[论文解读] The Compute ICE-AGE: Invariant Compute Envelope under Addressable Graph Evolution

Raymond Jay Martin II|arXiv (Cornell University)|Feb 17, 2026

Parallel Computing and Optimization Techniques被引用 0

一句话总结

本文提出一个生产级的 C++ 确定性语义状态底盘，通过对持久图的局部保持性、受限局部变动来保持不变的语义连续性，在 Apple Silicon M2 级硬件上对 1M–25M 节点给出经验结果。

ABSTRACT

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.

研究动机与目标

• Investigate how to preserve semantic continuity in a graph-based state substrate under bounded local mutations.
• Evaluate a CPU-resident persistent semantic graph engine in terms of locality, latency, and resource utilization.
• Characterize performance and scalability across large persistent graph sizes on modern Apple hardware.
• Assess resilience under hostile ingress conditions and paging pressure to understand degradation patterns.

提出的方法

• Implement a deterministic semantic state substrate as a CPU-resident persistent semantic graph engine.
• Employ locality-preserving traversal and bounded local mutation to evolve semantic continuity.
• Measure traversal latency (P50 ~ 0.0014 ms) and steady-state CPU utilization (~17.2%).
• Assess persistence capacity under compressed Float32 storage (≈687 bytes per node) to project total node capacity (~1.6B nodes in 1 TiB).
• Conduct stochastic ingress perturbations, malformed topology tests, fragmented adjacency, and active paging experiments to study replay integrity and degradation modes.

实验结果

研究问题

• RQ1 Can semantic continuity be preserved incrementally in a graph-based state substrate under bounded local mutations?
• RQ2 What are the latency, CPU utilization, and memory-density characteristics of a deterministic semantic graph engine on modern hardware?
• RQ3 How does the system respond to adverse conditions such as perturbations, topology issues, and paging pressure in terms of replay integrity and failure modes?

主要发现

• Locality-constrained traversal remains efficient across scales from 1M to 25M nodes (P50 latency ≈ 0.0014 ms).
• Steady-state CPU utilization is around 17.2% with no measurable scale-correlated thermal amplification during sustained operation.
• Memory density under compressed Float32 storage is about 687 bytes per node, projecting ~1.6B nodes in a 1 TiB envelope.
• Deterministic replay remains stable under hostile ingress conditions, with degradation localized to bounded orphan structures rather than global divergence.
• The approach avoids repeated probabilistic inference by preserving semantic continuity structurally through bounded local mutations.