mlsanityData to 14 Oct 2026

Uncovering Grounding IDs: How External Cues Shape Multimodal Binding

arXiv 2509.24072 ↗First posted 28 Sep 2025cs.CV cs.AIcited by 4

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

Abstract (arXiv)

Large vision-language models (LVLMs) perform well on multimodal tasks, but their ability to reason and precisely align visual and textual information still has room for improvement. In this study, we show that external visual cues, such as symbols or grid lines, help LVLMs form more accurate connections between visual components, such as objects, and their corresponding textual descriptions, improving their grounding and reasoning abilities. We introduce the concept of Grounding IDs, which are latent identifiers that arise within the model as a result of external cues structuring both visual and textual modalities. Our analysis reveals that partition-inducing external cues lead to Grounding IDs that make better alignment between corresponding visual and text representations, helping the model focus on relevant information. We find that Grounding IDs enhance attention between related components, improving cross-modal grounding and reducing hallucinations. Overall, our results show that Grounding IDs are a key mechanism that enables external cues to improve cross-modal alignment, reduce errors, and enhance the overall performance of LVLMs across a range of multimodal tasks.

Key facts. Cited by 4 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 29 of its 35 references that are in the map. First posted on arXiv 28 Sep 2025.

Builds on

29 of 35 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025Visual symbolic mechanisms: Emergent symbol processing in vision language models18 Jun
  2. 2025Investigating Mechanisms for In-Context Vision Language Binding28 May
  3. 2024Understanding the Limits of Vision Language Models Through the Lens of the Binding Problem31 Oct
  4. 2024Towards Interpreting Visual Information Processing in Vision-Language Models9 Oct
  5. 2024Understanding Information Storage and Transfer in Multi-modal Large Language Models6 Jun

Other references · 24

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  3. 2023Visual Instruction Tuningcited by 7,869
  4. 2024GPT-4o System Cardcited by 4,588
  5. 2023Improved Baselines with Visual Instruction Tuningcited by 4,418
  6. 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,796
  7. 2022Locating and Editing Factual Associations in GPTcited by 2,691
  8. 2023Evaluating Object Hallucination in Large Vision-Language Modelscited by 1,729
  9. 2019What Does BERT Look At? An Analysis of BERT's Attentioncited by 1,003
  10. 2023Towards Automated Circuit Discovery for Mechanistic Interpretabilitycited by 724
  11. 2023Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decodingcited by 688
  12. 2018Object Hallucination in Image Captioningcited by 669
12 more
  1. 2023Dissecting Recall of Factual Associations in Auto-Regressive Language Modelscited by 528
  2. 2023OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocationcited by 478
  3. 2024A Survey on Hallucination in Large Vision-Language Modelscited by 357
  4. 2021Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformerscited by 320
  5. 2024Multi-Modal Hallucination Control by Visual Information Groundingcited by 208
  6. 2024A Practical Review of Mechanistic Interpretability for Transformer-Based Language Modelscited by 118
  7. 2023How do Language Models Bind Entities in Context?cited by 108
  8. 2024Interpreting and Editing Vision-Language Representations to Mitigate Hallucinationscited by 104
  9. 2024Representational Analysis of Binding in Language Modelscited by 28
  10. 2025Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Modelscited by 22
  11. 2025Forgotten Polygons: Multimodal Large Language Models are Shape-Blindcited by 16
  12. 2025Visual Structures Helps Visual Reasoning: Addressing the Binding Problem in VLMscited by 15

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025Understanding Counting Mechanisms in Large Language and Vision-Language Modelscited by 4
  2. 2026The Dual Mechanisms of Spatial Variable Binding in Vision-Language Modelscited by 4
  3. 2026Who Says What: Symbolic Trimodal Binding Mechanisms in Audio-Visual LLMscited by 1
  4. 2026Improving Spatial-Temporal Reasoning in Video-Language Models with Structured Video Promptingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Mixture-of-Thought-Tokens: Unifying Perception and Reasoning for Free-form Multimodal Grounding
  2. 2025v1: Learning to Point Visual Tokens for Multimodal Grounded Reasoning
  3. 2026Beyond Language: Grounding Referring Expressions with Hand Pointing in Egocentric Vision
  4. 2025GroundingME: Exposing the Visual Grounding Gap in MLLMs through Multi-Dimensional Evaluation
  5. 2025Argus: Vision-Centric Reasoning with Grounded Chain-of-Thought
  6. 2025Towards Understanding Visual Grounding in Visual Language Models
  7. 2026Vision-Default, Prior-Override: Causal Mechanisms of Perception-Knowledge Conflict in Vision-Language Models
  8. 2026MM-OPD: Towards One More Bottleneck Between Perception and Reasoning