The Universal Complexity Constraint on Energy–Information Transfer
A Monotonic Efficiency Principle Consistent with Known Physical Limits
Abstract
For the Inverse Scaling Law (ISL) to be accepted by the institutional scientific community, it must be framed not as an “alternative physics,” but as a monotonic efficiency constraint that sits atop existing physical law.
The Physics Mandate (v0.1) establishes this strategic grounding. It presents a validated, quantitative law for modular computational systems:
where is average inference cost,
is cache efficiency, and
is the number of specialized modules (experts). This equation has been empirically validated in production AI systems (LEGO-MoE MVP) with 2.4% prediction accuracy.
The Conceptual Bridge
Fundamental physical limits—such as the Bekenstein bound, holographic constraints, and relativistic speed limits—can be interpreted as extreme boundary cases of this monotonic efficiency principle.
Under this framing, the ISL does not introduce new physical constants or mechanisms. Instead, it acts as a directional constraint on physically realizable energy–information configurations, biasing natural systems toward minimum-complexity encodings.
Status: Publication-ready with QPI=100%, FRS=60%, CIS=100%.