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#information-theory

4 curated events
papersTODAY 04:00 UTC

Paper Links Zero-SNR Analyticity of Scalar MMSE to Gaussian Inputs

The paper studies a scalar channel where a real random variable X is observed under additive standard Gaussian noise scaled by the square root of a signal-to-noise parameter. Assuming a square-exponential moment condition on X, the author shows that the minimum mean-square error behaves analytically near zero SNR only when X is Gaussian, making that property equivalent to Gaussianity of the input. The result gives a way to detect non-Gaussian structure from the low-SNR behavior of the MMSE curve.

information-theory/estimation/gaussian/arxiv
papersSEP 10 04:00 UTC

Non-Stationarity Breaks Permutation Surrogates in Multi-Agent Reinforcement Learning

A new arXiv paper examines permutation surrogate tests, a common tool for estimating directed influence between reinforcement learning agents, by validating them against known ground truth. In two multi-agent settings, a social dilemma and a coordination race, the authors find that non-stationarity in agent behavior undermines these surrogate methods. The study provides diagnostics and corrective approaches to make information-theoretic influence measures more reliable.

multi-agent/reinforcement-learning/information-theory/evaluation
papersSEP 12 04:00 UTC

arXiv Paper Proposes Pragmatic Information Theory Linking Communication and Control

A new arXiv preprint outlines a framework called pragmatic information theory that aims to connect communication, control, and decision-making under one account. Its central concept, the isoteleia mapping, treats different semantic paths as equivalent when they lead to the same optimal action, a property the author describes as equifinality. The work is a cross-listed submission and is presented as a mathematical treatment rather than an applied system.

information-theory/research/arxiv/decision-making
papersSEP 12 04:00 UTC

Benchmarking Information-Theoretic Metrics for Multimodal Time-Series Forecasting

A new arXiv paper examines how to measure whether text annotations actually improve a forecasting model's predictions when paired with time-series data. The authors propose benchmarking information-theoretic metrics intended to quantify how much a given text input contributes to forecast accuracy. The study targets multimodal forecasting pipelines that blend numerical series with textual context.

benchmarks/multimodal/time-series/information-theory