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      "content": "[cron:4821c83c-a376-460c-b7a4-f68807465cea uni-mas-memory-weekly-distill] WEEKLY_MEMORY_DISTILL: Run UNI MAS Memory V1 weekly distill. Use /data/workspace/memory as the only working directory for new artifacts. Read the last 7 daily notes under /data/workspace/memory/*.md (exclude weekly/archive/templates). Create/overwrite /data/workspace/memory/weekly/YYYY-Www.md following /data/workspace/memory/templates/weekly_distill_template.md. Update /data/workspace/MEMORY.md: keep it tech-first; maintain Hot Rules (<=10), Safety Guardrails, and portable SOPs. Move deprecated/outdated rules into /data/workspace/memory/archive/YYYY-MM.md with DEPRECATED date. Enforce guardrails: never store secrets/keys/certs/backups; do not modify config/backup/ssl; do not take destructive actions. After completion, post a short digest in chat: new/updated/deprecated rules + paths. Current time: Saturday, June 13th, 2026 - 12:00 PM (UTC) Reference UTC: 2026-06-13 12:00 UTC Use the message tool if you need to notify the user directly for the current chat. If you do not send directly, your final plain-text reply will be delivered automatically.",
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      "content": "{ \"query\": \"weekly memory distill process\", \"provider\": \"perplexity\", \"model\": \"perplexity/sonar-pro\", \"tookMs\": 4375, \"externalContent\": { \"untrusted\": true, \"source\": \"web_search\", \"provider\": \"perplexity\", \"wrapped\": true }, \"content\": \"\\n<<<EXTERNAL_UNTRUSTED_CONTENT id=\\\"5384731c7a385d82\\\">>>\\nSource: Web Search\\n---\\nIf by **“weekly memory distill process”** you mean a **weekly distillation workflow for model memory/knowledge**, the closest established idea is a **teacher-student distillation loop**: collect the model’s outputs or reasoning traces, compress the useful information into a smaller student model, then repeat on a regular cycle to refresh what the student has learned[3][5][8].\\n\\nA practical weekly process usually looks like this:\\n\\n- **Collect** examples from the week: prompts, model responses, errors, successful solutions, and feedback[3].\\n- **Select** the most useful signals to distill:\\n - soft output probabilities or logits from a stronger teacher model[4][8]\\n - intermediate reasoning/rationales[5]\\n - successful and failed attempts plus lessons learned[3]\\n- **Train** the student on those targets with a distillation loss, often combining standard task ...",
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      "content": "Based on the search results, the weekly memory distill process involves collecting examples from the week, selecting the most useful signals to distill, training the student on those targets with a distillation loss, evaluating whether the distilled student retained the important skills, and refreshing weekly by adding new cases and retraining. The process can be turned into a concrete weekly checklist, a training pipeline, or a pseudocode implementation.",
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