Paper Finds Personal Agents Get Worse as Memory Notes Pile Up
rohanpaul_ai · x · 2026-10-05
An arXiv paper (Zeyu Gan et al.) formalizes harness evolution for personal agents as a learning problem. Key findings: more memory notes help only up to a point, then agents start missing rules; anything requiring exact counting or tracking should be done in code, not memory. The authors introduce a preference-oriented benchmark and explain observed limits via approximation, generalization, and optimization errors.
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