Memory-Augmented Agents Solve Long-Horizon Planning Tasks in Simulated Research Environments

A research team at CMU has demonstrated that language-model agents augmented with external episodic memory stores can solve planning tasks spanning hundreds of steps in simulated laboratory environments, significantly outperforming agents relying solely on in-context learning. The approach uses a retrieve-then-reason pipeline that selectively recalls relevant past interactions to inform current decisions. Their agent achieved an 84% success rate on a 200-step chemistry experiment simulation, versus 31% for the baseline.