Small Backbone, Large Memory: What CHARM Actually Improves
TL;DR for operators A small neural backbone is not necessarily a small learned system: auxiliary task memories also consume parameters, and multiple inference attempts consume compute. In Structured Sparse Memory for Recurrent Reasoning1, CoSE cuts task-memory parameters by about 94% while improving controlled ARC accuracy. But CHARM’s strongest results also rely on rule-preserving synthetic examples, carefully configured recurrence, longer training, and inference-time aggregation. For teams evaluating specialized reasoning models, the actionable comparison is task accuracy versus total learned footprint, training-data investment, and inference cost. Private-memory updates are promising for adding related tasks without changing shared weights; the experiments do not demonstrate production savings or reliable generalization to arbitrary new work. ...