PREPING: Building Agent Memory without Tasks
The paper addresses the cold-start problem for agents in new environments by introducing PREPING, a proposer-guided memory construction framework that uses self-generated synthetic practice to build procedural memory before task exposure. Experiments show competitive performance with lower deployment cost compared to online methods.
[2605.13880] PREPING: Building Agent Memory without Tasks
[Submitted on 11 May 2026]
Title:PREPING: Building Agent Memory without Tasks
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Abstract:Agent memory is typically constructed either offline from curated demonstrations or online from post-deployment interactions. However, regardless of how it is built, an agent faces a cold-start gap when first introduced to a new environment without any task-specific experience available. In this paper, we study pre-task memory construction: whether an agent can build procedural memory before observing any target-environment tasks, using only self-generated synthetic 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, a proposer-guided memory construction framework. At its core is proposer memory, a structured control state that shapes future practice. A Proposer generates synthetic tasks conditioned on this state, a Solver executes them, and a Validator determines 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 $2.99\times$ lower on AppWorld and $2.23\times$ lower on BFCL v3 than online memory 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.
Comments: Preprint
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as: arXiv:2605.13880 [cs.AI]
(or arXiv:2605.13880v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.13880
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yumin Choi [view email] [v1] Mon, 11 May 2026 04:34:43 UTC (1,166 KB)
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