Plasticity-Compatible Representations Enable Fast Adaptation through Writable Interfaces

Long-lived adaptive systems may retain useful internal computation even as their observation or output mappings change during deployment. A central question is therefore not only how to adapt, but what learned representations make simple, restricted adaptation effective. We study a two-timescale recurrent framework in which outer-loop task learning establishes persistent dynamics and a low-dimensional query representation, while deployment-time feedback updates only a small, resettable interface. We show that the geometry of the learned queries directly governs the functional consequences of local plasticity: a feedback-driven write at one state transfers to another in proportion to their query similarity, determining both useful generalization and collateral interference. This yields a mechanistic criterion for plasticity-compatible representations, which we validate through analytic characterization and targeted interventions on the learned write/read geometry. On held-out action remappings, the resulting reward-modulated interface adapts rapidly from sparse binary feedback and remains robust when feedback is corrupted, without modifying the recurrent backbone. We further test the principle on neural population recordings, where writable interfaces support retrospective adaptation to a contingency reversal and cross-day recording drift by targeting output- and observation-side mismatches, respectively. Together, these results show that learned representations determine not only what can be read out from a fixed computation, but also how subsequent local learning propagates through it, providing a route toward selective adaptation without relearning reusable dynamics.