Memory Module
A core component of AI agents that provides contextual continuity. It is divided into short-term memory, which covers the conversation history of the current session, and long-term memory, which consists of persistent storage retrievable across sessions. This module is a key mechanism enabling agents to evolve from single-turn question answering into sustained collaborative interactions.
Core Facts
Timeline (last 90 days)
In ablation experiments, the Seer performed best with "memory but no deductive reasoning," and was actually worst under the full architecture (deductive reasoning + memory).
Adding long-term memory and summarization modules alone made the villagers' win rate lower than the chain-of-thought baseline (villagers won about four out of ten games, winning one fewer game with memory alone).
Most current mainstream Agent frameworks use a single-layer control structure: one scheduler is responsible for planning, calling tools, observing results, and proceeding to the next round.
All Facts (4)
Persistent memory is typically divided into three levels: short-term working memory, episodic memory, and semantic memory
65%UnverifiedIn ablation experiments, the Seer performed best with "memory but no deductive reasoning," and was actually worst under the full architecture (deductive reasoning + memory).
50%UnverifiedAdding long-term memory and summarization modules alone made the villagers' win rate lower than the chain-of-thought baseline (villagers won about four out of ten games, winning one fewer game with memory alone).
50%UnverifiedMost current mainstream Agent frameworks use a single-layer control structure: one scheduler is responsible for planning, calling tools, observing results, and proceeding to the next round.
50%