mlsanityData to 14 Oct 2026

Investigating Mechanisms for In-Context Vision Language Binding

arXiv 2505.22200 ↗First posted 28 May 2025cs.CV cs.AIcited by 5

On the map: Multimodal LLMs / vision-language models → VLM benchmarks and visual reasoning

Abstract (arXiv)

To understand a prompt, Vision-Language models (VLMs) must perceive the image, comprehend the text, and build associations within and across both modalities. For instance, given an 'image of a red toy car', the model should associate this image to phrases like 'car', 'red toy', 'red object', etc. Feng and Steinhardt propose the Binding ID mechanism in LLMs, suggesting that the entity and its corresponding attribute tokens share a Binding ID in the model activations. We investigate this for image-text binding in VLMs using a synthetic dataset and task that requires models to associate 3D objects in an image with their descriptions in the text. Our experiments demonstrate that VLMs assign a distinct Binding ID to an object's image tokens and its textual references, enabling in-context association.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 16 of its 18 references that are in the map. First posted on arXiv 28 May 2025.

Builds on

16 of 18 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024What's in the Image? A Deep-Dive into the Vision of Vision Language Models26 Nov
  2. 2024Towards Interpreting Visual Information Processing in Vision-Language Models9 Oct
  3. 2024Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models25 Sep

Other references · 13

  1. 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,703
  2. 2024GPT-4o System Cardcited by 4,588
  3. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,234
  4. 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,224
  5. 2023Sigmoid Loss for Language Image Pre-Trainingcited by 3,109
  6. 2024LLaVA-OneVision: Easy Visual Task Transfercited by 2,719
  7. 2024Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Trackingcited by 149
  8. 2023How do Language Models Bind Entities in Context?cited by 108
  9. 2024Interpreting and Editing Vision-Language Representations to Mitigate Hallucinationscited by 104
  10. 2023Entity Tracking in Language Modelscited by 44
  11. 2024Monitoring Latent World States in Language Models with Propositional Probescited by 43
  12. 2021MuMuQA: Multimedia Multi-Hop News Question Answering via Cross-Media Knowledge Extraction and Groundingcited by 19
1 more
  1. 2023VTQA: Visual Text Question Answering via Entity Alignment and Cross-Media Reasoningcited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2025Uncovering Grounding IDs: How External Cues Shape Multimodal Bindingcited by 4
  2. 2026Seeing to Generalize: How Visual Data Corrects Binding Shortcutscited by 4
  3. 2026The Dual Mechanisms of Spatial Variable Binding in Vision-Language Modelscited by 4
  4. 2026Diagnosing Dense Same-Class Attribute Misbinding in Large Vision-Language Modelscited by 1
  5. 2026Vision-Language Models are Fragile Multilingual Associatorscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Understanding the Limits of Vision Language Models Through the Lens of the Binding Problem
  2. 2025Can VLMs Recall Factual Associations From Visual References?
  3. 2025Visual symbolic mechanisms: Emergent symbol processing in vision language models
  4. 2025Vision language models have difficulty recognizing virtual objects
  5. 2026VLMs Need Words: Vision Language Models Ignore Visual Detail In Favor of Semantic Anchors
  6. 2025Object-centric Binding in Contrastive Language-Image Pretraining
  7. 2025VLM2-Bench: A Closer Look at How Well VLMs Implicitly Link Explicit Matching Visual Cues
  8. 2025V-SEAM: Visual Semantic Editing and Attention Modulating for Causal Interpretability of Vision-Language Models