SearcharxivSearch

arXiv · 2605.29957

Universal thermokinetic decomposition of short-time information fluctuations

Abstract

Biological, artificial, and physical systems dissipate energy to accurately transmit information. While tools of information theory have been used to characterize information-processing capabilities, how reliably this information is acquired along individual trajectories, and which aspects require a thermodynamic cost, is an open question. In this work, we focus on the stochastic predictability of an arbitrary Langevin dynamics, defined as the pointwise mutual information between the current and future states of a system. We show that the fluctuations of predictability obey a universal thermokinetic decomposition at short times, which reveals that information fluctuations are suppressed by energy dissipation and become stronger with increased dynamical activity. Remarkably, we find that the average predictability, i.e., the short-time mutual information, does not carry any dependence on the underlying thermodynamic and kinetic features. Thus, the role of dissipation at short times is not to enhance information, but to reduce its fluctuations. Such dissipative control is effective only when instantiated by nonlinear operations. Moreover, energy consumption governs short- and long-time precision in stochastic oscillators through structurally different mechanisms that can be independently tuned. Our decomposition offers a fundamental thermodynamic basis for understanding the reliability of information transmission in nonequilibrium systems, the constraints on precision in biological systems, and the design of energy-limited control strategies.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Giorgio Nicoletti, Daniel M. Busiello. 2026-05-28. Universal thermokinetic decomposition of short-time information fluctuations. https://arxiv.org/abs/2605.29957

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Universal sampling of spin systems across quenched disorder

Statistical physics extracts macroscopic laws by averaging over the many microscopic degrees of freedom of a system. Disordered systems demand a second and far harder average, one over the quenched randomness itself. The classic analytical routes, the replica and cavity methods, become uncontrolled outside mean-field or tree-like limits, and conventional numerical algorithms like parallel tempering require expensive, independent equilibration for every disorder realization. In this work, we introduce a universal neural variational framework that amortizes inference across the disorder ensemble, eliminating both the need for per-instance Markov chain equilibration and the cost of retraining instance-specific variational ansatzes. Built on an encoder-decoder Transformer architecture, after training once, it produces an explicit approximation to the Boltzmann distribution given previously unseen disorder realizations without further optimization. We validate this framework on 2D Edwards-Anderson models, and apply it to the random-bond Ising model, successfully capturing the Binder cumulant crossings near the Nishimori multicritical point. These results shift the object of variational inference from the single instance to the disorder ensemble, opening a route to frustrated many-body systems where instance-by-instance computation is prohibitive.

cond-mat.stat-mech

Information-Theoretic Characterization of Macroscopic Chaos Emerging from the Chemical Master Equation

Open chemical reaction networks exhibit stochastic concentration dynamics at finite system sizes, whereas their macroscopic limit is governed by deterministic rate equations that can display chaos. In this Letter, we show theoretically that a rate of information loss constructed from two-time mutual information recovers the Kolmogorov-Sinai entropy in the deterministic limit. We verify this result through numerical simulations of a Markov jump process for a three-species system involving seven reactions.

cond-mat.stat-mech

Orientational order on non-orientable domains

We study the statistical properties of passive and active many-body systems with orientational degrees of freedom on non-orientable domains. By rephrasing topological constraints as non-local symmetry relations on an orientable double-cover, we show that non-orientability eliminates global rotational soft modes without acting like an external field. In a passive XY model, this results in topological caging, where orientational fluctuations that exhibit conventional diffusive behavior on a torus saturate on a Klein bottle to a finite value that we compute exactly in the thermodynamic limit. In models of active self-propelled particles with orientational degrees of freedom, topological caging persists despite continuously changing interaction neighborhoods. In an active Ising spin model, non-orientability enforces the coexistence of ordered anti-parallel domains with vanishing global polar order, a state that is absent on orientable domains.

cond-mat.stat-mech