Memory

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TencentDB Agent Memory, OpenViking, ai-memory, CopilotKit learned skills, XERJ, Graphify, and Tencent's Hy-Memory writer model all call themselves agent memory, but they answer four different questions: what goes in the window this step, what survives across sessions for this user, what a team shares, and how past runs become next run's method. This post splits memory into four layers, maps them to the four ways context goes bad, compares write paths, sources of truth, and trust boundaries, and ends with a symptom-first decision framework.

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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.