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Machine learning interatomic potentials now enable molecular dynamics simulations with near-density-functional-theory accuracy at scales inaccessible to conventional quantum methods. Yet most remain fundamentally static: once trained, they are deployed without adaptation, even as simulations enter configurations outside the original training distribution. Although self-consistency ensures that predicted forces remain exact derivatives of the learned energy surface, it guarantees only internal coherence, not fidelity to the true reference landscape. As a result, small local errors can accumulate into substantial long-timescale drift. This paper proposes a conceptual framework for error-correcting machine learning potentials based on on-the-fly residual learning. The architecture combines a self-consistent base predictor with an error detector, a lightweight residual corrector, an online updater, and a memory manager. Embedded directly within the molecular dynamics loop, these components enable the system to identify unreliable predictions, apply immediate corrections, selectively request sparse density-functional-theory labels, and retain corrective knowledge during continuous adaptation. By shifting from static deployment to simulation-aware error correction, the framework addresses the central limitations of extrapolation failure and accumulated drift. It therefore outlines a path toward adaptive machine learning potentials capable of sustaining reliable long-timescale materials simulations with controlled computational overhead.