Agent-Memory

TL;DR Most “agent memory” is a junk drawer: it grows fast, gets noisy, and slows everything down. SEDM (Self‑Evolving Distributed Memory) proposes an auditable, efficiency‑first overhaul. It verifies each candidate memory by replaying the exact run in a Self‑Contained Execution Context (SCEC), assigns an initial utility‑aligned weight, and then self‑schedules what to retrieve next. The result: higher task accuracy with fewer tokens versus strong memory baselines on FEVER and HotpotQA. ...

What if your AI agent could remember the last time it made a mistake—and plan better this time? From Reaction to Reflection: Why Memory Matters Most language model agents today operate like goldfish—brilliant at reasoning in the moment, but forgetful. Whether navigating virtual environments, answering complex questions, or composing multi-step strategies, they often repeat past mistakes simply because they lack a memory of past episodes. That’s where the paper “Agentic Episodic Control” by Zhihan Xiong et al. introduces a critical upgrade to today’s LLM agents: a modular episodic memory system inspired by human cognition. Instead of treating each prompt as a blank slate, this framework allows agents to recall, adapt, and refine prior reasoning paths—without retraining the underlying model. ...