.

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Honeybee: Locality-enhanced projector for multimodal llm

Reference 5

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Observation 578e2d59-2d4a-4796-888a-adb393df60e7 · outbound

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

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic

Reference 6

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Observation 89f70929-9fb2-4836-8a7c-a31a4843be58 · outbound

This paper cites An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceleration for large vision-language models, 2024.

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceleration for large vision-language models, 2024

Reference 7

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Observation 3fd7e438-4b00-4406-ab26-73712da16cb6 · outbound

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

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, march

Reference 8

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Observation 55d157bb-c303-4343-a09c-d07f1ad997d5 · outbound

This paper cites Fusing finetuned models for better pretraining.

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Fusing finetuned models for better pretraining

Reference 9

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Observation e3c46c07-88a7-4f99-a008-8230dc87b68f · outbound

This paper cites MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices.

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices

Reference 10

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

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Instructblip: Towards general- purpose vision-language models with instruction tuning,

Reference 11

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This paper cites Epsilon Sampling Rocks: Investigating Sampling Strategies for Minimum Bayes Risk Decoding for Machine Translation.

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Epsilon Sampling Rocks: Investigating Sampling Strategies for Minimum Bayes Risk Decoding for Machine Translation

Reference 12

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This paper cites Mme: A compre- hensive evaluation benchmark for multimodal large language models, 2024.

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Mme: A compre- hensive evaluation benchmark for multimodal large language models, 2024

Reference 13

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This paper cites ConvLLaVA: Hierarchical Backbones as Visual Encoder for Large Multimodal Models.

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion ConvLLaVA: Hierarchical Backbones as Visual Encoder for Large Multimodal Models

Reference 14

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This paper cites Making the v in vqa matter: Elevating the role of image understanding in visual question answer- ing.

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Making the v in vqa matter: Elevating the role of image understanding in visual question answer- ing

Reference 15

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Observation 0765a90e-1a92-4eb8-8f00-fd0f3af35a10 · outbound

This paper cites Making the v in vqa matter: Elevating the role of image understanding in visual question answer- ing.

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Making the v in vqa matter: Elevating the role of image understanding in visual question answer- ing

Reference 16

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This paper cites Vizwiz grand challenge: Answering visual questions from blind people.

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Vizwiz grand challenge: Answering visual questions from blind people

Reference 17

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This paper cites Onellm: One framework to align all modalities with language.

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Onellm: One framework to align all modalities with language

Reference 18

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Editing models with task arithmetic

Reference 19

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Editing models with task arithmetic

Reference 20

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This paper cites LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion.

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 21

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Dataless knowledge fusion by merging weights of language models

Reference 22

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This paper cites A diagram is worth a dozen images.

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion A diagram is worth a dozen images

Reference 23

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion In- troducing idefics: An open reproduction of state-of-the-art visual language model, 2023

Reference 24

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

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 25

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models

Reference 26

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models

Reference 27

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This paper cites Video-LLaVA: Learning United Visual Representation by Alignment Before Projection.

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Video-LLaVA: Learning United Visual Representation by Alignment Before Projection

Reference 28

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Vila: On pre-training for visual language models

Reference 29

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Microsoft coco: Common objects in context

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This paper cites Mitigating hallucination in large multi-modal models via robust instruction tuning.

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Mitigating hallucination in large multi-modal models via robust instruction tuning

Reference 31

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Improved baselines with visual instruction tuning

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Llava-next: Im- proved reasoning, ocr, and world knowledge, 2024

Reference 33

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Visual instruction tuning

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This paper cites BitDelta: Your Fine-Tune May Only Be Worth One Bit.

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion BitDelta: Your Fine-Tune May Only Be Worth One Bit

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion MMBench: Is Your Multi-modal Model an All-around Player?

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion An Empirical Study of Scaling Instruct-Tuned Large Multimodal Models

Reference 38

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Learn- ing transferable visual models from natural language super- vision

Reference 39

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders

Reference 40

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Animating rotation with quaternion curves

Reference 41

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Towards vqa models that can read

Reference 42

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Visualizing data using t-sne

Reference 43

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Knowledge Fusion of Large Language Models

Reference 44

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Grok-1.5 Vision Preview, 2024

Reference 45

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning

Reference 46

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion TIES-Merging: Resolving Interference When Merging Models

Reference 47

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Law of vision represen- tation in mllms

Reference 48

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality

Reference 49

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This paper cites A Survey on Multimodal Large Language Models.

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion A Survey on Multimodal Large Language Models

Reference 50

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Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Language models are super mario: Absorbing abilities from homologous models as a free lunch

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:35:53.250350Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ba3966b2-3d3e-4c1e-922a-3b6331e1f0c1 · outbound

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

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for ex- pert agi

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:35:53.232358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 558f1a50-6dac-4dea-b57e-712b593cedeb · outbound

This paper cites LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding.

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-12T04:35:52.465577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2293816a-e9c4-45b7-9785-2041aec0a79d · outbound

This paper cites Beyond LLaVA-HD: Diving into High-Resolution Large Multimodal Models.

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion Beyond LLaVA-HD: Diving into High-Resolution Large Multimodal Models

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-12T04:35:52.470510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af8029f1-3b68-4ea6-857e-e5fb67d7643a · outbound

This paper cites SVIT: Scaling up Visual Instruction Tuning.

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion SVIT: Scaling up Visual Instruction Tuning

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-12T04:35:52.475515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35fc70fc-8ca2-46a9-8ed4-77b774c000ba · outbound

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

Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-12T04:35:52.480788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 81cc936d-1ccb-4918-be52-a8ad35fac335 · inbound

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging cites this paper.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-10T07:06:52.904006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d395fd3b-2d8b-4ac5-9735-002f65d9c99b · inbound

Closed-Form Spectral Regularization for Multi-Task Model Merging cites this paper.

Closed-Form Spectral Regularization for Multi-Task Model Merging Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:27:09.403073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.