🧠 Maybe an AI agent's memory shouldn't be organized the moment it's stored, but the moment it's recalled. Title: Just-in-Time Memory: Learning to Curate Task-Adaptive Memory for LLM Agents (JitMem) URL: ❓ What's the core idea behind JitMem? Traditional agent memory summarizes an experience at write time, right after a task finishes. JitMem skips that: it stores raw trajectories as-is and only synthesizes a task-specific summary at read time, once the new task is actually known. ❓ Why isn't write-time summarization good enough? You have to decide what matters before you know which future task will need it, so valuable details often get discarded for good. Worse, the same experience can teach different lessons depending on the task, but a write-time summary can only be one generic version. ❓ How is it trained? A curator model that writes summaries from the current task and past trajectories is trained directly on the success reward the executor gets from using that summary. No need to wait for future queries, which makes optimization much simpler. ❓ How much does it help? Across ALFWorld, WebShop, and τ²-bench, JitMem beats the strongest baseline by 16.2, 16.3, and 3.9 points respectively. Even the untrained version is already strong, showing that read-time curation itself is a major source of the gain. #AIAgents# #AgentMemory#