Active Memory Feedback Loop for Fast–Slow Dynamics in Liquid Neural Networks

Continuous-time recurrent models update hidden states as observations arrive, making them suitable for irregular and non-stationary temporal data. Liquid Neural Networks (LNNs) extend this formulation through input-dependent state dynamics, but they still rely on a single evolving state to integrate new observations and retain earlier context. Under long temporal gaps or missing inputs, earlier evidence can weaken as the hidden state continues to evolve. We propose Active Memory Feedback Loop (AMFL), a memory-augmented LNN architecture that feeds associative retrieval back into the recurrent computation. AMFL combines a learned Short-Term Memory (STM) that stores global sequence prototypes with a sequence-local Iconic Memory (IM) that adapts through novelty-gated, gradient-free updates. After each liquid refinement step, IM retrieves an associative representation, forms a residual correction, and writes the correction into a feedback buffer that conditions the following recurrent update. Memory therefore influences the evolving liquid representation rather than only augmenting the final prediction head. We evaluate AMFL on seven time-series benchmarks covering classification, regression, continual learning, and robustness to missing inputs. AMFL obtains the best result on six of seven benchmarks and remains competitive on the remaining task. Ablations show that the gains arise from looped memory feedback, controlled IM adaptation, and residual correction. Additional missing-input and long-sequence forecasting experiments further support the role of associative feedback in preserving task-relevant context under partial observability.