Paper Citation Record · LEDGER

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models

As of 12 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 5 inbound Pith citation observations for arXiv:2506.07936.

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

pith.paper-citation-record.v1
2506.07936 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:25:30.497174Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:43:16.986690Z

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

55 of 55 outbound references displayed

verified exact2
verified fuzzy11
unresolved41
parse uncertain0
malformed identifier1
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External citation measurements

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

Outbound references

Observation b5eba608-dd3f-43d8-809a-2737621f1a43 · outbound

This paper cites OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T05:25:30.326469Z

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Observation 6ac019dc-ebe7-4ccd-93b3-2005b827e975 · outbound

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

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T05:25:30.330578Z

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Observation 0206e29a-924e-436e-8dc7-d959a0587dfe · outbound

This paper cites Qwen2.5-VL Technical Report.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Qwen2.5-VL Technical Report

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T05:25:30.333944Z

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Observation 66e436cd-5e95-4931-ae50-c3bd46f26429 · outbound

This paper cites What makes multimodal in-context learning work? InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1539–1550, 2024.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models What makes multimodal in-context learning work? InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1539–1550, 2024

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T05:25:30.337467Z

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Observation 1b60879c-f6ec-499a-a516-da1b4fdfefe6 · outbound

This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T05:25:30.340707Z

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Observation 5cfa213e-4e85-4600-8e5a-d68347522cd7 · outbound

This paper cites M$^3$CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models M$^3$CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T05:25:30.344139Z

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Observation 0a990664-95bc-4ed7-9107-548f6eb72e66 · outbound

This paper cites Can Multimodal Large Language Models Truly Perform Multimodal In-Context Learning?.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Can Multimodal Large Language Models Truly Perform Multimodal In-Context Learning?

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T05:25:30.347857Z

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Observation d7134849-2ec4-4fe9-9076-ec3d3ab643e4 · outbound

This paper cites Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T05:25:30.352098Z

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Unavailable: canonical work link unavailable.

Observation b3be597a-86b0-4857-a4ed-2e67d0c0c170 · outbound

This paper cites A Survey on In-context Learning.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models A Survey on In-context Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T05:25:30.355259Z

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Observation 3124b6bc-7b85-49ed-8d4a-6cc563b1ffcb · outbound

This paper cites Vlmevalkit: An open-source toolkit for evaluating large multi-modality models.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Vlmevalkit: An open-source toolkit for evaluating large multi-modality models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T05:25:30.358643Z

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Observation 79eb36fa-ddc5-4e7c-9127-09243a67138f · outbound

This paper cites Interleaved-Modal Chain-of-Thought.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Interleaved-Modal Chain-of-Thought

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T05:25:30.361398Z

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Observation caf709a0-2700-4a83-a67d-e1ac8b44295c · outbound

This paper cites Innate Reasoning is Not Enough: In-Context Learning Enhances Reasoning Large Language Models with Less Overthinking.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Innate Reasoning is Not Enough: In-Context Learning Enhances Reasoning Large Language Models with Less Overthinking

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T05:25:30.364635Z

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Observation 58541b15-d921-4064-b89d-6dee79b1e6b8 · outbound

This paper cites Can MLLMs Reason in Multimodality? EMMA: An Enhanced MultiModal ReAsoning Benchmark.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Can MLLMs Reason in Multimodality? EMMA: An Enhanced MultiModal ReAsoning Benchmark

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T05:25:30.367946Z

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Observation cac6d4c7-64c9-4415-9f84-4ca332bb8d68 · outbound

This paper cites MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T05:25:30.371674Z

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Observation 725bc155-8341-46d3-a66d-1a6395d71782 · outbound

This paper cites Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T05:25:30.375814Z

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Observation be9d5f8a-08dc-47bc-a483-72007514d435 · outbound

This paper cites What matters when building vision-language models?.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models What matters when building vision-language models?

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T05:25:30.379020Z

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Observation a003c4e3-e160-4134-952d-a5187c342958 · outbound

This paper cites Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T05:25:30.381931Z

Source-reported events for the cited work

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Observation c9adec86-6858-4838-8f48-a51fcd794183 · outbound

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

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts

Reference 18

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Observation ee0fcb41-a038-4742-940b-4121a9091338 · outbound

This paper cites Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 19

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Observation 6903f7b2-2961-4c17-8107-a935e8baa3e8 · outbound

This paper cites Llama 3.2: Revolutionizing edge ai and vision with open, customizable models.https: //ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices/ , 2024.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Llama 3.2: Revolutionizing edge ai and vision with open, customizable models.https: //ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices/ , 2024

Reference 20

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Observation 1a023700-31db-4bd3-be62-7c1147e17792 · outbound

This paper cites Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation

Reference 21

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Observation f8ae2fe6-4446-47d9-9941-f825c3bfb4e9 · outbound

This paper cites Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages

Reference 22

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Observation 9d60bd3c-8794-4bbd-9635-e66877b7d1e4 · outbound

This paper cites What Factors Affect Multi-Modal In-Context Learning? An In-Depth Exploration.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models What Factors Affect Multi-Modal In-Context Learning? An In-Depth Exploration

Reference 23

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Observation 0d358e1e-a868-4533-be8e-b2a645030683 · outbound

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Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Unresolved cited work

Reference 24

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Observation cdd8f034-939e-43fc-bb23-0587c90b662e · outbound

This paper cites A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge

Reference 25

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This paper cites VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model

Reference 26

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Observation c79db2fd-71d1-418f-af04-e7c53c07e56f · outbound

This paper cites Towards vqa models that can read.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Towards vqa models that can read

Reference 27

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Observation 51a84144-6660-4ddd-8a42-896548d3a749 · outbound

This paper cites VL-Rethinker: Incentivizing Self-Reflection of Vision-Language Models with Reinforcement Learning.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models VL-Rethinker: Incentivizing Self-Reflection of Vision-Language Models with Reinforcement Learning

Reference 28

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Observation 9fef8f3e-b170-4db1-9add-e81f420a39da · outbound

This paper cites Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization

Reference 29

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This paper cites Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey

Reference 30

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This paper cites Emergent Abilities of Large Language Models.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Emergent Abilities of Large Language Models

Reference 31

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This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 32

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This paper cites Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering

Reference 33

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This paper cites Addressing Order Sensitivity of In-Context Demonstration Examples in Causal Language Models.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Addressing Order Sensitivity of In-Context Demonstration Examples in Causal Language Models

Reference 34

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Observation 0a3eec10-ee2b-4186-89bb-5b762e7a11a6 · outbound

This paper cites LLaVA-CoT: Let Vision Language Models Reason Step-by-Step.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models LLaVA-CoT: Let Vision Language Models Reason Step-by-Step

Reference 35

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This paper cites From Introspection to Best Practices: Principled Analysis of Demonstrations in Multimodal In-Context Learning.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models From Introspection to Best Practices: Principled Analysis of Demonstrations in Multimodal In-Context Learning

Reference 36

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This paper cites Formal Mathematical Reasoning: A New Frontier in AI.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Formal Mathematical Reasoning: A New Frontier in AI

Reference 37

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This paper cites An empirical study of gpt-3 for few-shot knowledge-based vqa.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models An empirical study of gpt-3 for few-shot knowledge-based vqa

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:31.031434Z

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This paper cites Automatic Chain of Thought Prompting in Large Language Models.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Automatic Chain of Thought Prompting in Large Language Models

Reference 39

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This paper cites Wong, and Simon See.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Wong, and Simon See

Reference 40

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This paper cites Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reference 41

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This paper cites VL-ICL Bench: The Devil in the Details of Multimodal In-Context Learning.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models VL-ICL Bench: The Devil in the Details of Multimodal In-Context Learning

Reference 42

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Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Can In-context Learners Learn a Reasoning Concept from Demonstrations?

Reference 43

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This paper cites Density is calculated as mass/volume.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Density is calculated as mass/volume

Reference 44

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Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Unresolved cited work

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Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Unresolved cited work

Reference 46

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This paper cites Final answer:.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Final answer:

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:30.996962Z

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This paper cites In the graphs, we need to compare the export value (top graph) with the import value (bottom graph) for each country in the year 2020.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models In the graphs, we need to compare the export value (top graph) with the import value (bottom graph) for each country in the year 2020

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:30.988293Z

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Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Export > Import, so Country 3 has a surplus

Reference 49

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Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:25:30.969273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2a9c27f2-c4f4-41c8-9844-8a492ad6ffd2 · outbound

This paper cites Final answer:.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Final answer:

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:30.959723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4c6fbc48-6bfe-4c30-bba2-6546a303e330 · outbound

This paper cites a² varies inversely with b³.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models a² varies inversely with b³

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:30.949945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3e46e54a-673e-47f6-a7ba-0ae533126ade · outbound

This paper cites Therefore, a² = 7² = 49.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Therefore, a² = 7² = 49

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:30.941221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e64c6f14-4372-42e7-b695-b4ca36a8aab3 · outbound

This paper cites When b = 6, a² = 1323 / 6³ = 1323 / 216 = 6.125.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models When b = 6, a² = 1323 / 6³ = 1323 / 216 = 6.125

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:30.932322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 54bdac12-e0f8-42b8-92ef-2625aef72dec · outbound

This paper cites an unresolved cited work.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:25:30.923432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

Observation adec5307-bff3-4d51-9fa7-21126ac3a9bb · inbound

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems cites this paper.

In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:16.986690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5ca7f5c-4333-4f1f-ac79-bab58917cdb4 · inbound

True Multimodal In-Context Learning Needs Attention to the Visual Context cites this paper.

True Multimodal In-Context Learning Needs Attention to the Visual Context Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T15:29:34.173300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84d6614a-312c-4db4-b403-35f50e49d566 · inbound

MetaEmbed: Scaling Multimodal Retrieval at Test-Time with Flexible Late Interaction cites this paper.

MetaEmbed: Scaling Multimodal Retrieval at Test-Time with Flexible Late Interaction Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-18T14:11:27.448460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 957de704-b9a9-4c56-983f-e25012054cec · inbound

Why Multimodal In-Context Learning Lags Behind? Unveiling the Inner Mechanisms and Bottlenecks cites this paper.

Why Multimodal In-Context Learning Lags Behind? Unveiling the Inner Mechanisms and Bottlenecks Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-10T13:45:28.245354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1dae7721-237e-4948-8945-f6b27325da46 · inbound

OralMLLM-Bench: Evaluating Cognitive Capabilities of Multimodal Large Language Models in Dental Practice cites this paper.

OralMLLM-Bench: Evaluating Cognitive Capabilities of Multimodal Large Language Models in Dental Practice Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:05:50.173877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.