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A developer shares their experience building an AI agent with memory using the Anthropic SDK and TypeScript, explaining the differences between working, episodic, semantic, and procedural memory and the challenges of scaling memory for production.
Procedural Memory Distillation (PMD) converts cross-episode signals from reinforcement learning rollouts into reusable procedural memory that is distilled into the policy weights during training, enabling self-improving language models without memory at inference. Experiments show PMD outperforms SDPO by 3.8-5.5% on SCIKNOWEVAL and 7.9-13.6% on LIVECODEBENCH.
This paper introduces Neural Procedural Memory (NPM), a training-free framework that stores procedural skills as activation steering vectors distilled from contrastive historical experience, enabling LLM agents to execute skills without relying solely on textual instructions.
Introduces AFTER, a benchmark of 382 enterprise tasks to evaluate procedural memory in LLM agents, showing that skill transfer improves performance across tasks, roles, and model backbones, with some skills generalizing broadly while others specialize.