Epistemic Uncertainty for Test-Time Discovery

arXiv:2605.11328v1 Announce Type: cross Abstract: Automated scientific discovery using large language models relies on identifying genuinely novel solutions. Standard reinforcement learning penalizes high-variance mutations, which

Interpretability Can Be Actionable

arXiv:2605.11161v1 Announce Type: cross Abstract: Interpretability aims to explain the behavior of deep neural networks. Despite rapid growth, there is mounting concern that much of

RT-Transformer: The Transformer Block as a Spherical State Estimator

arXiv:2605.11007v1 Announce Type: cross
Abstract: We show that the core components of the Transformer block — attention, residual connections, and normalization — arise naturally from a single geometric estimation problem. Modeling the latent state as a direction on the hypersphere, with noise defined in the tangent plane at the current estimate, yields a precision-weighted directional inference procedure in which attention aggregates evidence, residual connections implement incremental state updates, and normalization retracts the updated state back onto the hypersphere. Together, these components follow from the geometry of the estimation problem rather than being introduced as independent architectural choices.

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