Predictive Information Loss at Physical Model Interfaces: Sufficient State Transfer and Information-Retention Horizons

Coupled physical models can preserve conserved quantities and a current observable while discarding upstream information needed for future prediction. We formulate this loss relative to a shared information record, an additional record available to the sending module, and a specified future observation. Conditional mutual information gives the irreducible expected logarithmic-score loss of compression; a separate relative-entropy term measures downstream misspecification. A linear– Gaussian specialization yields a fixed-rank state-retention rule. A positive, energy-balanced threereservoir benchmark preserves total energy and instantaneous luminosity while hiding a coordinate that affects later emission. At the declared noise level, a nominal 95 % plug-in interval achieves 29.09 % ensemble coverage near the early loss maximum. Exact marginalization restores ensemble coverage at 3.50 times the width of the state-informed reference; transmitting the extra coordinate removes this interface loss only when it is already available upstream. A model-specific analytic bound guarantees a marginal information-retention interval of at least 0.02736 time units for a tolerance of 0.05 nats, compared with a numerically located first crossing at 0.02820. Joint-target calculations show why cancellation at one forecast time cannot certify an entire curve. Noise sensitivity and correlated-readout tests delimit the numerical claims. The results concern a controlled interface and do not infer an unknown source state or validate a real astrophysical event. Keywords: predictive sufficiency; model coupling; uncertainty quantification; information loss; adaptivecomputation; time-domain astrophysics.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-03
DOI
https://doi.org/10.5281/zenodo.23925805
Primary Topic
Advanced Thermodynamics and Statistical Mechanics
Type
article
Field-Weighted Citation Impact
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article

Predictive Information Loss at Physical Model Interfaces: Sufficient State Transfer and Information-Retention Horizons

Sergey Germanovich Kolesnyak
Zenodo (CERN European Organization for Nuclear Research)
Advanced Thermodynamics and Statistical Mechanics
article

Predictive Information Loss at Physical Model Interfaces: Sufficient State Transfer and Information-Retention Horizons

Sergey Germanovich Kolesnyak
article en

Abstract

Coupled physical models can preserve conserved quantities and a current observable while discarding upstream information needed for future prediction. We formulate this loss relative to a shared information record, an additional record available to the sending module, and a specified future observation. Conditional mutual information gives the irreducible expected logarithmic-score loss of compression; a separate relative-entropy term measures downstream misspecification. A linear– Gaussian specialization yields a fixed-rank state-retention rule. A positive, energy-balanced threereservoir benchmark preserves total energy and instantaneous luminosity while hiding a coordinate that affects later emission. At the declared noise level, a nominal 95 % plug-in interval achieves 29.09 % ensemble coverage near the early loss maximum. Exact marginalization restores ensemble coverage at 3.50 times the width of the state-informed reference; transmitting the extra coordinate removes this interface loss only when it is already available upstream. A model-specific analytic bound guarantees a marginal information-retention interval of at least 0.02736 time units for a tolerance of 0.05 nats, compared with a numerically located first crossing at 0.02820. Joint-target calculations show why cancellation at one forecast time cannot certify an entire curve. Noise sensitivity and correlated-readout tests delimit the numerical claims. The results concern a controlled interface and do not infer an unknown source state or validate a real astrophysical event. Keywords: predictive sufficiency; model coupling; uncertainty quantification; information loss; adaptivecomputation; time-domain astrophysics.

Zenodo (CERN European Organization for Nuclear Research)
The Institute for Education Research (CA)
Openalex Percentile: Top 16%
Advanced Thermodynamics and Statistical Mechanics
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