Recursive Self-Referential Compression (RSRC): AI's Survival Map in the Post-Scaling Era

Description

Abstract

Compute-centric AI scaling is reaching thermodynamic and economic limits—forcing a critical
paradigm shift in AI evaluation and optimization. Conventional brute-force methods are no longer sus-
tainable, as diminishing returns and skyrocketing energy costs underscore the need for a new approach.
DeepSeek R1, despite its disruptive nature, was a compelling demonstration of the limitations inherent
in brute-force scaling. Its superior efficiency—achieving state-of-the-art performance with drastically
reduced energy consumption, signals a transformative moment in AI.
In this work, we introduce Recursive Self-Referential Compression (RSRC) as a dual-metric framework
that distinguishes between training efficiency (RSRCt) and inference efficiency (RSRCi).
This separation enables a nuanced evaluation of models that, for example, exhibit modest training effi-
ciency yet excel in cost-effective inference. By integrating recursive processing, algorithmic compres-
sion, and thermodynamic cost, RSRC provides both a survival map, and the key to the next frontier of
AI scaling.