Dual-time-scale adaptive architecture for reliable online learning

A computing system comprising one or more processors and one or more non-transitory computer-readable storage media storing instructions that, when executed by the one or more processors, cause the computing system to:

• receive a request and determine a routing certificate by applying a scope function to at least one of the request and a context associated with the request, wherein the routing certificate is a discrete identifier representing a context partition;
• generate an output using a machine learning model comprising base parameters and adapter parameters, wherein an adapter state keyed to the routing certificate is applied as an additive low-rank delta to at least one transformation of the machine learning model, and wherein adapter states keyed to routing certificates other than the determined routing certificate are not applied to the at least one transformation during generation of the output;
• receive a supervision signal associated with the request and the output, and compute a candidate update to the adapter state keyed to the routing certificate;
• evaluate a gating policy to determine whether to authorize committing the candidate update, wherein the gating policy authorizes committing the candidate update only when each of the following conditions is satisfied:
• (i) a confidence measure meets a confidence threshold, (ii) a risk measure satisfies a risk threshold, (iii) a scope eligibility check confirms that the routing certificate matches the adapter state and is eligible for update, and (iv) a budget check confirms that a cumulative drift budget for the adapter state has not been exceeded;
• when the gating policy does not authorize committing the candidate update, refrain from modifying the adapter state;
• when the gating policy authorizes committing the candidate update, apply a projection operator to the candidate update to produce a constrained update, commit the constrained update to the adapter state, and increment a version index associated with the adapter state;
• append, to a versioned transaction log, a transaction record comprising at least the routing certificate, a delta update corresponding to the constrained update, provenance metadata comprising a timestamp, a supervision source identifier, the confidence measure, the risk measure, and a context hash, and the version index;
• and
• responsive to a rollback instruction identifying a target version, restore the adapter state to the target version by computing a prefix sum of delta updates from a baseline adapter state through the target version using the versioned transaction log, without modifying the base parameters of the machine learning model.