Memory

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Reading UC Berkeley arXiv:2609.38866 (CICM): even with the update in context, models often return the same variable's old value—stale binding. Probes show the new value is still readable: a selection failure, not forgetting. Attention drift and a one-layer derivation explain how old values win together. GPT-5.6 Sol: 9/40 on 2,048-step logs, 40/40 with a snapshot. Includes an AI memory and agent-state checklist.

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A mechanism read of arXiv:2609.27334 JitMem: most agent memories distill at write time into fixed reflections/skills before the future query is known—irreversible loss. Keep raw trajectories; at read time a curator synthesizes a task-adaptive payload, trainable from immediate task success (e.g. GRPO). +16.2/+16.3/+3.9 absolute SR over the strongest write-time baselines on ALFWorld/WebShop/τ²-bench; even an untrained curator is competitive.

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A deep read of arXiv:2609.23986 Jev-Mem—separating memory organize/retrieve/stop into a System-One control plane—contrasted with this site’s Jev decision line and Growing Harness “sink control into code,” with checked LoCoMo numbers and a reusable design checklist.

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A teardown of affaan-m/ECC—how Skills, Hooks, Instincts, Memory Vault, and AgentShield tackle performance, memory, and security; what actually plugs into Claude Code/Codex/Cursor; what enterprises can copy versus roadmap hype.