In-Context Collapse in Vision-Language Models and How to Mitigate it? | Paper Citation Record

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This paper cites Advances in Neural Information Processing Systems (NeurIPS).

In-Context Collapse in Vision-Language Models and How to Mitigate it? Advances in Neural Information Processing Systems (NeurIPS)

Reference 1

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Observation 1ae0ff46-6ca4-41ae-8df0-0c00860dfcf3 · outbound

This paper cites Flamingo: a Visual Language Model for Few-Shot Learning.

In-Context Collapse in Vision-Language Models and How to Mitigate it? Flamingo: a Visual Language Model for Few-Shot Learning

Reference 2

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In-Context Collapse in Vision-Language Models and How to Mitigate it? Visual Prompting via Image Inpainting

Reference 3

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In-Context Collapse in Vision-Language Models and How to Mitigate it? In: Advances in Neural Information Processing Systems (NeurIPS)

Reference 4

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In-Context Collapse in Vision-Language Models and How to Mitigate it? Dark Experience for General Continual Learning: a Strong, Simple Baseline

Reference 5

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This paper cites Journal of Artificial Intelligence Research 83.

In-Context Collapse in Vision-Language Models and How to Mitigate it? Journal of Artificial Intelligence Research 83

Reference 6

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This paper cites Dynamic Transformer Architecture for Continual Learning of Multimodal Tasks.

In-Context Collapse in Vision-Language Models and How to Mitigate it? Dynamic Transformer Architecture for Continual Learning of Multimodal Tasks

Reference 7

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This paper cites In: Findings of the Association for Computational Linguistics: EMNLP 2023.

In-Context Collapse in Vision-Language Models and How to Mitigate it? In: Findings of the Association for Computational Linguistics: EMNLP 2023

Reference 8

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This paper cites Efficient Lifelong Learning with A-GEM.

In-Context Collapse in Vision-Language Models and How to Mitigate it? Efficient Lifelong Learning with A-GEM

Reference 9

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This paper cites CoIN: A Benchmark of Continual Instruction tuNing for Multimodel Large Language Model.

In-Context Collapse in Vision-Language Models and How to Mitigate it? CoIN: A Benchmark of Continual Instruction tuNing for Multimodel Large Language Model

Reference 10

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This paper cites InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks.

In-Context Collapse in Vision-Language Models and How to Mitigate it? InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks

Reference 11

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This paper cites arXiv 2024 arXiv:2412.14133.

In-Context Collapse in Vision-Language Models and How to Mitigate it? arXiv 2024 arXiv:2412.14133

Reference 12

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

In-Context Collapse in Vision-Language Models and How to Mitigate it? InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning

Reference 13

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This paper cites arXiv preprint arXiv:240721783.

In-Context Collapse in Vision-Language Models and How to Mitigate it? arXiv preprint arXiv:240721783

Reference 14

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This paper cites What Do VLMs NOTICE? A Mechanistic Interpretability Pipeline for Gaussian-Noise-free Text-Image Corruption and Evaluation.

In-Context Collapse in Vision-Language Models and How to Mitigate it? What Do VLMs NOTICE? A Mechanistic Interpretability Pipeline for Gaussian-Noise-free Text-Image Corruption and Evaluation

Reference 15

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In-Context Collapse in Vision-Language Models and How to Mitigate it? In-Context Learning Creates Task Vectors

Reference 16

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This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

In-Context Collapse in Vision-Language Models and How to Mitigate it? LoRA: Low-Rank Adaptation of Large Language Models

Reference 17

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This paper cites Multi-modal Synthetic Data Training and Model Collapse: Insights from VLMs and Diffusion Models.

In-Context Collapse in Vision-Language Models and How to Mitigate it? Multi-modal Synthetic Data Training and Model Collapse: Insights from VLMs and Diffusion Models

Reference 18

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This paper cites Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning.

In-Context Collapse in Vision-Language Models and How to Mitigate it? Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning

Reference 19

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Observation 79b16fc2-0d09-489b-b9a6-846b65495a2c · outbound

This paper cites Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models.

In-Context Collapse in Vision-Language Models and How to Mitigate it? Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models

Reference 20

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This paper cites In: Advances in Neural Information Processing Systems (NeurIPS) 43.

In-Context Collapse in Vision-Language Models and How to Mitigate it? In: Advances in Neural Information Processing Systems (NeurIPS) 43

Reference 21

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This paper cites Many-Shot In-Context Learning in Multimodal Foundation Models.

In-Context Collapse in Vision-Language Models and How to Mitigate it? Many-Shot In-Context Learning in Multimodal Foundation Models

Reference 22

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This paper cites What's in the Image? A Deep-Dive into the Vision of Vision Language Models.

In-Context Collapse in Vision-Language Models and How to Mitigate it? What's in the Image? A Deep-Dive into the Vision of Vision Language Models

Reference 23

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In-Context Collapse in Vision-Language Models and How to Mitigate it? Overcoming catastrophic forgetting in neural networks

Reference 24

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This paper cites Trends in Cognitive Sciences https://doi.org/10.1016/j.tics.2016.05.004.

In-Context Collapse in Vision-Language Models and How to Mitigate it? Trends in Cognitive Sciences https://doi.org/10.1016/j.tics.2016.05.004

Reference 25

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In-Context Collapse in Vision-Language Models and How to Mitigate it? A continual learning survey: Defying forgetting in classification tasks

Reference 26

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This paper cites Otter: A Multi-Modal Model with In-Context Instruction Tuning.

In-Context Collapse in Vision-Language Models and How to Mitigate it? Otter: A Multi-Modal Model with In-Context Instruction Tuning

Reference 27

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In-Context Collapse in Vision-Language Models and How to Mitigate it? LLaVA-OneVision: Easy Visual Task Transfer

Reference 28

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This paper cites BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models.

In-Context Collapse in Vision-Language Models and How to Mitigate it? BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

Reference 29

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This paper cites arXiv 2025 arXiv:2505.17097.

In-Context Collapse in Vision-Language Models and How to Mitigate it? arXiv 2025 arXiv:2505.17097

Reference 30

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In-Context Collapse in Vision-Language Models and How to Mitigate it? Learning without Forgetting

Reference 31

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In-Context Collapse in Vision-Language Models and How to Mitigate it? Visual Instruction Tuning

Reference 32

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In-Context Collapse in Vision-Language Models and How to Mitigate it? Gradient Episodic Memory for Continual Learning

Reference 33

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This paper cites Psychological Review https://doi.org/10.1037/0033-295X.102.3.419.

In-Context Collapse in Vision-Language Models and How to Mitigate it? Psychological Review https://doi.org/10.1037/0033-295X.102.3.419

Reference 34

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In-Context Collapse in Vision-Language Models and How to Mitigate it? Towards Interpreting Visual Information Processing in Vision-Language Models

Reference 35

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In-Context Collapse in Vision-Language Models and How to Mitigate it? Transformer Circuits Thread 44

Reference 36

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This paper cites Cognitive Science https://doi.org/10.1111/j.1551-6709.2011.01214.

In-Context Collapse in Vision-Language Models and How to Mitigate it? Cognitive Science https://doi.org/10.1111/j.1551-6709.2011.01214

Reference 37

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In-Context Collapse in Vision-Language Models and How to Mitigate it? Towards Vision-Language Mechanistic Interpretability: A Causal Tracing Tool for BLIP

Reference 38

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In-Context Collapse in Vision-Language Models and How to Mitigate it? iCaRL: Incremental Classifier and Representation Learning

Reference 39

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This paper cites In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI-23), pp 3058–3066, https://doi.org/10.24963/ ijcai.2023/341.

In-Context Collapse in Vision-Language Models and How to Mitigate it? In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI-23), pp 3058–3066, https://doi.org/10.24963/ ijcai.2023/341

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This paper cites PNAS https://doi.org/ 10.1073/pnas.2502194122.

In-Context Collapse in Vision-Language Models and How to Mitigate it? PNAS https://doi.org/ 10.1073/pnas.2502194122

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Observation 492ee162-1ae2-417e-bc59-a92c25b8b532 · outbound

This paper cites Continual Learning with Deep Generative Replay.

In-Context Collapse in Vision-Language Models and How to Mitigate it? Continual Learning with Deep Generative Replay

Reference 42

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This paper cites CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks.

In-Context Collapse in Vision-Language Models and How to Mitigate it? CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks

Reference 43

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This paper cites LAMOL: LAnguage MOdeling for Lifelong Language Learning.

In-Context Collapse in Vision-Language Models and How to Mitigate it? LAMOL: LAnguage MOdeling for Lifelong Language Learning

Reference 44

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In-Context Collapse in Vision-Language Models and How to Mitigate it? Link-Context Learning for Multimodal LLMs

Reference 45

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In-Context Collapse in Vision-Language Models and How to Mitigate it? Function Vectors in Large Language Models

Reference 46

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In-Context Collapse in Vision-Language Models and How to Mitigate it? Multimodal Few-Shot Learning with Frozen Language Models

Reference 47

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This paper cites Nature Machine Intelligence https://doi.org/10.1038/s42256-022-00568-3.

In-Context Collapse in Vision-Language Models and How to Mitigate it? Nature Machine Intelligence https://doi.org/10.1038/s42256-022-00568-3

Reference 48

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Observation 1efa80e3-2e31-41d8-a17d-09d3b0a85b2e · outbound

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In-Context Collapse in Vision-Language Models and How to Mitigate it? Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 49

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Observation f62b34ac-89e4-4bd2-9a3f-4b1ba6d66a16 · outbound

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In-Context Collapse in Vision-Language Models and How to Mitigate it? Images Speak in Images: A Generalist Painter for In-Context Visual Learning

Reference 50

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Observation ed66c1cf-0541-4d8f-a9c0-f03302a03cbd · outbound

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In-Context Collapse in Vision-Language Models and How to Mitigate it? Orthogonal Subspace Learning for Language Model Continual Learning

Reference 51

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Observation cb23eb8b-acfa-4f7c-b8a1-017299a918b8 · outbound

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In-Context Collapse in Vision-Language Models and How to Mitigate it? SegGPT: Segmenting Everything In Context

Reference 52

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This paper cites SMoLoRA: Exploring and Defying Dual Catastrophic Forgetting in Continual Visual Instruction Tuning.

In-Context Collapse in Vision-Language Models and How to Mitigate it? SMoLoRA: Exploring and Defying Dual Catastrophic Forgetting in Continual Visual Instruction Tuning

Reference 53

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This paper cites arXiv preprint arXiv:251005024.

In-Context Collapse in Vision-Language Models and How to Mitigate it? arXiv preprint arXiv:251005024

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:05:20.166930Z

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Observation fa74df9c-2c29-4388-9cff-676c57337ada · outbound

This paper cites ModalPrompt: Towards Efficient Multimodal Continual Instruction Tuning with Dual-Modality Guided Prompt.

In-Context Collapse in Vision-Language Models and How to Mitigate it? ModalPrompt: Towards Efficient Multimodal Continual Instruction Tuning with Dual-Modality Guided Prompt

Reference 55

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Observation 60142902-c718-4bed-89aa-85923076d2af · outbound

This paper cites Continual Learning Through Synaptic Intelligence.

In-Context Collapse in Vision-Language Models and How to Mitigate it? Continual Learning Through Synaptic Intelligence

Reference 56

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This paper cites Investigating the Catastrophic Forgetting in Multimodal Large Language Models.

In-Context Collapse in Vision-Language Models and How to Mitigate it? Investigating the Catastrophic Forgetting in Multimodal Large Language Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T15:05:19.412336Z

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Observation f7107f3b-8393-4377-8b80-5e22068c8aeb · outbound

This paper cites What Makes Good Examples for Visual In-Context Learning?.

In-Context Collapse in Vision-Language Models and How to Mitigate it? What Makes Good Examples for Visual In-Context Learning?

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T15:05:19.416727Z

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Observation d6fceeff-5317-41b8-a511-74d77b603445 · outbound

This paper cites Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models.

In-Context Collapse in Vision-Language Models and How to Mitigate it? Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T15:05:19.421206Z

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Observation 1cdba336-9c2b-4911-8a76-217d3b4bc1ff · outbound

This paper cites In: International Conference on Learning Representations (ICLR) 46.

In-Context Collapse in Vision-Language Models and How to Mitigate it? In: International Conference on Learning Representations (ICLR) 46

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:05:20.153010Z

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

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