mlsanityData to 14 Oct 2026

VisualScratchpad: Inference-time Visual Concepts Analysis in Vision Language Models

arXiv 2603.07335 ↗First posted 7 Mar 2026cs.AIcited by 1

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

Abstract (arXiv)

High-performing vision language models still produce incorrect answers, yet their failure modes are often difficult to explain. To make model internals more accessible and enable systematic debugging, we introduce VisualScratchpad, an interactive interface for visual concept analysis during inference. We apply sparse autoencoders to the vision encoder and link the resulting visual concepts to text tokens via text-to-image attention, allowing us to examine which visual concepts are both captured by the vision encoder and utilized by the language model. VisualScratchpad also provides a token-latent heatmap view that suggests a sufficient set of latents for effective concept ablation in causal analysis. Through case studies, we reveal three underexplored failure modes: limited cross-modal alignment, misleading visual concepts, and unused hidden cues. Project page: https://hyesulim.github.io/visual_scratchpad_projectpage/

Key facts. Cited by 1 paper 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 23 of its 30 references that are in the map. First posted on arXiv 7 Mar 2026.

Builds on

23 of 30 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025VL-SAE: Interpreting and Enhancing Vision-Language Alignment with a Unified Concept Set24 Oct

Other references · 22

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2024The Llama 3 Herd of Modelscited by 14,715
  3. 2023Visual Instruction Tuningcited by 7,869
  4. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  5. 2025Qwen2.5-VL Technical Reportcited by 5,587
  6. 2023InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Taskscited by 2,565
  7. 2023Sparse Autoencoders Find Highly Interpretable Features in Language Modelscited by 1,420
  8. 2022Toy Models of Superpositioncited by 971
  9. 2024Gemma: Open Models Based on Gemini Research and Technologycited by 916
  10. 2024Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMscited by 735
  11. 2024Mechanistic Interpretability for AI Safety -- A Reviewcited by 422
  12. 2025Open Problems in Mechanistic Interpretabilitycited by 179
10 more
  1. 2024A is for Absorption: Studying Feature Splitting and Absorption in Sparse Autoencoderscited by 160
  2. 2024BatchTopK Sparse Autoencoderscited by 130
  3. 2025Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Modelscited by 73
  4. 2024Sparse autoencoders reveal selective remapping of visual concepts during adaptationcited by 68
  5. 2017NSML: A Machine Learning Platform That Enables You to Focus on Your Modelscited by 34
  6. 2025SAE-V: Interpreting Multimodal Models for Enhanced Alignmentcited by 23
  7. 2025CytoSAE: Interpretable Cell Embeddings for Hematologycited by 8
  8. 2025ConceptScope: Characterizing Dataset Bias via Disentangled Visual Conceptscited by 5
  9. 2026CFM: Language-aligned Concept Foundation Model for Visioncited by 4
  10. 2025ConceptViz: A Visual Analytics Approach for Exploring Concepts in Large Language Modelscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Grounding Isn't Knowing: Do VLMs Need Object Localization for Spatial Reasoning?cited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2026Vision Inference Former: Sustaining Visual Consistency in Multimodal Large Language Models
  2. 2025VScan: Rethinking Visual Token Reduction for Efficient Large Vision-Language Models
  3. 2025Diagnosing Visual Reasoning: Challenges, Insights, and a Path Forward
  4. 2026Extraction and Analysis of Multimodal Concepts in Vision Language Models through Sparse Autoencoders
  5. 2026What Do Visual Tokens Really Encode? Uncovering Sparsity and Redundancy in Multimodal Large Language Models
  6. 2023Synthesize, Diagnose, and Optimize: Towards Fine-Grained Vision-Language Understanding
  7. 2026Show Me Examples: Inferring Visual Concepts from Image Sets
  8. 2025V-SEAM: Visual Semantic Editing and Attention Modulating for Causal Interpretability of Vision-Language Models