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The Compute ICE-AGE: Invariant Compute Envelope under Addressable Graph Evolution
R. Jay Martin
Intelligence
Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 94%
Last extracted: 7/21/2026, 2:31:04 AM
Summary
The paper introduces the Compute ICE-AGE framework, a deterministic semantic state substrate implemented in C++ that preserves semantic continuity through persistent graph evolution rather than probabilistic inference. Operating on Apple Silicon M2 hardware, the system demonstrates low-latency traversal and stable CPU utilization across 1-25 million nodes. Key components include a Bounded Local Generator Class (BLGC) for constant-time incremental updates and a calculus engine for entropy stabilization, relevance evolution, and propagation damping. The architecture decouples semantic identity from inference, allowing for deterministic replay integrity and localized degradation under hostile ingress conditions.
Entities (8)
Relation Signals (8)
R. Jay Martin → authored → Compute ICE-AGE
confidence 99% · The Compute ICE-AGE Author: R. Jay Martin
OPAL → implements → Compute ICE-AGE
confidence 95% · This paper presents empirical results from a production-grade C++ implementation of a deterministic semantic state substrate... The Compute ICE-AGE
OPAL → isimplementedin → C++17
confidence 95% · The implemented substrate is realized as a CPU-resident C++17 systems library
OPAL → runson → Apple Silicon M2 Pro
confidence 95% · The primary development and testing environment consisted of an Apple Silicon M2 Pro system
OPAL → contains → Calculus Engine
confidence 94% · The calculus engine was introduced to regulate semantic evolution operationally rather than probabilistically.
OPAL → uses → Bounded Local Generator Classes
confidence 92% · The operational behavior observed in the implemented substrate is derived from prior formal work on Bounded Local Generator Classes (BLGC)
Calculus Engine → performs → entropy stabilization
confidence 90% · The calculus layer regulates entropy stabilization, relevance evolution, propagation damping...
OPAL → uses → CBOR
confidence 90% · Persistence behavior was evaluated through repeated save-load replay cycles using CBOR-backed graph serialization.
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Abstract
Abstract:This paper presents empirical results from a production-grade C++ implementation of a deterministic semantic state substrate operating under bounded local state evolution. The system was realized as a CPU-resident persistent semantic graph engine designed to preserve semantic continuity structurally rather than repeatedly reconstructing it through probabilistic inference. Contemporary inference-driven AI systems repeatedly recompute semantic state through context replay and probabilistic recomposition. In contrast, the substrate described here evolves semantic continuity incrementally through locality-preserving traversal and bounded local mutation over persistent graph topology. Empirical measurements on Apple Silicon M2-class hardware demonstrated locality-constrained traversal behavior across scaling regimes ranging from 1 million to 25 million persistent semantic nodes. Traversal latency remained within low microsecond ranges (P50 approximately 0.0014 ms) under sustained workloads, while steady-state CPU utilization remained approximately 17.2% with no measurable scale-correlated thermal amplification observed during sustained operation. Measured persistent node density averaged approximately 687 bytes per node under compressed Float32 storage regimes, corresponding to a projected capacity of approximately 1.6 billion persistent semantic nodes within a 1 TiB memory envelope. Under hostile ingress conditions including stochastic perturbation, malformed topology, fragmented adjacency, and active paging pressure, deterministic replay integrity remained stable while degradation localized into bounded orphan structures rather than propagating catastrophic global divergence.
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- Source: https://arxiv.org/abs/2602.16736v3
- Canonical: https://arxiv.org/abs/2602.16736v3
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The Compute ICE-AGE Author: R. Jay Martin V.3 May, 2026 Lake Arrowhead, CA | ray@opalone.ai Abstract Page of 140 T his paper presents empirical results from a completed production-grade C + + i m p l e m e n t a t i o n o f a deterministic semantic state substrate derived from prior formal work on bounded local g e n e r a t o r c l a s s e s . T h e s y s t e m w a s m a t h e m a t i c a l l y s p e c i f i e d p r i o r t o implementation and subsequently realized as an operational substrate; all results reported here are drawn from direct measurement of the deployed system rather than from simulation or speculative modeling. Contemporary inference-driven architectures reconstruct semantic state through repeated probabilistic recomputation. As model dimensionality and temporal horizon increase, this paradigm produces scale- dependent compute growth and sustained energy expenditure. In contrast, the substrate described here decouples semantic continuity from inference by representing meaning as a persistent, addressable state within a deterministic memory graph. The architectural objective is not to extend LLM memory capacity but to externalize s e m a n t i c c o n t i n u i t y a s a p e r s i s t e n t d e t e r m i n i s t i c s u b s t r a t e o p e r a t i n g independently of probabilistic inference. In this configuration, the substrate functions as a low-variance continuity layer, while probabilistic inference operates strictly as a higher-order reasoning process. Semantic identity therefore persists independently of inference recomposition. State evolution is governed by a time- modulated operator g(t), in which time acts as an active control variable over graph traversal and composition. Semantic updates occur through bounded local operations over persistent structure rather than through repeated global recomputation. This design constrains computational work to graph traversal and localized mutation. Empirical measurements on consumer-grade Apple Silicon demonstrated locality- constrained traversal across scaling regimes ranging from 1M to 25M persistent semantic nodes. Traversal latency remained within low microsecond ranges (P50 approximately 0.0014 ms) under sustained workloads, with no observed catastrophic scale-dependent divergence. Sustained operation maintained stable CPU utilization (~17% baseline) with no measurable scale-correlated thermal escalation. Serialized node footprint averaged approximately 1.3 KB under Float64 precision, while quantization analysis indicated feasible billion-node scaling within a 1 TiB memory envelope. We d o c u m e n t t h e s t r u c t u r a l i n v a r i a n t s t h a t preserve identity, continuity, and auditability under long-horizon operation, and show that scaling behavior is governed by available memory capacity rather than inference complexity. This regime shift, from inference-dominated recomputation to deterministic memory-bound traversal, constitutes the Compute ICE-AGE: a thermodynamically stabilized semantic regime in which scale is bounded by memory capacity rather than probabilistic recomposition cost. The Compute ICE-AGE 1. The Industry Problem Contemporary AI systems repeatedly reconstruct semantic continuity through probabilistic inference. Meaning is not preserved as persistent operational state; it is regenerated from context history during each invocation. As context horizon, token volume, and model dimensionality increase, semantic continuity becomes increasingly coupled to inference cost, memory traffic, and sustained compute load. This reconstruction model creates a structural inefficiency: small semantic changes frequently require full-scale recomposition across large parameter spaces and expanding context windows. Even when the semantic delta is minimal, the system repeatedly traverses the same global inference surface to regenerate prior continuity. In transformer-based architectures, semantic response generation is conditioned on token history rather than persistent semantic state. Let semantic response at time t be denoted . Under inference-driven reconstruction: where θ represents model parameters and represents prior context. Continuity therefore remains implicit and transient. Semantic state is reconstructed probabilistically rather than preserved structurally. Under this regime: •Compute cost scales with context horizon and token throughput. •Activation storage scales with inference depth. •Repeated attention operations regenerate previously established semantic state. •Operational cost remains coupled to global model traversal rather than localized semantic change. This behavior persists even when semantic change between operations is negligible. The dominant computational cost is determined primarily by model parameter mass, activation s t s t ∼P(s∣C <t ,θ) C <t Page of 240 The Compute ICE-AGE propagation, and context participation rather than by the magnitude of the semantic update itself. Retrieval-augmented generation (RAG) partially offsets context limitations but does not eliminate reconstruction behavior. Retrieved context must still be reintegrated into probabilistic inference during each invocation. Retrieval therefore supplements reconstruction rather than replacing it with persistent continuity. As semantic systems scale toward longer operational horizons, autonomous agents, and robotics, this reconstruction regime produces increasing pressure across compute infrastructure, memory bandwidth, and sustained thermal load. The substrate presented in this work departs from this regime by externalizing semantic continuity into persistent deterministic state. By separating continuity maintenance from inference generation, the system constrains computational work according to semantic evolution rather than model scale. Continuity replaces repeated reconstruction. Page of 340 The Compute ICE-AGE 2. Persistent Semantic State OPAL is a deterministic semantic substrate designed to preserve and evolve persistent semantic state independently of probabilistic inference. The system functions as a semantic continuity layer rather than as a language model, retrieval engine, or vector similarity system. The implemented substrate is realized as a CPU-resident C++17 systems library operating at the operating-system layer. It is not a model wrapper, inference accelerator, middleware cache, vector database, retrieval-augmented generation pipeline, or approximate nearest- neighbor retrieval system. Probabilistic models remain useful as reasoning layers, but continuity itself is maintained structurally within persistent graph state. Semantic state is represented as a persistent graph G = (V, E), where nodes encode stable semantic units and edges encode bounded relational structure. Once instantiated, semantic identity persists unless explicitly mutated. Meaning is therefore preserved operationally rather than regenerated from prompt history during each invocation. Under this model, queries do not reconstruct semantic continuity through repeated global inference traversal. Instead, they retrieve, traverse, and advance existing semantic state through bounded local operations over persistent graph structure. This produces several important distinctions from inference-driven reconstruction systems: • semantic identity persists independently of prompt horizon, • continuity survives save-load replay cycles, • semantic advancement occurs incrementally rather than through repeated regeneration, • traversal remains locality-preserving and structurally bounded, • and persistent semantic state remains addressable across long-horizon operation. The substrate executes entirely on commodity CPU hardware during steady-state traversal and mutation. No GPU acceleration, tensor-parallel inference kernel, cosine-similarity ranking loop, or stochastic retrieval layer is required for continuity maintenance. Address Page of 440 The Compute ICE-AGE resolution occurs through deterministic graph traversal rather than probabilistic semantic reconstruction. Persistence behavior was evaluated through repeated save-load replay cycles using CBOR- backed graph serialization. Deterministic replay preserved invariant graph hashes across measured workloads, including stochastic ingress and topology perturbation experiments. Under hostile conditions, degradation localized into orphan structures and fragmented neighborhoods rather than propagating catastrophic global divergence. The substrate therefore separates continuity maintenance from inference generation. Probabilistic reasoning systems generate inference over persistent semantic structure, while OPAL preserves continuity as durable operational state. Continuity becomes operational state rather than reconstructed approximation. Page of 540 The Compute ICE-AGE 3. Bounded Local Evolution The operational behavior observed in the implemented substrate is derived from prior formal work on Bounded Local Generator Classes (BLGC) for Deterministic State Evolution [1]. As established in Martin (2026), BLGC demonstrates that deterministic state evolution can proceed with constant-time incremental work relative to total system size under bounded locality constraints. That work provided the theoretical existence proof for bounded-local operators acting over persistent state spaces rather than through repeated global reconstruction. The present implementation applies those locality constraints directly to persistent semantic graph evolution. Rather than traversing a global parameter surface during each semantic update, state evolution occurs through bounded local traversal and mutation under a deterministic operator. Let semantic state be represented as a persistent graph: Evolution proceeds according to: where operates only within a bounded traversal neighborhood d. Under bounded locality constraints, incremental work remains proportional to traversal radius rather than total graph size or model dimensionality: This produces an important structural distinction from inference-driven architectures. In transformer-based systems, computational work scales primarily with parameter mass, G=(V,E) G t+1 =g(t)(G t ) g(t) W∝O(d) Page of 640 The Compute ICE-AGE activation propagation, and context participation. Under bounded local evolution, operational work becomes constrained by semantic locality and mutation scope. The result is a practical decoupling between representational dimensionality and incremental computational work. Graph expansion increases addressable semantic capacity without requiring proportional growth in per-operation traversal cost under measured workloads. Operationally, this locality-preserving behavior was observed throughout sustained multi- million-node testing. Traversal latency remained bounded under scaling pressure, while degradation localized into fragmented neighborhoods and orphan structures rather than propagating catastrophic global divergence. The BLGC framework therefore provides the locality constraints underlying persistent semantic continuity within the implemented substrate. Continuity evolves through bounded structural advancement rather than repeated probabilistic reconstruction across a global inference surface. Reference [1] Martin, R. J. (2026). Bounded Local Generator Classes for Deterministic State Evolution. arXiv. https://arxiv.org/abs/2602.11476 Page of 740 The Compute ICE-AGE 4. The Calculus Engine Persistent semantic continuity alone is insufficient for long-horizon intelligent systems operating under noisy ingress, probabilistic reasoning layers, and evolving topology. Without regulation, persistent semantic graphs accumulate entropy, fragment structurally, amplify propagation drift, and degrade traversal locality over time. The calculus engine was introduced to regulate semantic evolution operationally rather than probabilistically. Rather than repeatedly reconstructing continuity through global inference, the engine stabilizes persistent semantic state incrementally through bounded local regulation operating directly over graph topology. The implementation regulates entropy stabilization, relevance evolution, propagation damping, centroid alignment, semantic compression, and locality-preserving mutation directly within the substrate through deterministic update operations including stabilizeEntropy(), evolveRelevance(), relevanceCurve(), propagation decay functions, centroid alignment scoring, and locality-preserving compression routines. Operationally, the calculus engine functions as the stabilization layer governing persistent semantic evolution under sustained operation. Continuity is not repeatedly regenerated through global probabilistic reconstruction. It is incrementally regulated through bounded local semantic evolution over persistent graph state. Semantic Regulation Current AI systems repeatedly reconstruct semantic continuity through global probabilistic inference. Semantic state is regenerated from context history during each invocation rather than evolved as persistent operational structure. As interaction horizon increases, this reconstruction process amplifies compute repetition, memory traffic, propagation instability, and sustained thermal load. Persistent semantic continuity alone does not eliminate these pressures. Long-horizon semantic systems operating under noisy ingress, probabilistic reasoning layers, OCR Page of 840 The Compute ICE-AGE corruption, fragmented topology, stochastic propagation, and structurally unstable semantic input accumulate entropy over time. Without regulation, persistent semantic graphs drift structurally, fragment semantically, amplify unstable propagation paths, and degrade traversal locality. The calculus layer was introduced to regulate semantic evolution operationally rather than probabilistically. Instead of repeatedly reconstructing continuity globally, the substrate evolves semantic state incrementally through bounded local regulation operating directly over persistent graph topology. Operationally, the calculus layer regulates entropy stabilization, relevance evolution, propagation damping, centroid alignment, semantic compression, locality coherence, and bounded structural mutation during sustained graph evolution. Relevance values strengthen or decay according to traversal frequency, relational reinforcement, semantic coherence, propagation stability, and locality preservation across persistent state. Strongly aligned semantic neighborhoods stabilize naturally over time, while weakly connected or low-coherence structures decay toward isolation. Under stochastic ingress conditions, malformed topology, noisy embeddings, fragmented adjacency, probabilistic model output, and unstable propagation paths produce orphan structures, fragmented semantic neighborhoods, locality degradation, and propagation instability. However, degradation remained localized rather than globally divergent under measured workloads. Entropy stabilization constrained uncontrolled propagation, deterministic replay preserved invariant graph structure across repeated save-load cycles, and bounded local regulation prevented instability from cascading across unrelated semantic regions. The distinction is operationally important. Deterministic persistence does not require deterministic ingress. The substrate accepts noisy semantic input while the calculus layer continuously regulates propagation, relevance evolution, locality alignment, entropy stabilization, and structural compression across persistent graph state. Continuity is not repeatedly regenerated. It is incrementally stabilized. Page of 940 The Compute ICE-AGE Operational Mechanics The calculus layer operates through deterministic local update mechanics applied directly over persistent graph topology. Rather than reconstructing semantic continuity globally through repeated inference traversal, the substrate evolves semantic state incrementally through bounded local regulation. Operationally, the regulation layer stabilizes entropy, evolves semantic relevance, damps propagation instability, aligns semantic centroids, compresses persistent topology, and constrains locality divergence during sustained graph evolution. Entropy stabilization regulates uncontrolled semantic propagation across weakly related regions. As traversal pressure increases or noisy ingress introduces malformed adjacency, localized stabilization routines constrain propagation spread and isolate structurally unstable neighborhoods before instability cascades globally. These behaviors are implemented through deterministic update operations including stabilizeEntropy() and propagation decay functions operating over bounded traversal neighborhoods. Relevance evolution regulates semantic persistence over time. Relevance values strengthen or decay according to traversal frequency, relational reinforcement, semantic coherence, locality preservation, and propagation stability. Frequently traversed semantic neighborhoods stabilize naturally through repeated reinforcement, while weakly connected or low-coherence structures decay toward isolation. This behavior is implemented operationally through evolveRelevance() and relevanceCurve() update mechanics. Centroid alignment scoring regulates semantic locality coherence across evolving graph regions. During traversal and mutation, semantic neighborhoods continuously re-align around structurally reinforced regions while unstable propagation paths weaken progressively over time. This bounded alignment behavior constrains locality fragmentation under stochastic ingress conditions and preserves traversal stability during long-horizon evolution. Page of 1040 The Compute ICE-AGE Propagation decay functions regulate semantic expansion pressure during traversal. Instead of allowing unrestricted propagation across persistent topology, propagation strength decreases according to traversal depth, locality coherence, and structural reinforcement. Semantic influence therefore remains bounded locally rather than expanding globally across unrelated graph regions. Compression behavior operates incrementally through locality-preserving structural reduction rather than destructive semantic collapse. Persistent topology compresses through bounded local mutation, semantic reinforcement, and relevance-guided stabilization while preserving deterministic replay integrity across save-load cycles. The operational consequence is that semantic continuity evolves through bounded local regulation rather than repeated global reconstruction. Stability emerges incrementally through deterministic traversal, reinforcement, decay, alignment, and compression operating continuously over persistent semantic state. Semantic Stabilization Deterministic persistence does not require deterministic ingress. Real-world semantic systems operate under noisy conditions including OCR corruption, probabilistic model output, malformed topology, fragmented adjacency, stochastic propagation, incomplete semantic linkage, and structurally unstable input. Persistent continuity systems must therefore stabilize evolving semantic state without assuming globally coherent ingress behavior. Under stochastic ingress conditions, malformed semantic structures produced orphan nodes, fragmented neighborhoods, unstable propagation paths, and degraded locality coherence. Noisy embeddings and probabilistic inference output introduced inconsistent relational structure and irregular semantic propagation across persistent graph topology. The substrate does not respond to these conditions through repeated global reconstruction. Instead, stabilization emerges through bounded local regulation operating continuously over persistent graph state. Page of 1140 The Compute ICE-AGE Entropy stabilization constrained uncontrolled propagation across weakly related semantic regions. Propagation decay functions damped unstable traversal amplification and reduced semantic expansion pressure across fragmented topology. Relevance evolution continuously strengthened coherent semantic neighborhoods while weakly reinforced structures decayed progressively toward isolation. Centroid alignment scoring preserved locality coherence during sustained mutation and traversal. Structurally reinforced semantic neighborhoods stabilized incrementally over time, while unstable propagation paths weakened through bounded local regulation rather than global recomposition. This behavior produced an important operational consequence: degradation remained localized rather than globally divergent under measured workloads. Fragmentation occurred, orphan structures emerged, and locality degraded under hostile conditions, but instability did not propagate catastrophically across unrelated semantic regions. Replay integrity remained stable throughout measured save-load cycles. Deterministic replay preserved invariant graph hashes despite noisy ingress, fragmented topology, and stochastic traversal perturbation. Semantic continuity therefore survived imperfect semantic input without requiring probabilistic regeneration of prior state. Graph hygiene emerged as an operational stabilization requirement rather than a theoretical abstraction. Orphan isolation, propagation regulation, bounded locality preservation, and deterministic cleanup routines became necessary components of sustained semantic continuity under long-horizon operation. The operational result is a continuity substrate capable of evolving persistent semantic structure under noisy conditions without requiring repeated global reconstruction of semantic state. Stability emerges incrementally through bounded local regulation operating continuously over persistent graph topology. Page of 1240 The Compute ICE-AGE 5. Experimental Environment All measurements reported in this work were obtained from the implemented OPAL substrate operating on commodity consumer hardware rather than specialized compute infrastructure. The primary development and testing environment consisted of an Apple Silicon M2 Pro system with 16 GB unified memory operating under sustained multi-million-node workloads. The substrate was implemented as a CPU-resident C++17 systems library with deterministic graph traversal, bounded local mutation, and CBOR-backed persistence operating directly over persistent semantic graph topology. Measurements were collected from live operational runs rather than from simulation or isolated synthetic benchmarks. Testing included both structured and hostile ingress conditions. Workloads incorporated deterministic semantic insertion, stochastic graph perturbation, malformed topology, OCR- derived corruption, noisy semantic embeddings, fragmented adjacency, ANN/LSH perturbation experiments, and mixed semantic traversal pressure under sustained operation. Persistence behavior was evaluated through repeated save-load replay cycles using deterministic CBOR serialization and invariant graph hash verification. Traversal behavior was measured under increasing node count, memory pressure, and paging conditions to evaluate locality preservation, degradation characteristics, replay integrity, and traversal stability during sustained operation. Additional interface and continuity testing was performed through iPhone simulator integration using the operational substrate as a persistent semantic continuity layer beneath probabilistic reasoning systems. Workload and Data Structures The evaluation utilized synthetic ingress workloads designed to simulate long-horizon semantic evolution under both stable and hostile operating conditions. Testing focused on persistent graph-state continuity, traversal locality, replay preservation, and degradation behavior under increasing scale and structural perturbation. Page of 1340 The Compute ICE-AGE The substrate was scaled incrementally from 1 million to 25 million persistent semantic nodes operating over bounded relational graph topology. Workloads incorporated deterministic semantic insertion, locality-preserving traversal, repeated persistence roundtrips, sustained traversal pressure, and stochastic topology mutation during active operation. Persistence operations utilized CBOR-backed binary serialization to local SSD storage. Save- load replay cycles were repeatedly executed to evaluate deterministic replay integrity, invariant graph hash preservation, structural continuity, and topology survivability under sustained operation. Ingress conditions included both structured semantic hierarchies and hostile semantic workloads incorporating OCR-derived corruption, malformed relational topology, fragmented adjacency, noisy semantic embeddings, stochastic propagation behavior, and ANN/LSH perturbation experiments. These workloads were intentionally designed to stress locality preservation, graph hygiene, propagation regulation, and stabilization behavior under imperfect semantic conditions. Measurement Methodology Direct instrumentation was used to capture traversal behavior, memory pressure, thermal stability, persistence integrity, and degradation characteristics during sustained operation across scaling regimes ranging from 1 million to 25 million nodes. Traversal latency was measured continuously at P50, P95, and P99 intervals during locality- preserving graph traversal under sustained semantic evolution workloads. Measurements focused on traversal stability, jitter behavior, bounded locality performance, and degradation characteristics as total graph cardinality increased under active paging pressure. Memory footprint was monitored relative to available physical RAM capacity in order to evaluate paging behavior, traversal survivability, locality degradation, and stability under Page of 1440 The Compute ICE-AGE oversubscribed memory conditions. Particular attention was given to graceful degradation behavior once total graph footprint exceeded available unified memory. Thermal stability measurements tracked sustained CPU utilization, operating behavior, and workload consistency during multi-hour evolution cycles operating under continuous traversal and mutation pressure. Measurements focused on determining whether operational cost scaled catastrophically with graph cardinality or remained locality-constrained under sustained operation. Structural integrity was verified through deterministic replay instrumentation using invariant graph hash matching across repeated save-load persistence roundtrips. Replay verification was performed under both stable and hostile ingress conditions, including malformed topology, stochastic perturbation, orphan emergence, fragmented neighborhoods, and ANN/ LSH instability experiments. Page of 1540 The Compute ICE-AGE 6. Core Measurements The implemented substrate was evaluated under sustained operational pressure across scaling regimes ranging from 1 million to 25 million persistent semantic nodes. Measurements focused on traversal locality, replay integrity, paging survivability, degradation behavior, stochastic ingress response, and operational stability under hostile semantic conditions. Unlike isolated synthetic throughput benchmarks, the measurements reported here were collected during active semantic evolution, persistence replay, topology perturbation, and sustained traversal workloads operating over persistent graph topology. Testing intentionally included malformed ingress, fragmented adjacency, stochastic propagation, ANN/LSH perturbation, and oversubscribed memory conditions in order to evaluate stabilization behavior under non-ideal operational environments. The primary objective was not to demonstrate idealized peak throughput, but to observe how deterministic semantic continuity behaved under increasing scale, memory pressure, traversal stress, and structural instability. Measurements focused specifically on: • traversal latency stability across increasing graph cardinality, • jitter behavior under sustained traversal pressure, • locality preservation during oversubscribed memory conditions, • deterministic replay integrity across persistence roundtrips, • degradation characteristics under hostile ingress, • propagation stability during stochastic perturbation, • and survivability of persistent semantic continuity under sustained operation. Particular attention was given to graceful degradation behavior once total graph footprint exceeded available physical memory. Under these conditions, the substrate encountered active paging pressure, fragmented topology, orphan emergence, stochastic propagation instability, and ANN/LSH-induced perturbation while continuing deterministic replay and locality-preserving traversal operations. The following sections report the measured operational behavior observed during these experiments. Page of 1640 The Compute ICE-AGE Traversal Stability Traversal behavior was evaluated across scaling regimes ranging from 1 million to 25 million persistent semantic nodes under sustained locality-preserving traversal workloads. Measurements focused on traversal latency stability, jitter behavior, locality preservation, and degradation characteristics during active semantic evolution. Traversal latency was instrumented continuously at P50, P95, and P99 intervals during sustained graph traversal and mutation operations. Median traversal latency remained within low microsecond ranges (P50 ≈ 0.0014 ms) throughout measured locality-preserving traversal workloads. Higher-percentile jitter increased gradually under scaling pressure, with degradation governed primarily by paging overhead and locality fragmentation rather than by catastrophic traversal expansion. Observed behavior suggested that operational work remained strongly locality-constrained despite increasing graph cardinality. Importantly, traversal behavior did not exhibit the instability profile commonly associated with repeated global reconstruction systems operating under expanding semantic horizons. As graph cardinality increased, operational work remained locality-constrained rather than coupling directly to total graph size. Under oversubscribed memory conditions, total graph footprint exceeded available physical unified memory and active paging behavior emerged. Traversal latency increased measurably under these conditions; however, degradation remained gradual and operational continuity persisted. Jitter amplification remained bounded even during sustained paging pressure and fragmented topology conditions. Traversal locality remained operationally stable during stochastic perturbation experiments including malformed topology, noisy embeddings, fragmented adjacency, and ANN/LSH instability injection. Under hostile ingress conditions, traversal neighborhoods degraded locally rather than producing catastrophic global traversal divergence. Measured traversal behavior therefore suggested that operational cost remained strongly coupled to locality-preserving traversal behavior rather than to total persistent graph cardinality alone. As scale increased, degradation manifested primarily through bounded latency expansion and locality fragmentation rather than through catastrophic traversal collapse. Page of 1740 The Compute ICE-AGE This behavior became particularly important once active paging pressure emerged beyond available physical memory capacity. Even under these conditions, traversal continuity survived while degradation remained incremental rather than divergent. Figure 1 ~ Traversal Stability Across Persistent Semantic Scaling Regimes Traversal latency remained locality-constrained across scaling regimes ranging from 1 million to 25 million persistent semantic nodes. P50, P95, and P99 latency intervals widened gradually under increasing graph cardinality and paging pressure, but catastrophic traversal divergence was not observed. Operational work remained strongly coupled to bounded local traversal behavior rather than total persistent graph mass. FIG.1 Page of 1840 The Compute ICE-AGE Memory Pressure and Paging As total graph footprint increased beyond available physical unified memory, the substrate entered active paging conditions under sustained traversal and mutation workloads. These experiments were intentionally maintained beyond physical RAM capacity in order to observe degradation behavior under oversubscribed memory pressure rather than under idealized in-memory conditions. Under active paging, traversal latency increased measurably as memory pressure forced portions of persistent graph topology into SSD-backed virtual memory. Traversal neighborhoods experienced reduced locality efficiency, increased page retrieval overhead, and greater traversal jitter during sustained semantic evolution. However, degradation remained gradual rather than catastrophic. Traversal continuity persisted throughout sustained paging conditions, while jitter amplification remained bounded across measured workloads. Latency expansion occurred incrementally as paging pressure increased rather than exhibiting abrupt divergence or global traversal collapse. Importantly, operational instability remained locality-constrained even under oversubscribed memory conditions. Fragmented neighborhoods, orphan emergence, and locality degradation occurred during hostile traversal conditions, but instability did not propagate catastrophically across unrelated graph regions. Replay integrity also remained stable during paging pressure experiments. Deterministic save-load replay preserved invariant graph hashes despite fragmented topology, stochastic perturbation, and active virtual memory pressure operating simultaneously during sustained traversal cycles. This behavior produced an important operational observation: scaling pressure shifted increasingly toward memory capacity and locality efficiency rather than toward catastrophic compute divergence. The substrate remained operational under memory oversubscription while exhibiting graceful degradation under memory pressure. Page of 1940 The Compute ICE-AGE Figure 2 ~ Graceful Degradation Under Paging Pressure Traversal latency remained locality-constrained across scaling regimes ranging from 1 million to 25 million persistent semantic nodes. P50, P95, and P99 latency intervals widened gradually under increasing graph cardinality and paging pressure, but catastrophic traversal divergence was not observed. Operational work remained strongly coupled to bounded local traversal behavior rather than total persistent graph mass. FIG.2 Page of 2040 The Compute ICE-AGE Replay Integrity and Persistence Stability Persistence stability was evaluated through repeated deterministic save-load replay cycles operating under sustained traversal pressure, stochastic ingress conditions, fragmented topology, and active memory oversubscription. The objective was to determine whether persistent semantic continuity could survive hostile operational conditions without structural divergence across replay boundaries. Replay operations utilized CBOR-backed binary serialization operating directly over persistent graph topology. During each replay cycle, invariant graph hashes were generated before serialization and revalidated after reconstruction in order to verify deterministic persistence integrity across save-load boundaries. Under stable operating conditions, replay integrity remained consistent across repeated persistence roundtrips. Graph structure, traversal locality, semantic linkage, and bounded relational topology reconstructed deterministically without observable divergence across measured workloads. Replay stability was also evaluated under hostile ingress conditions including malformed topology, stochastic propagation, orphan emergence, fragmented adjacency, noisy embeddings, and ANN/LSH perturbation experiments. Under these conditions, structural degradation occurred locally through fragmented neighborhoods and orphan structures; however, replay integrity remained operationally stable and catastrophic graph divergence was not observed. Importantly, replay preservation survived active paging pressure once total graph footprint exceeded available physical unified memory. Save-load reconstruction continued operating under oversubscribed memory conditions while invariant graph hash verification remained stable across sustained replay cycles. This behavior produced an important operational distinction from probabilistic reconstruction systems. Semantic continuity was preserved structurally through deterministic persistence rather than regenerated heuristically through inference replay. Persistent semantic state Page of 2140 The Compute ICE-AGE survived serialization, paging pressure, hostile ingress, and traversal perturbation without requiring probabilistic reconstruction of prior continuity. Replay stability therefore emerged as an operational property of persistent graph structure rather than as a transient artifact of prompt reconstruction or inference regeneration. ANN/LSH Perturbation ANN/LSH perturbation experiments were conducted to evaluate substrate stability under intentionally unstable semantic retrieval conditions. These experiments introduced stochastic neighborhood disruption, fragmented adjacency behavior, noisy embedding relationships, and probabilistic traversal instability into persistent graph topology during sustained operation. The perturbation layer intentionally violated deterministic locality assumptions by introducing approximate semantic linkage behavior commonly associated with probabilistic nearest-neighbor retrieval systems. This produced unstable traversal neighborhoods, orphan emergence, locality fragmentation, inconsistent propagation behavior, and noisy semantic path evolution during active traversal workloads. As expected, instability increased under ANN/LSH perturbation conditions. Traversal locality degraded, fragmented semantic neighborhoods expanded, and propagation behavior became increasingly irregular as stochastic linkage pressure increased. Orphan structures emerged more frequently under sustained perturbation and locality coherence weakened progressively across unstable graph regions. However, degradation remained bounded rather than catastrophic. Instability localized primarily within perturbed traversal neighborhoods rather than propagating globally across unrelated semantic regions. Traversal continuity survived sustained perturbation workloads while deterministic replay preserved invariant graph hashes across repeated persistence roundtrips. Importantly, replay integrity remained stable despite stochastic traversal disruption, fragmented topology, and noisy semantic linkage operating simultaneously during active graph evolution. Structural degradation occurred operationally, but deterministic persistence survived hostile perturbation conditions without observable global graph collapse. Page of 2240 The Compute ICE-AGE These experiments produced an important operational distinction between deterministic continuity preservation and probabilistic retrieval instability. Approximate semantic linkage increased traversal noise and locality fragmentation, but bounded local regulation constrained instability propagation and preserved replay survivability under sustained perturbation pressure. Degradation occurred, but catastrophic divergence did not occur. Page of 2340 The Compute ICE-AGE 7. Failure Experiments The substrate was intentionally evaluated under hostile operational conditions in order to identify structural instability, locality degradation, replay failure modes, and propagation divergence during sustained semantic evolution. These experiments were not designed to demonstrate idealized throughput behavior. Their purpose was to determine how persistent semantic continuity behaved when deterministic locality assumptions were stressed or violated. Failure conditions included malformed topology, stochastic propagation, fragmented adjacency, ANN/LSH perturbation, noisy semantic embeddings, OCR-derived corruption, oversubscribed memory pressure, orphan emergence, and unstable traversal neighborhoods operating simultaneously during sustained graph evolution. Under these conditions, several predictable instability behaviors emerged. Fragmented topology produced orphan structures and degraded locality coherence across weakly reinforced semantic neighborhoods. Noisy ingress introduced unstable adjacency relationships and inconsistent propagation paths. ANN/LSH perturbation increased stochastic traversal instability and reduced locality preservation across evolving graph regions. One of the most important observations during testing was the substrate’s ability to convert stochastic semantic ingress into deterministic persistent structure. This process was not lossless. Under probabilistic ingest workloads, malformed or weakly reinforced semantic structures accumulated as orphan nodes and fragmented neighborhoods during sustained traversal and mutation pressure. Benchmark observations demonstrated this behavior directly. Deterministic ingest workloads produced zero orphan structures across measured 8,004-node replay verification runs while preserving stable replay integrity and invariant graph hashes. Under stochastic ingress conditions, ingesting equivalent semantic workloads produced the emergence of 39 orphan nodes while invariant graph hash verification continued returning PASS across replay boundaries. Importantly, stochastic instability remained localized rather than globally divergent. Structural degradation occurred operationally, but the substrate isolated structural “scars” to Page of 2440 The Compute ICE-AGE fragmented semantic regions rather than allowing noisy propagation to cascade into catastrophic graph collapse across unrelated topology. As graph cardinality increased beyond available physical memory capacity, active paging pressure amplified traversal jitter and reduced locality efficiency. Under sustained perturbation workloads, unstable semantic neighborhoods occasionally accumulated propagation noise faster than bounded local regulation could immediately stabilize. However, degradation remained gradual rather than catastrophic. Traversal continuity survived sustained perturbation workloads while deterministic replay preserved invariant graph hashes under active paging pressure and stochastic traversal instability. These experiments exposed operational requirements that were not obvious during early implementation stages. Persistent semantic continuity required active graph hygiene policies in order to preserve locality stability during long-horizon evolution. Orphan isolation, deterministic cleanup behavior, locality-preserving compression, propagation damping, replay verification, and bounded stabilization routines became necessary operational components of sustained semantic regulation. Because stochastic ingress is unavoidable in intelligent systems, deterministic hygiene mechanisms were implemented directly within the substrate to manage structural noise accumulation operationally rather than probabilistically. Stabilization routines including stabilizeEntropy(), evolveRelevance(), propagation decay functions, and locality-preserving cleanup operations continuously regulated semantic drift and constrained instability propagation across evolving graph topology. Importantly, the stabilization layer did not eliminate structural degradation entirely. Instead, bounded local regulation constrained instability propagation and prevented hostile semantic conditions from producing catastrophic divergence across unrelated semantic regions. These experiments produced an important operational conclusion: deterministic persistence alone is insufficient for sustained semantic continuity under hostile conditions. Persistent semantic substrates require active stabilization, locality preservation, propagation regulation, and deterministic hygiene protocols in order to survive long-horizon stochastic evolution. The substrate converts stochastic noise into deterministic structure. Stability emerges through explicit hygiene rather than probabilistic luck. Page of 2540 The Compute ICE-AGE 8. Thermodynamic Decoupling Having established that the substrate can localize structural failure and preserve deterministic replay integrity under paging pressure, we now examine the thermodynamic implications of bounded local semantic evolution. In the Compute ICE-AGE regime, semantic continuity becomes increasingly decoupled from repeated global inference reconstruction, shifting operational work toward locality-preserving traversal and bounded mutation. Traditional inference-driven systems repeatedly regenerate semantic continuity through global parameter traversal and context recomposition. Under this regime, operational work scales primarily with model dimensionality and active context horizon: where: • M represents model parameter mass. • L represents active context horizon. Even when semantic change between operations is minimal, inference systems repeatedly traverse large parameter surfaces in order to regenerate prior continuity. In contrast, the OPAL substrate evolves persistent semantic state incrementally through bounded local traversal and mutation. Operational work becomes constrained primarily by active semantic change operating within finite traversal neighborhoods: where: • represents the bounded traversal neighborhood. • represents active semantic change. W t ∝M⋅L W t ∝ | N(k) | ⋅Δs N(k) Δs Page of 2640 The Compute ICE-AGE This distinction produced measurable operational consequences during sustained traversal workloads. Because traversal and mutation remained bounded to finite semantic neighborhoods, operational work no longer scaled proportionally with accumulated historical semantic mass. Repeated global reconstruction was replaced by locality-preserving traversal operating only across actively evolving semantic regions. This reduced recurring energy expenditure associated with maintaining continuity and constrained compute amplification during long- horizon operation. Thermal Floor and CPU Behavior Under sustained traversal and mutation workloads across scaling regimes ranging from 1 million to 25 million persistent semantic nodes, CPU utilization remained comparatively stable despite increasing graph cardinality. Measured steady-state traversal workloads operated near approximately 17.2% CPU utilization under normal desktop operating conditions during sustained semantic evolution cycles. Importantly, no monotonic increase in thermal output or CPU load was observed as graph cardinality increased under locality-preserving traversal conditions, including experiments operating under active paging pressure beyond available physical unified memory. As semantic change approached minimal bounded mutation states ( s → 0), operational work converged increasingly toward traversal-local activity rather than repeated global recomposition. Energy expenditure therefore tracked active semantic evolution more closely than accumulated historical semantic mass. Under this regime, scale pressure shifted toward memory locality, paging behavior, and structural density rather than uncontrolled compute escalation. Scaling without Global Reconstruction Measured substrate behavior suggested that operational cost remained locality-constrained despite increasing persistent graph cardinality. Traversal degradation emerged gradually Page of 2740 The Compute ICE-AGE under paging pressure through locality fragmentation and virtual memory overhead rather than through uncontrolled compute amplification. This produced an important operational distinction from inference-driven reconstruction systems. Inference reconstruction repeatedly incurs global activation cost in order to preserve continuity. The OPAL substrate instead preserves continuity structurally and evolves semantic state incrementally through bounded local traversal. In inference-driven systems, maintaining long-horizon continuity requires repeated replay of historical semantic context through inference infrastructure. This produces recurring bandwidth, activation-memory, and token-processing overhead even when semantic change between operations is minimal. Persistent semantic continuity reduces this replay burden by preserving continuity structurally rather than repeatedly reconstructing it through token propagation. Operationally, continuity maintenance became increasingly memory-bound rather than inference-bound. The measured constraint therefore shifted toward memory capacity and locality preservation rather than toward repeated reconstruction cost. Page of 2840 The Compute ICE-AGE 9. Compression Economics The transition to a persistent deterministic substrate shifts the governing constraints of intelligent systems from inference throughput toward storage density and locality preservation. Current architectures pay a recurring “entropy tax” by repeatedly spending compute and energy to reconstruct previously established semantic continuity. In the Compute ICE-AGE regime, continuity becomes structural rather than transient; the primary scaling variable becomes the physical memory envelope required to preserve persistent semantic identity. Per-Node Density Metrics Empirical measurements of the OPAL substrate established two primary precision regimes for persistent node storage. Under the high-precision Float64 configuration, serialized footprint averaged approximately 1.3 KB per node. In this regime, the 128-dimension embedding vector constituted approximately 79% of total node mass (~1024 bytes), with structural linkage, identifiers, edge relationships, and metadata accounting for the remaining footprint. Under the compressed Float32 configuration, reduced embedding precision and optimized binary accounting reduced average node footprint to approximately 687 bytes per node. In both configurations, structural graph overhead remained bounded and sub-dominant relative to embedding mass. Scaling behavior therefore remained primarily precision- governed rather than topology-governed. Scaling to Billion-Node Regimes Unlike inference-driven systems, where scaling pressure emerges from repeated recomposition across expanding context horizons, OPAL scaling behavior remained coupled primarily to memory capacity and persistent storage density. Page of 2940 The Compute ICE-AGE Using the measured compressed-build density (~687 bytes per node), a theoretical 1 TiB memory envelope corresponds to approximately 1.6 billion persistent semantic nodes operating under identical traversal mechanics and locality-preserving evolution rules. Under additional quantization regimes, including reduced-precision embedding representations such as Int8 accounting, projected density envelopes exceed approximately 2.5 billion nodes per TiB without altering traversal locality or bounded operator behavior. Importantly, these projections do not require changes to traversal mechanics, replay behavior, or locality-preserving mutation rules. Scaling pressure shifts increasingly toward memory density and storage architecture rather than toward repeated inference reconstruction cost. Economic Implications for Edge AI The reconstruction cost of maintaining long-horizon semantic continuity makes inference- driven systems increasingly expensive for decentralized or power-constrained environments. Repeated context regeneration consumes bandwidth, activation memory, inference throughput, and sustained thermal overhead even when semantic change is minimal. By externalizing continuity into a high-density CPU-resident substrate, persistent semantic systems can maintain long-horizon semantic state on commodity hardware without requiring repeated reconstruction of prior continuity. Persistent semantic continuity therefore becomes increasingly storage-bound rather than inference-bound. Persistence also allows semantic state to survive power cycles, replay deterministically across save-load boundaries, and partition across distributed storage segments without losing structural continuity or relational identity. Under this regime, long-horizon semantic continuity becomes increasingly a state management problem rather than a repeated reconstruction problem. Page of 3040 The Compute ICE-AGE Figure 3 ~ Scale-by-Reconstruction vs Scale-by-Envelope Inference-driven systems repeatedly reconstruct semantic continuity through expanding context replay, global activation propagation, and probabilistic recomposition. Persistent semantic continuity instead preserves evolving semantic state structurally through bounded local traversal, deterministic replay, and locality-preserving mutation. Under this regime, scaling pressure shifts from repeated reconstruction cost toward memory envelope and locality preservation. Fig. 3 Page of 3140 The Compute ICE-AGE 10. Cognitive Memory Substrates Persistent semantic state enables an architectural separation between deterministic continuity and probabilistic reasoning. In conventional inference-driven systems, continuity exists primarily as transient context reconstructed during each invocation. Under the OPAL substrate, continuity persists structurally as durable semantic state operating independently of episodic inference generation. This distinction changes the operational role of memory in intelligent systems. Rather than functioning as a temporary inference buffer, persistent semantic state becomes a continuity substrate capable of preserving semantic identity across long operational horizons. Deterministic replay further extends this behavior. Because persistence operates through invariant graph structure and stable semantic linkage, prior semantic states can be reconstructed deterministically across save-load cycles without relying on probabilistic regeneration of historical context. Semantic continuity therefore survives traversal, mutation, paging pressure, and persistence replay as operational structure rather than as transient inference residue. This behavior becomes increasingly important in autonomous and bandwidth-constrained environments. Robotics systems, UAV platforms, distributed edge devices, and disconnected local AI systems cannot continuously depend on cloud-mediated context reconstruction in order to preserve operational continuity. Repeated inference replay introduces bandwidth dependence, thermal overhead, activation-memory pressure, and continuity instability during long-horizon operation. A persistent semantic substrate instead allows continuity to remain local, durable, and operationally stable across extended execution horizons. Local traversal and bounded mutation preserve evolving semantic state without requiring repeated reconstruction of historical state through token propagation. The deterministic nature of node identity and locality-preserving traversal also permits semantic state to partition across distributed environments without losing structural Page of 3240 The Compute ICE-AGE continuity. Persistent semantic graphs can operate as addressable distributed segments while preserving deterministic replay behavior and bounded local evolution across partitioned systems. Importantly, the substrate does not eliminate probabilistic reasoning. Inference remains useful for interpretation, generation, planning, and abstraction. The architectural shift instead separates reasoning from continuity preservation. Probabilistic inference becomes an episodic reasoning layer operating over persistent semantic state rather than serving as the sole carrier of memory continuity. Under this regime, long-horizon semantic continuity becomes an operational property of persistent state evolution rather than a transient artifact of repeated probabilistic reconstruction. Page of 3340 The Compute ICE-AGE 11. Potential Implications The empirical validation of the OPAL substrate across scaling regimes ranging from 1 million to 25 million persistent semantic nodes on consumer-grade Apple Silicon hardware suggests that semantic scale can become increasingly decoupled from thermodynamic escalation under bounded local evolution. Across measured workloads, the dominant operational constraint shifted toward memory capacity, locality preservation, and storage density rather than toward catastrophic compute divergence. By externalizing continuity into a persistent memory-bound substrate, long-horizon semantic systems become increasingly viable on local, privacy-preserving edge hardware. Persistent semantic continuity allows systems to evolve durable operational state without requiring repeated transmission of historical context through centralized inference infrastructure. This shift also suggests an alternative architectural scaling regime for intelligent systems. Under inference-driven reconstruction, scale increases through repeated recomposition across expanding context horizons, increasing activation pressure, bandwidth consumption, and thermodynamic load. Under persistent semantic continuity, scaling pressure instead becomes increasingly governed by memory envelope, storage density, and locality efficiency operating over durable semantic state. Operationally, this represents a transition from scale-by-reconstruction toward scale-by- envelope. The measured survival of deterministic replay integrity under stochastic ingress, fragmented topology, active paging pressure, and ANN/LSH perturbation further suggests that bounded deterministic substrates may provide greater operational stability in noisy real-world environments than transient probabilistic continuity buffers alone. Robotics systems, distributed sensing platforms, UAV architectures, and disconnected local AI systems frequently operate under incomplete, fragmented, or unstable data conditions where continuity preservation becomes operationally critical. Page of 3440 The Compute ICE-AGE Importantly, the substrate does not eliminate probabilistic inference. The measured implication instead suggests a redistribution of computational responsibility: probabilistic systems continue performing reasoning, abstraction, and generation, while persistent semantic substrates preserve continuity structurally across long operational horizons. The transition described throughout this work defines the Compute ICE-AGE: a computational regime in which intelligent systems increasingly preserve and evolve semantic continuity structurally rather than repeatedly reconstructing prior state through probabilistic recomposition. Page of 3540 The Compute ICE-AGE 12. Limitations While the measured results confirmed the structural and thermodynamic behavior of the implemented substrate within the evaluated operating regimes, several limitations define the current empirical scope of validation. The measurements reported throughout this work are implementation-specific observations obtained from the operational C++ substrate under the tested workloads and hardware environments. They should not be interpreted as universal guarantees for all persistent semantic systems, bounded-local substrates, or future implementations operating under materially different traversal mechanics, topology structures, or hardware conditions. Empirical locality-preserving traversal behavior and deterministic replay stability were validated across scaling regimes ranging from 1 million to 25 million persistent semantic nodes. While measured density accounting supports billion-node memory projections under compressed storage configurations, full billion-node runtime deployments have not yet been executed operationally under sustained traversal and mutation workloads. Traversal latency remained stable across measured P50, P95, and P99 intervals during tested workloads; however, extreme tail-latency behavior under adversarial traversal patterns, pathological locality fragmentation, or maximum memory oversubscription requires additional characterization beyond the current experimental envelope. The ability of the calculus layer to stabilize arbitrary high-entropy stochastic ingress also remains an open operational question. Although bounded local regulation successfully constrained instability propagation across measured perturbation workloads, the limits of deterministic convergence under severe topological corruption, uncontrolled probabilistic linkage, or hostile semantic ingress are not yet fully defined. Graph hygiene behavior likewise remains under active refinement. Orphan isolation, deterministic cleanup policies, locality-preserving compression, propagation regulation, and stabilization routines evolved substantially during implementation and stress-testing. Page of 3640 The Compute ICE-AGE Operational hygiene APIs and long-horizon cleanup policies should therefore be considered active engineering components rather than finalized theoretical guarantees. Distributed and partitioned semantic deployments have been architecturally considered but have not yet been evaluated operationally at the same scale or stress conditions as the single- node substrate reported throughout this work. These limitations define the present empirical boundary of the implementation while identifying areas requiring additional billion-node runtime validation, adversarial stress characterization, distributed replay testing, and long-horizon stochastic stabilization analysis. Page of 3740 The Compute ICE-AGE 13. Conclusion This work examined persistent semantic state evolution under bounded locality constraints through the implementation and stress-testing of a deterministic semantic substrate operating across scaling regimes ranging from 1 million to 25 million persistent semantic nodes on commodity Apple Silicon hardware. The measured results demonstrated that semantic continuity can persist structurally rather than requiring repeated probabilistic reconstruction through expanding context horizons and global inference replay. Traversal remained locality-constrained under increasing graph cardinality, deterministic replay preserved invariant graph integrity under stochastic ingress and paging pressure, and degradation emerged incrementally through locality fragmentation and orphan formation rather than through catastrophic global divergence. Operationally, the measured constraint increasingly became memory capacity, locality preservation, and storage density rather than uncontrolled recomposition cost. Persistent semantic continuity shifted operational work toward bounded traversal, incremental mutation, deterministic replay, and locality-preserving evolution operating over durable semantic structure. The experiments also demonstrated important operational boundaries. Stochastic ingress produced orphan structures, fragmented neighborhoods, and unstable propagation behavior under hostile conditions. Active paging pressure increased traversal jitter and reduced locality efficiency once graph footprint exceeded available physical memory. However, bounded local regulation constrained instability propagation and preserved deterministic replay continuity throughout measured workloads. Thermodynamically, the substrate exhibited behavior distinct from inference-coupled reconstruction systems. CPU utilization remained comparatively stable during sustained traversal workloads, replay integrity survived hostile perturbation conditions, and operational work increasingly tracked active semantic change rather than accumulated historical semantic mass. Page of 3840 The Compute ICE-AGE The measured results suggest that persistent semantic continuity may provide an alternative scaling path for memory architectures in long-horizon intelligent systems. By externalizing continuity into durable semantic state, intelligent systems can increasingly preserve operational memory structurally rather than repeatedly reconstructing prior continuity through probabilistic inference replay. The Compute ICE-AGE describes this transition toward memory-bound semantic continuity operating under bounded local evolution. The system did not eliminate computation. It changed where computation occurs. Page of 3940 The Compute ICE-AGE CITATION Martin, R. J. (2026). Bounded Local Generator Classes for Deterministic State Evolution. arXiv. https://arxiv.org/abs/2602.11476 In-Text Context (Section 3) “As established in Martin (2026), BLGC demonstrates the existence of deterministic state evolution classes whose incremental work remains bounded independently of total system size under locality constraints.” Page of 4040