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[2602.16736] The Compute ICE-AGE: Invariant Compute Envelope under Addressable Graph Evolution
Summary
The paper presents a deterministic semantic state substrate for AI, demonstrating a novel compute envelope that maintains performance across varying node scales, with empirical results showing stable latency and CPU utilization.
Why It Matters
This research addresses the challenges of scaling AI architectures by proposing a new framework that emphasizes memory efficiency and consistent performance. It offers insights into how AI can evolve with less computational overhead, which is crucial for future advancements in machine learning and operating systems.
Key Takeaways
- Introduces the Compute ICE-AGE framework for AI architectures.
- Demonstrates invariant traversal latency and stable CPU utilization across node scales.
- Highlights the importance of memory capacity over inference-bound recomposition in AI systems.
- Empirical results indicate a potential for high node capacity within a limited memory envelope.
- Offers a new perspective on semantic continuity in AI state management.
Computer Science > Operating Systems arXiv:2602.16736 (cs) [Submitted on 17 Feb 2026] Title:The Compute ICE-AGE: Invariant Compute Envelope under Addressable Graph Evolution Authors:Raymond Jay Martin II View a PDF of the paper titled The Compute ICE-AGE: Invariant Compute Envelope under Addressable Graph Evolution, by Raymond Jay Martin II View PDF 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. ...
