DAEDALUS: Bootstrapping Agent Memory from Self-Generated Tasks

Excerpt

arXiv:2610.08048v1 Announce Type: cross Abstract: LLM agents often lack the operational knowledge to act reliably in new environments, as they must discover specific tool behaviors or environment conventions on their own. Without memory of past attempts, they repeat the same mistakes across tasks, leading to more task failures and longer trajectories. To address this, agentic systems typically rely on human-written guidelines or on procedural memory built from training tasks and an oracle verifi