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.
A tokenizer-free pixel embedding, position encoding, and 256-way head let frozen LLMs act as portable entropy models for lossless RGB compression across model families.
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.
Upper and lower bounds on capacity of quantized MIMO ISAC are tight at low SNR, saturate at high SNR due to quantization, and i.i.d. Gaussian signaling is near-optimal; closed-form LMMSE also saturates under Kronecker model.
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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Semantic Identity Compression: Zero-Error Laws, Rate-Distortion, and Neurosymbolic Necessity
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.
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LUMI: Tokenizer-Agnostic LLM-Based Lossless Image Compression
A tokenizer-free pixel embedding, position encoding, and 256-way head let frozen LLMs act as portable entropy models for lossless RGB compression across model families.
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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.
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Fundamental Limits of Quantized MIMO ISAC under Gaussian Signaling
Upper and lower bounds on capacity of quantized MIMO ISAC are tight at low SNR, saturate at high SNR due to quantization, and i.i.d. Gaussian signaling is near-optimal; closed-form LMMSE also saturates under Kronecker model.