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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 four-judge head-to-head on LongMemEval-S (n=500) with a shared reader. Atlaso beats the leading open-source memory system by +9.8 to +14.8 percentage points across four independent judges, at 1.20× lower cost per query.
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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