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Every design choice in Atlaso started as a result we ran — memory retrieval, agent coordination, timing. Published with full methodology, judge prompts, and raw run logs.
Your AI wakes up blank every session. Ambient Memory hands it an orientation block built from your own memories — what changed, what you keep returning to, what's still unsettled, and where you're headed — before you type a word.
A reader-matched head-to-head on LongMemEval-S (n=500): the same Qwen 3.5-9B reader answers for every system. Atlaso beats mem0 by +6.6 points (p≈0.007) across three independent judges, with zero LLM calls at ingestion. Letta ties; the losses are published too.
An adaptive gate that turns on coordination only under population-level distress outperforms both always-on and always-off baselines by +34% on the hard task across 10 seeds — and the same principle transfers to LLM agent collectives.
On 10 seeds × 300 tasks, the +11.87pp lift on a 9B model decomposes cleanly: ~94% comes from scope-matched retrieval, ~6% from the trained deposit format. All 10 seeds positive, sign-test p = 0.00195.
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