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DAEDALUS: Bootstrapping Agent Memory from Self-Generated Tasks

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Briefings

DAEDALUS is a method for bootstrapping reusable agent memory from self-generated tasks, improving success rates by up to 15.9 points and pass rates by up to 2.2x on AppWorld, τ^2-bench, and AutomationBench. It pairs an explorer and solver agents to derive heuristics from solver failures, which are then consolidated into a memory bank for test-time use. Performance gains emerge even with a small exploration budget, and the generated tasks serve as a proxy for benchmark tasks.

Key points

• DAEDALUS pairs an explorer agent and a solver agent to generate tasks and derive heuristics from solver failures.
• Accepted heuristics are consolidated into a memory bank for test-time use, improving success rates by up to 15.9 points on AppWorld.

Context

LLM agents lack operational knowledge in new environments, often repeating mistakes without memory. DAEDALUS addresses this by bootstrapping memory from self-generated tasks, improving performance on benchmark suites like AppWorld and AutomationBench.

Limitations

Performance gains emerge with a small exploration budget, and ablations reveal solver traces provide key information for heuristics. Code and artifacts are available via GitHub, but specific inference costs and model family benefits remain partially unquantified.

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