Why MLLMs Struggle to Count: Overcoming Individuation and Aggregation Bottlenecks with ConvStack

arXiv:2609.37096 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-10-02

Terminology

• Accuracy — 48× in this paper · explained in 1 episode
• Benchmark — 12× in this paper · explained in 2 episodes
• Aggregation — 11× in this paper · explained in 1 episode
• Transformer — 10× in this paper · explained in 2 episodes
• Adaptation — 7× in this paper · explained in 1 episode
• Grounding — 7× in this paper · explained in 3 episodes
• Embeddings — 6× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 4× in this paper
• Ablation study — 3× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Feature Extraction — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Hallucination — 2× in this paper · explained in 3 episodes
• Spatial Aptitude Training — 2× in this paper
• Visual Grounding — 2× in this paper · explained in 2 episodes
• Additivity — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Hybrid Architectures — 1× in this paper · explained in 3 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Mechanistic interpretability — 1× in this paper · explained in 2 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes
• Vision-language models — 1× in this paper · explained in 2 episodes

Sources

• Qwen3-VL Technical Report
• InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
• Understanding Counting Mechanisms in Large Language and Vision-Language Models
• Counting Circuits: Mechanistic Interpretability of Visual Reasoning in Large Vision-Language Models
• Your Vision-Language Model Can't Even Count to 20: Exposing the Failures of VLMs in Compositional Counting
• Convolutional Bypasses Are Better Vision Transformer Adapters
• Spatial-MLLM: Boosting MLLM Capabilities in Visual-based Spatial Intelligence
• PaliGemma: A versatile 3B VLM for transfer
• SAT: Dynamic Spatial Aptitude Training for Multimodal Language Models

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