Paper Club: Memory, Modularity, and Safety

About the event

Technical Note: This event is intended for participants with a technical background. We strongly encourage reading the paper ahead of time to fully engage with the discussion. Recent AI progress has leaned on ever‑larger monolithic LLMs, where core intelligence functions like memory, reasoning and planning are densely entangled, resulting in reduced interpretability and control. In this week's paper club, we explore an alternative trajectory: constructing intelligence through modular architectures that separate concerns (planning, execution, memory, retrieval policy) to expose natural governance and safety levers, without compromising on performance. Our focal case study is "Memento: Fine-tuning LLM Agents without Fine-tuning LLMs" (https://arxiv.org/abs/2508.16153), a planner-executor agent with an explicit episodic Case Bank and a tiny (2‑layer MLP) learned retrieval module that optimizes which past cases to surface rather than fine‑tuning the underlying LLMs. In this system, we can see episodic memories (past experiences) in plaintext, and trace its process of storage and retrieval which influences its decision-making process.