QKLA achieves quadratic query-complexity improvement for clipped KL estimation, yielding 2.7-7.4x fewer oracle queries than classical methods when embedded in the PC causal-discovery algorithm at moderate precision.
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Collision fiber sizes determine precise zero-error compression bounds and rate-distortion laws for semantic identity, establishing symbolic mechanisms as necessary complements to non-injective neural representations.
Three frameworks adapt foundation models for generalized category discovery under domain shifts via disentanglement and prompt tuning, showing gains on synthetic and real multi-domain data.
The work derives an approximate local secrecy capacity and defines secret local contraction coefficients as largest generalized eigenvalues of channel matrix pencils, obtained via local Euclidean geometry approximations to the wiretap channel optimization problem.
citing papers explorer
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Quantum Causal Discovery via Amplitude Estimation of Kullback-Leibler Divergence
QKLA achieves quadratic query-complexity improvement for clipped KL estimation, yielding 2.7-7.4x fewer oracle queries than classical methods when embedded in the PC causal-discovery algorithm at moderate precision.
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Generalized Category Discovery under Domain Shifts: From Vision to Vision-Language Models
Three frameworks adapt foundation models for generalized category discovery under domain shifts via disentanglement and prompt tuning, showing gains on synthetic and real multi-domain data.
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Local Information-Theoretic Security via Euclidean Geometry
The work derives an approximate local secrecy capacity and defines secret local contraction coefficients as largest generalized eigenvalues of channel matrix pencils, obtained via local Euclidean geometry approximations to the wiretap channel optimization problem.