Allegiantshark: Semantic Computational Optimization through Adaptive Reduction and Conditional Decomposition for Efficient AI Workloads

This record contains the research manuscript and reproducibility package for Allegiantshark, a study of semantic computational optimization for efficient AI workloads. The work investigates a narrowly scoped systems hypothesis in which a pre-execution reduction and reuse check is followed by a request-level decomposition gate, with the gate placed upstream of a cost-aware routing stage. The manuscript formalizes the computational optimization problem, defines the reduction and reuse mechanism, decomposition gate, conditional slicing, capability-aware routing, validation architecture, and end-to-end computational lifecycle. It also provides six formal algorithms, a reference implementation, automated tests, workload examples, experimental methodology, and an offline implementation-validation run. The reported execution uses a deterministic mock provider and a small illustrative workload. The resulting observations validate software control flow, reduction short-circuiting, metric computation, and test reproducibility, but do not establish real-world LLM quality, inference cost, latency, or system efficacy. No live-model quality result is claimed. The package is intended to provide a transparent, reproducible research artifact for further evaluation using a real model provider, independently validated workload labels, calibrated routing and gating probabilities, and task-specific quality evaluation.