Adaptive Associative Memory with Differentiable Content-Addressable Memories for Online Learning

Associative memory is a unifying abstraction underlying attention mechanisms, energy-based models, and adaptive inference systems. Analog content-addressable memories (CAMs) enable graded similarity search directly in hardware, supporting soft retrieval and online adaptation within a single substrate. We study differentiable CAM (diff-CAM) as a general associative memory primitive. Through controlled simulations, we characterize its retrieval robustness, learning dynamics, and stability–plasticity tradeoffs under distribution drift and representation shift. Compared to static CAMs, adaptive diff-CAM exhibits rapid rebinding and improved associative inference under non-stationary inputs. These results position diff-CAM as a hardware-aligned substrate for efficient and adaptive memory-augmented systems