Hebbian SDR Adapter for Streaming Context Learning

Place the adapter after a frozen encoder: for a graph model use a pretrained node feature encoder h=f(x_u), while for a language or multimodal stream use the frozen item representation h. Hash or randomly project h to dimension d if necessary. For every streaming example, identify a context c from a neighboring graph node, a co-occurring token, a label-free augmentation, or a random-walk window. Update a count sketch for feature-context pairs, compute q from smoothed counts, and perform the BCM update on the adapter columns. To reduce cost, update only the top L response columns plus a small random subset of non-top columns; compute the final k-winner code from all responses or a candidate index. Clip the norm of h and renormalize w_j after each minibatch. Keep the base encoder frozen and train only a linear classifier on SDRs in the first experiment, then compare against full fine-tuning and a replay-based adapter. Use a two-layer GraphSAGE encoder on Cora or ogbn-arxiv, expose new nodes in temporal batches, and evaluate accuracy after every batch, trainable parameters, peak memory, and forgetting on earlier nodes. The first success criterion is at least ten times fewer trainable parameters than full fine-tuning, final accuracy within two percentage points, and a smaller accuracy drop when the stream changes and old examples are not replayed.