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A memory filter cuts the failure rate of an agent by 8.8 percentage points

The paper reports that the 2 filters cause more personalization failures.

Claimed, not confirmed

This is a brief. We point to the report and do not rewrite it. Read it at the source below.

Admission cut failure; presentation did not Two columns. Admission, what enters context: tightening it with presentation held fixed cut cross-domain failure by 17.5 points (p = 1.6e-4), shown as a tall red bar. Presentation, how it is expressed: no gain survived correction across two renderings of the same adjudicated outputs, shown as an empty dashed outline. Admission Presentation What enters context How it is expressed points fewer cross-domain failures No gain survived Presentation held fixed Same adjudicated outputs, two renderings 17.5 P = 1.6E-4 CORRECTION APPLIED
With presentation held fixed, admitting less memory cut cross-domain failure by 17.5 points; no presentation change held up under correction.

Sources

  1. What to Admit and How to Present: Governing Persistent Memory in LLM Agentsarxiv.org
AI MATTER · NEWS · AI MATTER · NEWS ·9 OCT2026

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