How Much Memory Does Your Agent Actually Need?
Hugging Face 2 weeks ago 20
A study of ALTK-Evolve found that agents benefit from different amounts of self-distilled memory guidelines depending on model capability: stronger models gain from the full guideline set, weaker models perform better with selective retrieval, and saturated models show no improvement.The strongest result was gpt-oss-120b gaining +16.1 percentage points in task completion using curated retrieval while adding only 5% token overhead, compared to full guideline injection which cost 51% more tokens.This means agentic memory should be calibrated per model tier rather than simply maximized, with prompt caching making even large guideline sets affordable in production.