Attention sharpness barely predicts VLM correctness while hidden-state probes and self-consistency strongly do, with late-fusion models showing fragile reliability bottlenecks unlike early-fusion ones.
Visual instruction tuning
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FastOCR dynamically selects a small subset of visual tokens per decoding step using focal-guided pruning and cross-step reuse, retaining 98% accuracy on Qwen2.5-VL while attending to only 5% of tokens and cutting attention latency by 3x.
citing papers explorer
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Where Reliability Lives in Vision-Language Models: A Mechanistic Study of Attention, Hidden States, and Causal Circuits
Attention sharpness barely predicts VLM correctness while hidden-state probes and self-consistency strongly do, with late-fusion models showing fragile reliability bottlenecks unlike early-fusion ones.
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FastOCR: Dynamic Visual Fixation via KV Cache Pruning for Efficient Document Parsing
FastOCR dynamically selects a small subset of visual tokens per decoding step using focal-guided pruning and cross-step reuse, retaining 98% accuracy on Qwen2.5-VL while attending to only 5% of tokens and cutting attention latency by 3x.