Paper page - PREPING: Building Agent Memory without Tasks

Source: https://huggingface.co/papers/2605.13880

Abstract

Preping is a framework for pre-task memory construction that uses proposer-guided synthetic practice to improve agent performance in new environments with reduced deployment costs.

Agent memoryis typically constructed either offline from curated demonstrations or online from post-deployment interactions. However, regardless of how it is built, an agent faces acold-start gapwhen first introduced to a new environment without any task-specific experience available. In this paper, we studypre-task memoryconstruction: whether an agent can buildprocedural memorybefore observing any target-environment tasks, using only self-generatedsynthetic practice. Yet, synthetic interaction alone is insufficient, as without controlling what to practice and what to store, synthetic tasks become redundant, infeasible, and ultimately uninformative, and memory further degrades quickly due to unfiltered trajectories. To overcome this, we present Preping, aproposer-guided memory constructionframework. At its core isproposer memory, a structured control state that shapes future practice. A Proposer generates synthetic tasks conditioned on this state, aSolverexecutes them, and aValidatordetermines which trajectories are eligible for memory insertion while also providing feedback to guide future proposals. Experiments on AppWorld, BFCL v3, and MCP-Universe show that Preping substantially improves over a no-memory baseline and achieves performance competitive with strong playbook-based methods built from offline or online experience, with deployment cost 2.99times lower on AppWorld and 2.23times lower on BFCL v3 than onlinememory construction. Further analyses reveal that the main benefit does not come from synthetic volume alone, but from proposer-side control over feasibility, redundancy, and coverage, combined with selective memory updates.

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