Info Theory

The paper proposes viewing national AI development, specifically in France, as a 'national AI learning system' governed by a controlled balance between information injection and entropy dissipation, a…

This paper presents the 2026 Algorithmic Information Theory Data Compression Challenge, evaluating lossless compressors under realistic constraints and revealing performance dependencies.

The paper characterizes the secure rate-distortion-perception (RDP) trade-off region for neural image compression over various noisy and noiseless channels, demonstrating that randomized distributed f…

STaR-KV introduces a novel, training-free KV cache compression framework that adaptively re-weights token importance across spatial, temporal, and distributional axes, significantly reducing GPU memor…

The authors introduce a quantum empirical operator and use it to prove a universal achievability result for composite quantum hypothesis testing.

The paper establishes an information-theoretic upper bound on the combined functional capacity and perturbation retention of code LLMs, quantifying the security budget available for code generation.

The paper proposes an efficient and provably secure linguistic steganography method using range coding that achieves high embedding capacity and speed, outperforming existing methods.

This paper derives the Cramer-Rao lower bound for target state estimation in a distributed radar sensing system, revealing a tradeoff between update rate and quantization fidelity.

This paper proposes a statistical framework for designing task-aware multiple-input multiple-output (MIMO) precoders in future wireless networks using statistical channel state information and trainin…

The paper proposes a novel information-geometric framework to analyze LLM stability by integrating task utility, external entropy, and internal structural proxies, showing this composite score improve…

This paper presents a product coding scheme that converts bit-level reliability into block-level reliability, achieving the same asymptotic rate.

This paper provides the first systematic threat analysis of State-Space Models (SSMs) in safety-critical applications, introducing novel attack classes and formal metrics to quantify their security an…

This paper proposes a framework for compositional semantic communication (CSC) in physical AI systems, enabling heterogeneous devices to transmit semantic representations that compose meaningfully at…

The paper establishes a theoretical information-theoretic bound proving that for Vision-Language-Action (VLA) models, capability and robustness cannot both be arbitrarily high, quantifying the trade-o…

This paper proposes an economic model to optimize the trade-off between entanglement distribution latency and decoherence time in distributed quantum computing.

InfoMerge is a novel, training-free method that significantly compresses visual tokens for Video-LLMs by estimating temporal redundancy and allocating tokens based on content richness, achieving high…

This paper compares the preservation of linguistic content in different text encodings (tokens, bytes, pixels) using a shared bottleneck, revealing their distinct strengths in surface form preservatio…

The paper proposes a provably secure steganography scheme based on list decoding that significantly increases embedding capacity for Large Language Models (LLMs) compared to existing methods.

This paper proposes methods to improve the encoding capacity and disentanglement of Variational Autoencoders (VAE) by imposing entropy-based constraints and a weight-filter method.

The paper proposes a dual-interventional framework to characterize how linguistic structures and contextual cues influence LLMs' spatial reasoning for navigation, finding that topological information…