# Stability-Aware Generalization Objective (SAGO)

The work introduces the Stability-Aware Generalization Objective (SAGO), which defines LLM generalization as per-instance behavioral stability of the same input under semantically equivalent variations, and evaluates eleven open- and closed-source models across six datasets on four behavioral axes—generation consistency, internal activations, confidence, and response mirroring—finding that every model exceeds a significance threshold on at least one axis, that the axes capture independent failure modes, and that cross-dataset variation can reverse model rankings.

The work introduces the Stability-Aware Generalization Objective (SAGO), which defines LLM generalization as per-instance behavioral stability of the same input under semantically equivalent variations, and evaluates eleven open- and closed-source models across six datasets on four behavioral axes—generation consistency, internal activations, confidence, and response mirroring—finding that every model exceeds a significance threshold on at least one axis, that the axes capture independent failure modes, and that cross-dataset variation can reverse model rankings.