How Much Memory Does Your Agent Actually Need?
A previous post compared ALTK-Evolve with ACE, focusing on how self-distilled guidelines affect accuracy and cost. This follow-up examines the optimal amount of information to provide an agent. Data from AppWorld tasks, graded by two percentage-based metrics, shows that models like DeepSeek-V3.2 and Claude Opus 4.6 benefit significantly from a full guideline set, achieving improvements of +9.5 and +4.1 respectively. In contrast, GLM-5 shows no improvement with a full guideline set, indicating saturation.
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IngestedOffset at this time: UTC+0Aug 18, 2026, 20:00 UTC
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- Aug 18, 2026, 20:00
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