The Compute ICE-AGE: Invariant Compute Envelope under Addressable Graph Evolution
This addresses the issue of compute-intensive inference scaling for AI systems, offering a potential breakthrough in memory-governed scaling rather than inference-bound recomposition.
The paper tackles the problem of scaling compute cost in AI architectures by introducing a deterministic semantic state substrate that maintains invariant traversal latency and CPU utilization across 1M to 25M nodes, with per-node density as low as 687 bytes, enabling a projected 1.6 billion node capacity within 1 TiB.
This paper presents empirical results from a production-grade C++ implementation of a deterministic semantic state substrate derived from prior formal work on Bounded Local Generator Classes (Martin, 2026). The system was mathematically specified prior to implementation and realized as a CPU-resident graph engine operating under bounded local state evolution. Contemporary inference-driven AI architectures reconstruct semantic state through probabilistic recomposition, producing compute cost that scales with token volume and context horizon. In contrast, the substrate described here represents semantic continuity as a persistent, addressable memory graph evolved under a time-modulated local operator g(t). Work is bounded by local semantic change Delta s, independent of total memory cardinality M. Empirical measurements on Apple M2-class silicon demonstrate invariant traversal latency (approximately 0.25 to 0.32 ms), stable CPU utilization (approximately 17.2 percent baseline with Delta CPU approximately 0 to 0.2 percent), and no scale-correlated thermal signature across 1M to 25M node regimes under sustained operation. Measured per-node density ranges from approximately 1.3 KB (Float64 baseline) to approximately 687 bytes (compressed Float32 accounting). Under binary memory accounting, this yields a 1.6 billion node capacity projection within a 1 TiB envelope. These results indicate an empirically invariant thermodynamic regime in which scaling is governed by memory capacity rather than inference-bound recomposition. The Compute ICE-AGE is defined as the Invariant Compute Envelope under Addressable Graph Evolution, and the empirical evidence presented demonstrates this regime up to 25M nodes.