Scaling Laws for Neural Language Models

This paper develops a transport-validity theory for agentic AI interventions that are first screened on small systems and later considered for frontier-scale deployment. Rather than predicting absolute frontier performance, it asks when a comparative gain observed at small scale can be carried forward without overclaiming. The analysis targets an explicitly delimited class of operationally isolatable interventions whose effects can be compiled from logged event-local channels with bounded spillover and replayable extraction maps. The paper proves structured failure modes for naive extrapolation, including sign reversal under bottleneck-weight shift and the vacuity of observable closeness when a descriptor…