DAEDALUS: Bootstrapping Agent Memory from Self-Generated Tasks

Key points

• 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.1
• DAEDALUS pairs two agents: an explorer that interacts with the environment to generate challenging yet solvable tasks, and a solver that attempts them.1
• We show that performance gains already emerge with a small exploration budget, and that its heuristics also benefit agents from other model families.1
• Beyond memory construction, we find that the tasks generated by DAEDALUS can serve as a proxy for benchmark tasks when ranking models by performance.1

Sources (1)

• [1]DAEDALUS: Bootstrapping Agent Memory from Self-Generated Tasks
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 09:46 AM

  We present DAEDALUS, a method for bootstrapping reusable agent memory from self-generated practice without existing tasks or oracle verifiers.

  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.

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