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Most visual computation in VLMs is redundant, allowing for a 55% FLOP reduction with minimal accuracy loss by simply bypassing token-specific projections in early and late layers.
Instead of trusting black-box scalar scores to evaluate world models, video verification can match end-to-end VLM recall while producing fully auditable, spatiotemporally grounded physical proofs of failure.
Long-tail semantic failures in document understanding are exposed when models are forced to reason with a unified vocabulary of visual anchors rather than treating elements in isolation.
Achieving over 134% accuracy gains with just 1,000 trainable parameters reveals a game-changing approach to enhancing spatial reasoning in vision language models.
SeClaw reveals that existing benchmarks fall short in capturing the complexities of agent behavior, enabling a more nuanced evaluation of security risks in autonomous systems.