Paper Citation Record · LEDGER

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models

As of 18 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 1 inbound Pith citation observation for arXiv:2508.06895.

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

pith.paper-citation-record.v1
2508.06895 v1

Coverage vector

measured 72 of 72 reference resolution

Typed states for the displayed outbound observations.

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measured 73 of 73 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T15:05:31.609683Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T03:37:35.644847Z

Reference resolution

72 of 72 outbound references displayed

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External citation measurements

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Outbound references

Observation c70655ab-56a5-4c64-bcc4-f669907cb552 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 1

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Observation 8547c9d4-2611-4401-8dae-29d19e8c4628 · outbound

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

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Flamingo: a visual language model for few-shot learning

Reference 2

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Observation 5f44d82c-5bda-47ac-a98c-b965b523c10e · outbound

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

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 3

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Observation a14b0dbd-b9c8-40c4-a722-e6160cca53aa · outbound

This paper cites BEiT: BERT Pre-Training of Image Transformers.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models BEiT: BERT Pre-Training of Image Transformers

Reference 4

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Observation ea69dbda-7193-4816-a5a4-94047e0dd567 · outbound

This paper cites Introducing our multimodal models, 2023.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Introducing our multimodal models, 2023

Reference 5

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Observation 286fbd26-d5d9-43fe-83b7-8e642984c910 · outbound

This paper cites Eliciting Latent Predictions from Transformers with the Tuned Lens.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Eliciting Latent Predictions from Transformers with the Tuned Lens

Reference 6

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Observation f00a6e90-2e19-4a0b-84b4-e36a274994d6 · outbound

This paper cites Mechanistic Interpretability for AI Safety -- A Review.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Mechanistic Interpretability for AI Safety -- A Review

Reference 7

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This paper cites Towards monose- manticity: Decomposing language models with dictionary learning.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Towards monose- manticity: Decomposing language models with dictionary learning

Reference 8

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This paper cites InternLM2 Technical Report.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models InternLM2 Technical Report

Reference 9

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

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Honeybee: Locality-enhanced projector for multimodal llm

Reference 10

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Observation 4b911440-bcf0-4973-b37a-3b6aef5f396f · outbound

This paper cites Sssd: Self-supervised self distillation.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Sssd: Self-supervised self distillation

Reference 11

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Observation ee4f9e61-2f6d-45bf-bad7-0e323b25dc70 · outbound

This paper cites How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

Reference 12

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This paper cites Gonzalez, Ion Stoica, and Eric P.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Gonzalez, Ion Stoica, and Eric P

Reference 13

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This paper cites On the efficacy of knowledge distillation.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models On the efficacy of knowledge distillation

Reference 14

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This paper cites Towards automated circuit discovery for mechanistic interpretability.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Towards automated circuit discovery for mechanistic interpretability

Reference 15

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Observation 017ca2ca-ff9b-4ced-a391-915a93fb7aec · outbound

This paper cites Sparse Autoencoders Find Highly Interpretable Features in Language Models.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Sparse Autoencoders Find Highly Interpretable Features in Language Models

Reference 16

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BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Unresolved cited work

Reference 17

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Observation 2b905e90-76bd-496b-8670-40fdc7d8fa8c · outbound

This paper cites The Llama 3 Herd of Models.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models The Llama 3 Herd of Models

Reference 18

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This paper cites Softmax linear units.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Softmax linear units

Reference 19

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This paper cites Making LLaMA SEE and Draw with SEED Tokenizer.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Making LLaMA SEE and Draw with SEED Tokenizer

Reference 20

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This paper cites Knowledge distillation: A survey.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Knowledge distillation: A survey

Reference 21

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Observation 54b1e966-2a24-4446-b3ca-c33e26b2df1d · outbound

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

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Making the v in vqa matter: Elevating the role of image understanding in visual question answer- ing

Reference 22

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

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Vizwiz grand challenge: Answering visual questions from blind people

Reference 23

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This paper cites Learning lightweight lane detection cnns by self at- 9 tention distillation.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Learning lightweight lane detection cnns by self at- 9 tention distillation

Reference 24

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

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Gqa: A new dataset for real-world visual reasoning and compositional question answering

Reference 25

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BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Perceiver: General perception with iterative attention

Reference 26

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BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Mistral 7B

Reference 27

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This paper cites Unified language-vision pretraining in LLM with dynamic discrete visual tokenization.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Unified language-vision pretraining in LLM with dynamic discrete visual tokenization

Reference 28

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BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Referitgame: Referring to objects in pho- tographs of natural scenes

Reference 29

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BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Segment any- thing

Reference 30

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

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Obelics: An open web-scale filtered dataset of interleaved image-text documents

Reference 31

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

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 32

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This paper cites Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning

Reference 33

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This paper cites Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla

Reference 34

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BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Improved baselines with visual instruction tuning

Reference 35

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BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Visual instruction tuning

Reference 36

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

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Mmbench: Is your multi-modal model an all-around player? In European Conference on Computer Vision, pages 216–233

Reference 37

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

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Learn to explain: Multimodal reasoning via thought chains for science question answering

Reference 38

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BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Generation and comprehension of unambiguous object descriptions

Reference 39

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BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Self-distillation amplifies regularization in hilbert space

Reference 40

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This paper cites DINOv2: Learning Robust Visual Features without Supervision.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models DINOv2: Learning Robust Visual Features without Supervision

Reference 41

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This paper cites Towards vision-language mechanistic interpretabil- ity: A causal tracing tool for blip.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Towards vision-language mechanistic interpretabil- ity: A causal tracing tool for blip

Reference 42

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This paper cites Bridging Vision and Language Spaces with Assignment Prediction.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Bridging Vision and Language Spaces with Assignment Prediction

Reference 43

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BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Relational knowledge distillation

Reference 44

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BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Multi-modal Auto-regressive Modeling via Visual Words

Reference 45

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This paper cites BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers

Reference 46

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BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Distillation-based training for multi-exit architectures

Reference 47

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

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Learning transferable visual models from natural language supervi- sion

Reference 48

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Observation 68fa1b46-277f-42de-b208-af31d40615e4 · outbound

This paper cites https://sharegpt.com/, 2023.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models https://sharegpt.com/, 2023

Reference 49

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Observation ef80358d-b4f4-414a-8d08-98c36f3996a2 · outbound

This paper cites Self-distillation from the last mini-batch for consis- tency regularization.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Self-distillation from the last mini-batch for consis- tency regularization

Reference 50

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This paper cites Towards vqa models that can read.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Towards vqa models that can read

Reference 51

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BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Emu: Generative pretraining in multimodality

Reference 52

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BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Generative multimodal mod- els are in-context learners

Reference 53

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This paper cites Chameleon: Mixed-Modal Early-Fusion Foundation Models.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Chameleon: Mixed-Modal Early-Fusion Foundation Models

Reference 54

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BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Gemma: Open Models Based on Gemini Research and Technology

Reference 55

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BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Neural discrete representation learning

Reference 56

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

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 57

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BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models CogVLM: Visual Expert for Pretrained Language Models

Reference 58

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This paper cites Do Llamas Work in English? On the Latent Language of Multilingual Transformers.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Do Llamas Work in English? On the Latent Language of Multilingual Transformers

Reference 59

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This paper cites Libra: Building decoupled vision system on large lan- guage models.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Libra: Building decoupled vision system on large lan- guage models

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:35:51.768776Z

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BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Qwen2 Technical Report

Reference 61

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This paper cites Snapshot distillation: Teacher-student optimization in one generation.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Snapshot distillation: Teacher-student optimization in one generation

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:35:51.752840Z

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This paper cites mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models

Reference 63

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This paper cites Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation

Reference 64

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

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities

Reference 65

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This paper cites How do Large Language Models Learn In-Context? Query and Key Matrices of In-Context Heads are Two Towers for Metric Learning.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models How do Large Language Models Learn In-Context? Query and Key Matrices of In-Context Heads are Two Towers for Metric Learning

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-05T22:35:50.970245Z

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

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Transformer visualization via dictionary learning: contextualized embedding as a linear superposition of transformer factors

Reference 67

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This paper cites Sigmoid loss for language image pre-training.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Sigmoid loss for language image pre-training

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:35:51.733114Z

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Observation 107a0250-cf9a-4c02-9cad-a6a47b7d1a3f · outbound

This paper cites Be your own teacher: Improve the performance of convolutional neural networks via self distillation.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Be your own teacher: Improve the performance of convolutional neural networks via self distillation

Reference 69

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Observation 5405491c-0ee7-4509-ac3e-1fe47aa53663 · outbound

This paper cites Self- distillation: Towards efficient and compact neural networks.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Self- distillation: Towards efficient and compact neural networks

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:35:51.702294Z

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Observation 16ed01c1-cdd9-4097-b31d-5e42bfcd5df1 · outbound

This paper cites Self-distillation as instance- specific label smoothing.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Self-distillation as instance- specific label smoothing

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:35:51.686436Z

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Observation fee9d293-fe43-469a-8139-a64fb92017f2 · outbound

This paper cites Decoupled knowledge distillation.

BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models Decoupled knowledge distillation

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:35:51.669092Z

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Pith citing papers

Observation 7f1da0d9-bb6c-42ec-8773-cebbd1aa81f4 · inbound

RespiraMFM: A Multimodal Foundation Model with Contrastive Audio-Language Alignment for Respiratory Disease Identification cites this paper.

RespiraMFM: A Multimodal Foundation Model with Contrastive Audio-Language Alignment for Respiratory Disease Identification BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models

Reference 43

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