Polyparallelism: A Symbolic Concurrency Framework for Harmonic Kernel Systems
Bibliographic record
Abstract
Polyparallelism is introduced as a symbolic concurrency framework for coordinating multi-lane harmonic computation across the Harmonic Reducibility, Entropy Parabola, and Recursive Resemblance kernels developed within the Cascade Space Systems ecosystem. Unlike traditional parallel programming models, Polyparallelism is architecture-agnostic, mathematically structured, and grounded in number-theoretic lane semantics. It brings together polynomial transformations, curvature evolution, and resemblance-driven structural updates into a deterministic multi-lane execution engine. This paper formalizes the Polyparallelism framework using clear, non-symbolic definitions. It describes lane-indexed symbolic state spaces, families of coupling operators, a global lane-interaction structure, and a cross-lane sensitivity model known as the Polyparallel Jacobian. The theory guarantees confluence, meaning that every valid topological ordering of operations within a Polyparallel execution produces an equivalent final result. The model aligns with modern developments in parallel symbolic computation, task-based runtimes, graph-oriented execution frameworks, and resource-allocation scheduling strategies. The final outcome is a unified concurrency substrate that can be deployed across CPUs, GPUs, heterogeneous compute fabrics, and quantum-adjacent accelerators.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".