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PrisMem evolves agent memory by optimizing individual capabilities

huggingface.co by Yaoqi Chen et al. Editor score 8/10

PrisMem selects memory revisions by capability, then integrates complementary changes using paired differential cases; it reports gains of 10.54 and 7.83 percentage points on BEAM-1M and LongMemEval-M. The approach addresses how to improve agent memory without hiding regressions behind aggregate scores.

What: PrisMem evolves executable agent memory programs by optimizing individual capability dimensions rather than overall performance alone.
Method: Dependency-aware selection, history-guided diagnosis, and trace-guided integration target capabilities and combine complementary gains.
Why it matters: PrisMem exceeded the strongest baselines by 10.54 and 7.83 percentage points on BEAM-1M and LongMemEval-M, respectively.

PrisMem targets capabilities, diagnoses revisions, and integrates complementary gains into a unified memory program. Sketched from the article's own text.

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