Externalizing Plasticity: Zero-Update Continual Learning via Symbolic Memories

Manojshyaam C J

Keywords: Continual Learning, Neuro-Symbolic AI, Foundation Models, Catastrophic Forgetting, External Memory, Edge Computing, DOLCE Ontology

TL;DR: We propose a zero-update continual learning framework that uses external DOLCE-guided symbolic memories to acquire new skills without modifying foundation model weights, eliminating catastrophic forgetting

Abstract:

Foundation models acquire broad competence
through large-scale pre-training, yet adapting
them to shifting real-world distributions demands either expensive retraining or fragile finetuning—both of which risk catastrophic forgetting of prior knowledge. We ask a different question: must the model change at all? We propose DCASM, a framework for decoupled continual adaptation in which a frozen foundation
model is paired with an external DOLCE-guided
symbolic memory—a structured “notebook” that
records human-ensemble decisions as ontological graphs of endurants, perdurants, and qualities. New skills enter via the notebook, never the
model weights; the model learns only to read and
execute symbolic graphs. Over time, repeated execution consolidates notebook patterns into model
activations, progressively reducing external reliance. On the CLEAR real-world continual learning benchmark, DCASM improves mean accuracy
by 8.4 percentage points over vanilla fine-tuning
and reduces catastrophic forgetting (BWT: -2.1 vs.
-18.4) while performing zero parameter updates
to the foundation model at test time

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Submission Number: 29