mlsanityData to 7 Oct 2026

Probing Perceptual Constancy in Large Vision-Language Models

arXiv 2502.10273 ↗First posted 14 Feb 2025cs.CV cs.AIcited by 7

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

Abstract (arXiv)

Perceptual constancy is the ability to maintain stable perceptions of objects despite changes in sensory input, such as variations in distance, angle, or lighting. This ability is crucial for visual understanding in a dynamic world. Here, we explored such ability in current Vision Language Models (VLMs). In this study, we evaluated 155 VLMs using 236 experiments across three domains: color, size, and shape constancy. The experiments included single-image and video adaptations of classic cognitive tasks, along with novel tasks in in-the-wild conditions. We found significant variability in VLM performance across these domains, with model performance in shape constancy clearly dissociated from that of color and size constancy.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 26 of its 51 references that are in the map. First posted on arXiv 14 Feb 2025.

Builds on

26 of 51 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024LLaVA-CoT: Let Vision Language Models Reason Step-by-Step15 Nov
  2. 2022Flamingo: a Visual Language Model for Few-Shot Learning29 Apr

Other references · 24

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2023GPT-4 Technical Reportcited by 18,132
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  5. 2023Visual Instruction Tuningcited by 7,792
  6. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,069
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  11. 2019LXMERT: Learning Cross-Modality Encoder Representations from Transformerscited by 1,548
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12 more
  1. 2024SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilitiescited by 679
  2. 2023Instruction Tuning with GPT-4cited by 676
  3. 2023V*: Guided Visual Search as a Core Mechanism in Multimodal LLMscited by 459
  4. 2018Human Action Recognition and Prediction: A Surveycited by 226
  5. 2024SpatialBot: Precise Spatial Understanding with Vision Language Modelscited by 146
  6. 2022BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learningcited by 56
  7. 2016Going Deeper into Action Recognition: A Surveycited by 55
  8. 2022Consistent-Teacher: Towards Reducing Inconsistent Pseudo-targets in Semi-supervised Object Detectioncited by 43
  9. 2024Core Knowledge Deficits in Multi-Modal Language Modelscited by 38
  10. 2021More than Encoder: Introducing Transformer Decoder to Upsamplecited by 26
  11. 2023Diverse Cotraining Makes Strong Semi-Supervised Segmentorcited by 26
  12. 2024From Pixels to Tokens: Byte-Pair Encoding on Quantized Visual Modalitiescited by 16

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 4.

7 papers.

  1. 2025The Philosophical Foundations of Growing AI Like A Childcited by 6
  2. 2025Video Models Start to Solve Chess, Maze, Sudoku, Mental Rotation, and Raven' Matricescited by 4
  3. 2026Vision Language Models Cannot Reason About Physical Transformationcited by 4
  4. 2025Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AIcited by 3
  5. 2026Egocentric Bias in Vision-Language Modelscited by 3
  6. 2026Deferred Exposure of Future Trajectories for Verifiable Reasoning in Autonomous Driving VLMscited by 0
  7. 2026Training Object Permanence in World Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025The Perceptual Observatory Characterizing Robustness and Grounding in MLLMs
  2. 2026Same Answer, Different Representations: Hidden instability in VLMs
  3. 2026Do VLMs Perceive or Recall? Probing Visual Perception vs. Memory with Classic Visual Illusions
  4. 2026Spatial Colour Mixing Illusions as a Perception Stress Test for Vision-Language Models
  5. 2026Visuals Lie, Consistency Speaks: Disentangling Spatial Attention from Reliability in Vision-Language Models
  6. 2026The Last Visible Pixel: Probing Fine-Scale Perception in Vision-Language Models
  7. 2025Mixed Signals: Decoding VLMs' Reasoning and Underlying Bias in Vision-Language Conflict
  8. 2025CoSpace: Benchmarking Continuous Space Perception Ability for Vision-Language Models