Layered Online Service and Replay Control for Verified AI R and D Acceleration

This manuscript introduces Layered Online Service and Certified Replay Control (LOSCR), a practical, model-independent framework for evaluating and controlling verified acceleration in AI-assisted research and development. The paper addresses a central operational problem: AI systems can generate code, experiments, proofs, evaluators, datasets, tools, and reusable procedures, but these outputs constitute real progress only when verified gains exceed hidden work, evaluator failures, benchmark contamination, service overload, maintenance burden, unsafe artifacts, and misleading progress claims.

LOSCR separates lightweight observation from stronger evidence claims. It defines an always-on edge telemetry layer, service-capacity and queue-control mechanisms for validation, audit, replay, maintenance, and registry work, and a certified replay layer for reusable artifacts. The framework includes machine-readable claim profiles, deterministic claim checking, failure-code transitions, append-only ledgers, reference reducers, implementation adapters, evaluator audits, baseline and frontier governance, service-obligation accounting, and falsification rules.

The goal is to support live AI R&D workflows in which claims about acceleration can be continuously checked, downgraded, quarantined, or escalated according to observable evidence and operational capacity. The manuscript is intended for researchers and practitioners working on AI R&D automation, AI agents, evaluation methodology, software engineering automation, reproducible research infrastructure, and governance of AI-assisted scientific workflows.