Agent Memory: A Dose to Calibrate, Not a Switch to Flip
IBM Research's ALTK-Evolve lets agents learn from past trajectories by distilling guidelines injected at inference. Testing across eight models, they found optimal memory dosage varies: strong models benefit from full guideline sets, weaker models prefer curated retrieval, and saturated models show no gain. gpt-oss-120b gained +16.1pp with selective retrieval at only +5% tokens.
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“Agentic memory is not a feature you switch on. It's a dose you calibrate to the model.”
Summary last validated Aug 30, 2026
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