Paper Citation Record · LEDGER

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

As of 22 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 8 inbound Pith citation observations for arXiv:2411.17491.

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

pith.paper-citation-record.v1
2411.17491 v1

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T12:08:51.495834Z

measured 62 of 62 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:05:19.272138Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-08T05:54:33.575217Z

Reference resolution

54 of 54 outbound references displayed

verified exact0
verified fuzzy14
unresolved40
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation de162673-5c57-4daa-9aa8-86f1473d7bd1 · outbound

This paper cites GPT-4 Technical Report.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models GPT-4 Technical Report

Reference 1

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Observation b5b22506-24a0-4e0f-a8ac-5e58e5b63171 · outbound

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

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Flamingo: a visual language model for few-shot learning

Reference 2

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Observation 087802d2-6fab-4b70-b3da-6d7ddc6b265f · outbound

This paper cites Hallucination of Multimodal Large Language Models: A Survey.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Hallucination of Multimodal Large Language Models: A Survey

Reference 3

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Observation c5281822-b368-42a7-a429-9a55a42a0703 · outbound

This paper cites Understanding Information Storage and Transfer in Multi-modal Large Language Models.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Understanding Information Storage and Transfer in Multi-modal Large Language Models

Reference 4

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Observation beb9b1f7-2603-43fe-9f23-28c01888eb24 · outbound

This paper cites Generic attention- model explainability for interpreting bi-modal and encoder- decoder transformers.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Generic attention- model explainability for interpreting bi-modal and encoder- decoder transformers

Reference 5

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Observation 892eb2f5-f6e5-4c20-85a2-2f1fd3b6fbf7 · outbound

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

What's in the Image? A Deep-Dive into the Vision of Vision Language Models How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

Reference 6

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Observation 2c5e41ca-7797-44e9-9092-f701b65660a5 · outbound

This paper cites InternVL: Scaling up vision foundation models and aligning for generic visual-linguistic tasks.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models InternVL: Scaling up vision foundation models and aligning for generic visual-linguistic tasks

Reference 7

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Observation af800eb7-bc7a-49ae-ad87-0c72d78b3f4c · outbound

This paper cites What Does BERT Look At? An Analysis of BERT's Attention.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models What Does BERT Look At? An Analysis of BERT's Attention

Reference 8

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Observation 1c8bde47-d19b-45bd-9b00-ac98c8ef9461 · outbound

This paper cites Towards automated circuit discovery for mechanistic interpretability.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Towards automated circuit discovery for mechanistic interpretability

Reference 9

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Observation d6ffd854-c6f7-4f43-8c74-f458c8a0410a · outbound

This paper cites The Llama 3 Herd of Models.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models The Llama 3 Herd of Models

Reference 10

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Observation 11c432f5-df61-4d2d-8652-c4014c85c10b · outbound

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

What's in the Image? A Deep-Dive into the Vision of Vision Language Models MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 11

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Observation 762f1bae-143f-4988-b852-fe6dfba05150 · outbound

This paper cites Transformer Feed-Forward Layers Are Key-Value Memories.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Transformer Feed-Forward Layers Are Key-Value Memories

Reference 12

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Observation 69994d22-d342-4955-87e9-fc3bb30f59b0 · outbound

This paper cites Dissecting Recall of Factual Associations in Auto-Regressive Language Models.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Dissecting Recall of Factual Associations in Auto-Regressive Language Models

Reference 13

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Observation e7439443-0c2a-4d36-8655-d036e87cd50b · outbound

This paper cites Chat- gpt outperforms crowd workers for text-annotation tasks.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Chat- gpt outperforms crowd workers for text-annotation tasks

Reference 14

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Observation a6fe1baa-c504-4b3e-b6e3-a84e9024d6cb · outbound

This paper cites The Unreasonable Ineffectiveness of the Deeper Layers.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models The Unreasonable Ineffectiveness of the Deeper Layers

Reference 15

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Observation 36b2e311-9696-4ac9-b50c-540d6696c5be · outbound

This paper cites Conceptgraphs: Open-vocabulary 3d scene graphs for per- ception and planning.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Conceptgraphs: Open-vocabulary 3d scene graphs for per- ception and planning

Reference 16

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Observation 5ee653b1-5446-478e-86a5-0f086855a170 · outbound

This paper cites Is chatgpt better than human annotators? potential and limitations of chatgpt in explaining implicit hate speech.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Is chatgpt better than human annotators? potential and limitations of chatgpt in explaining implicit hate speech

Reference 17

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Observation 3d0eefc8-f3f7-4c04-9799-4d0f9b678951 · outbound

This paper cites Mistral 7B.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Mistral 7B

Reference 18

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Observation 23d4f89a-e093-4483-952c-f7d56133c2f8 · outbound

This paper cites Interpreting and Editing Vision-Language Representations to Mitigate Hallucinations.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Interpreting and Editing Vision-Language Representations to Mitigate Hallucinations

Reference 19

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Observation f30c3830-d93c-40f3-9e8d-a8f0597fa5d2 · outbound

This paper cites Segment any- thing.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Segment any- thing

Reference 20

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Observation 0d8990f3-234b-4afc-95c1-294ea1ca8b52 · outbound

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

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 21

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Observation 1b0f2264-ffa6-4b98-8eba-dfb359cab21d · outbound

This paper cites LLM-grounded Video Diffusion Models.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models LLM-grounded Video Diffusion Models

Reference 22

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Observation 4d141e33-a30a-4f55-a70a-1c097ee82b25 · outbound

This paper cites Microsoft coco: Common objects in context.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Microsoft coco: Common objects in context

Reference 23

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Observation 9accea1c-b706-46d7-8a4e-b15e7e7dcba2 · outbound

This paper cites Improved baselines with visual instruction tuning.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Improved baselines with visual instruction tuning

Reference 24

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Observation fe2364bd-9b3a-4fa7-a5c8-5f8c273d67e4 · outbound

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What's in the Image? A Deep-Dive into the Vision of Vision Language Models Visual instruction tuning

Reference 25

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This paper cites Clip-driven universal model for organ segmentation and tumor detection.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Clip-driven universal model for organ segmentation and tumor detection

Reference 26

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This paper cites OK-Robot: What Really Matters in Integrating Open-Knowledge Models for Robotics.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models OK-Robot: What Really Matters in Integrating Open-Knowledge Models for Robotics

Reference 27

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This paper cites Locating and editing factual associations in gpt.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Locating and editing factual associations in gpt

Reference 28

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This paper cites Towards Interpreting Visual Information Processing in Vision-Language Models.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Towards Interpreting Visual Information Processing in Vision-Language Models

Reference 29

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This paper cites Interpreting gpt: The logit lens.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Interpreting gpt: The logit lens

Reference 30

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Observation b41ac9f2-1cee-4e82-b4c6-0217cac9eb25 · outbound

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

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Learning transferable visual models from natural language supervi- sion

Reference 31

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This paper cites A multimodal automated interpretability agent.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models A multimodal automated interpretability agent

Reference 32

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This paper cites ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation

Reference 33

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This paper cites DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models

Reference 34

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

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs

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

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Eyes wide shut? exploring the visual shortcomings of multimodal llms

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This paper cites Chain-of-thought prompting elicits reasoning in large lan- guage models.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Chain-of-thought prompting elicits reasoning in large lan- guage models

Reference 37

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This paper cites Qwen2 Technical Report.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Qwen2 Technical Report

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This paper cites EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model

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This paper cites Yes” and “No.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Yes” and “No

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This paper cites Detect only tangible objects that can be interacted with.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models Detect only tangible objects that can be interacted with

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What's in the Image? A Deep-Dive into the Vision of Vision Language Models Unresolved cited work

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This paper cites If half of the physical objects in the predicted caption are also in the groundtruth caption, the precision would be 0.5.

What's in the Image? A Deep-Dive into the Vision of Vision Language Models If half of the *physical objects* in the predicted caption are also in the groundtruth caption, the precision would be 0.5

Reference 43

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What's in the Image? A Deep-Dive into the Vision of Vision Language Models fine detail

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What's in the Image? A Deep-Dive into the Vision of Vision Language Models Please answer yes or no

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:08:51.629549Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation b7d78133-b0b0-44da-9a83-3dc8da27fa44 · inbound

Investigating Mechanisms for In-Context Vision Language Binding cites this paper.

Investigating Mechanisms for In-Context Vision Language Binding What's in the Image? A Deep-Dive into the Vision of Vision Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T13:20:29.226330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd8974b9-4d49-436a-8611-a22b476e4d89 · inbound

PUMA: Layer-Pruned Language Model for Efficient Unified Multimodal Retrieval with Modality-Adaptive Learning cites this paper.

PUMA: Layer-Pruned Language Model for Efficient Unified Multimodal Retrieval with Modality-Adaptive Learning What's in the Image? A Deep-Dive into the Vision of Vision Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T18:39:01.636238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42272f09-424f-4567-976e-8fde399d364d · inbound

Scale Your Instructions: Enhance the Instruction-Following Fidelity of Unified Image Generation Model by Self-Adaptive Attention Scaling cites this paper.

Scale Your Instructions: Enhance the Instruction-Following Fidelity of Unified Image Generation Model by Self-Adaptive Attention Scaling What's in the Image? A Deep-Dive into the Vision of Vision Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T15:19:23.993702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fdb07771-8a63-4754-bea1-7ffeaf987dc0 · inbound

Counting to Four is still a Chore for VLMs cites this paper.

Counting to Four is still a Chore for VLMs What's in the Image? A Deep-Dive into the Vision of Vision Language Models

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:06:03.297518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c3bcf70c-4763-4c29-890f-db5f2eb90ecc · inbound

Vision-Default, Prior-Override: Causal Mechanisms of Perception-Knowledge Conflict in Vision-Language Models cites this paper.

Vision-Default, Prior-Override: Causal Mechanisms of Perception-Knowledge Conflict in Vision-Language Models What's in the Image? A Deep-Dive into the Vision of Vision Language Models

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T17:15:52.050405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f099b476-2c82-4664-bae0-8de9a7610a42 · inbound

Analysis-by-Proxy: Localization Signals in VLMs Operating as Condition Encoders cites this paper.

Analysis-by-Proxy: Localization Signals in VLMs Operating as Condition Encoders What's in the Image? A Deep-Dive into the Vision of Vision Language Models

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-07-08T05:54:33.577235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f90f76b3-eed1-4a3b-a13a-947ff61d0e32 · inbound

In-Context Collapse in Vision-Language Models and How to Mitigate it? cites this paper.

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

Resolution: unresolved; no resolver link, observed 2026-08-15T15:05:19.272138Z

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Observation 72836731-785e-4f17-8230-0bacb9b81695 · inbound

Routing Is Least Learnable Where It Is Most Valuable: Bounds on Representation Routing for Web Agents cites this paper.

Routing Is Least Learnable Where It Is Most Valuable: Bounds on Representation Routing for Web Agents What's in the Image? A Deep-Dive into the Vision of Vision Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:52.051427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.