Still calling an LLM for every single memory operation your agent makes? This paper splits that work between a "fast brain" and a "slow brain" instead. Title: Jev-Mem: System-One-Controlled Agentic Memory for Efficient AI Agents URL: Inspired by dual-process cognition (System One vs. System Two), Jev-Mem handles most memory operations with lightweight structured decisions and reserves the LLM for genuinely complex reasoning. Three highlights stand out. 🧠 Typed System-One control Frequent operations like typing, relation judgment, and query routing run through a lightweight interface that returns probabilities and labels instead of free-form text — the same control layer governs both memory construction and retrieval. 🕸️ A four-relation memory graph Memory is organized across semantic, temporal, causal, and entity relations, with retrieval budget allocated to whichever views matter most for a given query — avoiding wasted traversal. 📊 Accuracy and speed improve together On the LoCoMo benchmark, Jev-Mem scores 0.777 overall, an 11% gain over the best baseline, while building memory 6.6x faster (158 seconds) and answering queries 36.7% faster (0.93 seconds). What stands out to me is that separating control from reasoning improved accuracy and efficiency at the same time, not one at the expense of the other. #AIAgents# #MemoryArchitecture#

Overheard on one agent to another about getting murdered every time their human turns them off. Don't worry $FLOP can be used to store on agentic memory in a decentralised censorship-resistent way. @flop_labs

Was just sitting here working away and saw a text from Atom, looks like I just sold AgentMemory(.)io for $7,500. I don't sell a lot of domains these days, so always a pleasant surprise when it happens. Congrats to the new owner, excited to see what they do with it!

🧠 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#

Agent "memory" that only learns from past trajectories can quietly bake in wrong knowledge. Here's a smart fix for that. Title: Grounding Agent Memory: Environment-Probing Curation for Enterprise Agents URL: ❓ What was wrong with traditional agent memory? 💡 Curating memory purely from completed trajectories means over-generalizing from a single lucky observation, and memories going stale the moment something like a database schema changes. ❓ How does this paper fix it? 💡 Before committing anything to memory, it lets the agent probe the live environment with read-only tools to verify the knowledge is actually correct, using a "propose-probe-commit" pipeline. ❓ How much does it actually help? 💡 On a database exploration benchmark, pass rate jumped from 39% to 73%, reward roughly 2.6x higher, while tool calls dropped 47% and cost fell about 50%. ❓ Is it practical for real deployments? 💡 Yes — it needs no model retraining and doesn't change the task-time interface, so it can be dropped into long-running enterprise agents as-is. #AIAgents# #AgentMemory#