In deterministic partially observable worlds, perfect prediction requires either identifying the relevant hidden quotient or achieving overwrite control, while high empowerment alone is insufficient.
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11 Pith papers cite this work, alongside 4,171 external citations. Polarity classification is still indexing.
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cs.AI 3 cs.LO 2 astro-ph.IM 1 cond-mat.mes-hall 1 cond-mat.soft 1 math.PR 1 physics.soc-ph 1 q-bio.NC 1years
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Closes the missing direction of an open question on incomparability of two induction theories via a short syntactic argument and extracts the Syntactic Invariance Principle.
A crumbling abstract machine yields a reversible Landauer's embedding for call-by-value lambda calculus with constant space overhead per step.
Information defined as maximum-caliber deviation derives IIT 3.0 cause-effect repertoires from constrained entropy maximization and equates to prediction error under CLT and LDT.
Positive-rate probabilistic cellular automata admitting stationary Bernoulli measures are exponentially ergodic with logarithmic mixing times for finite regions.
Defines a Cognitive Kardashev Scale using total power, cognitive fraction f, compute efficiency η, and brain reference to place current humanity at K ≈ 0.73 and estimate Type I/II capacities.
A thermodynamic-inspired information-geometric framework defines a composite LLM stability score that outperforms a utility-entropy baseline by 0.0299 on average across 80 observations, with gains increasing at higher entropy.
Energy production data falsifies the Kardashev 1% exponential model and yields an unphysical 1.6E15-year timescale to solar luminosity; the proposed KSN renormalization B(t) = P(t)/H(t) using Bitcoin hashrate spans 14 orders of magnitude from 2009-2024.
Nonlinear detuning stabilizes non-adiabatic magnonic dynamics in YIG:Co nanostructures, enabling low-occupancy resonant states with estimated 22 aJ switching energy.
The Kerimov-Alekberli model uses KL divergence on a Riemannian manifold with a Fisher-derived threshold and the Landauer principle to treat adversarial perturbations as measurable physical work for real-time AI system stability.
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Positive-rate PCA and IPS with stationary Bernoulli measures are rapidly forgetful
Positive-rate probabilistic cellular automata admitting stationary Bernoulli measures are exponentially ergodic with logarithmic mixing times for finite regions.