Under fixed innovation coupling, finite-horizon optimizers admit minimal pathwise realizations and incidence-identifiable Möbius effects, with a five-term readout transfer from hidden relaxation and a closed reduced-value factorial experiment.
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In LVLMs, attention can be replaced by random Gaussian weights with little or no performance loss, indicating that current models get lost in attention rather than efficiently using visual context.
Sufficient conditions are provided for exact recovery of target mean and correlation matrix via forward KL and α-divergences in VI, without requiring log-concavity of the target.
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Causal Optimizer Interaction Calculus: Hidden Geometric Relaxation and Identifiable Interventions
Under fixed innovation coupling, finite-horizon optimizers admit minimal pathwise realizations and incidence-identifiable Möbius effects, with a five-term readout transfer from hidden relaxation and a closed reduced-value factorial experiment.
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Large Vision-Language Models Get Lost in Attention
In LVLMs, attention can be replaced by random Gaussian weights with little or no performance loss, indicating that current models get lost in attention rather than efficiently using visual context.
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Even More Guarantees for Variational Inference in the Presence of Symmetries
Sufficient conditions are provided for exact recovery of target mean and correlation matrix via forward KL and α-divergences in VI, without requiring log-concavity of the target.