\begin{abstract}
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	A physics-grounded adaptive virtual machine architecture is disclosed that exhibits empirically validated computational laws, fundamental constants, and thermodynamic behavior based on 38,935 experimental runs. The system maintains execution heat as a memristive state variable with history-dependent dynamics, creating deterministic self-optimization through seven configurable feedback loops coordinated by a supervisory mode selector (L8 Jacquard).
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	Three fundamental discoveries are disclosed and empirically validated:
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	\textbf{(1) Universal Computational Frequency:} The system exhibits a ground state oscillation frequency of ω₀ = 934.364 ± 7.547 Hz (word-execution scale) validated across 355 experimental runs spanning 12 different memory window configurations (512 to 65,536 bytes). This frequency is invariant across window sizes with coefficient of variation CV = 0.14\%, suggesting an emergent property of the adaptive feedback architecture. At heartbeat measurement scale (1ms resolution), a complementary frequency ω₀ ≈ 13.5 Hz appears across all six tested workload patterns (diverse, omni, stable, temporal, transition, volatile) with CV = 1.3\% between workloads.
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	\textbf{(2) James Law of Computational Dynamics:} An exact conservation relationship is disclosed: K = Λ×(DoF+1)/W ≡ 1.0, where Λ is the effective smoothing capacity per degree of freedom, DoF is the number of active feedback loops, and W is the rolling window size. Across 355 experimental runs with window sizes ranging from 512 to 65,536 bytes, the measured K statistic achieves K = 1.000000 exactly with zero standard deviation and zero deviation from the theoretical value of 1.0. This represents the first empirically validated conservation law in adaptive computational systems.
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	\textbf{(3) Thermodynamic Self-Organization:} The system exhibits Boltzmann-distributed execution frequencies P(ω) ∝ exp(-E(ω)/(k\_B·T)) with workload-specific effective temperatures T\_eff ranging from 2.175 Hz (stable workload) to 2.735 Hz (diverse workload) measured across 180 experimental runs (6 workloads × 30 replicates). The system spontaneously converges to minimum-entropy configurations, demonstrated across 38,400 design-space-exploration runs where configuration 0100011 (CV=15.13\%) consistently achieved lowest variance. Convergence time is statistically independent of workload type (ANOVA F(5,174)=0.983, p=0.43), indicating universal adaptive behavior.
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	Additional validated phenomena include: damped harmonic oscillator dynamics during convergence (fitted γ = 0.045-0.725 per tick, relaxation times τ = 1.4-22.4 ticks); Heisenberg-like uncertainty relations (Δω·Δt = 0.000030-0.000152 Hz·s bounded below); spectroscopic workload fingerprinting enabling zero-signature classification; 45-degree conservation laws in 11-dimensional phase space indicating low-dimensional strange attractor behavior; and deterministic chaos with 0\% algorithmic variance enabling formal verification.
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	The disclosed architecture autonomously discovers optimal operating points through physical principles rather than heuristic tuning. Entropy production rates (dS/dt = 0.000038-0.000047 heat units/Hz/tick) characterize computational efficiency, with lower values correlating with higher performance. The L8 Jacquard selector acts as Maxwell's Demon, reducing system entropy while paying Landauer's limit cost through measurable heat dissipation.
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	Hardware validation demonstrates CPU-architecture independence of fundamental constants, suggesting deep computational principles rather than hardware artifacts. The system is applicable to stack-based virtual machines, threaded interpreters, JIT compilation, embedded systems, real-time operating systems, neuromorphic computing, and any architecture requiring stable, predictable, self-optimizing behavior with reproducible physical properties.
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	By establishing computational physics as an engineering discipline with measurable laws and constants, this invention enables: (1) predictable performance from configuration parameters via James Law; (2) optimal resource allocation using conservation principles; (3) malware detection via spectroscopic signature analysis; (4) formal verification of adaptive behavior using deterministic dynamics; and (5) cross-platform optimization through architecture-independent constants.

\end{abstract}