KAIST Team Proposes Preping Framework, Reports Nearly 3x Lower Deployment Cost for Agent Cold-Start
A KAIST research team released the Preping framework, which builds agent memory through synthetic practice. Self-reported data shows deployment costs are 2.99x lower on AppWorld and 2.23x lower on BFCL v3 compared to online memory construction, with performance competitive with experience-based methods.
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A research team from KAIST (Yumin Choi et al.) updated their paper "PREPING: Zero-Task Agent Memory Construction via Self-Practice" on October 14, 2026, proposing a framework to build procedural memory for LLM agents without observing target-environment tasks. The study addresses the "cold-start" problem where agents lack task-specific experience upon entering new environments.
The core mechanism of Preping is "proposer-guided memory construction." It uses a structured control state (proposer memory) to guide the generation, execution, and validation of synthetic tasks, filtering out redundant and infeasible trajectories. Experiments show that this method achieves performance competitive with strong baseline methods relying on offline or online experience across AppWorld, BFCL v3, and MCP-Universe benchmarks.
Key metrics come from author-run tests: On AppWorld, Preping's deployment cost is 2.99x lower than online memory construction methods; on BFCL v3, it is 2.23x lower. This cost advantage stems primarily from active control over feasibility, redundancy, and coverage, rather than simply increasing synthetic data volume. Results have not yet been independently reproduced and represent first-party benchmark data from a preprint stage.