aiqbench analysis
FutureWeaver addresses the challenge of scaling test-time computation in multi-agent systems by introducing a framework that allocates inference resources under strict budget constraints. By utilizing self-play reflection to generate reusable collaboration modules and a dual-level planning architecture, the system optimizes inference trajectories without requiring additional model training. This approach offers a principled method for enhancing multi-agent coordination efficiency, which is critical for deploying complex autonomous workflows in resource-limited environments.
FutureWeaver: Planning Test-Time Compute for Multi-Agent Systems with Modularized Collaboration
arXiv:2512.11213v2 Announce Type: replace Abstract: Scaling test-time computation has been shown to significantly improve large language model (LLM) performance without additional training. However, extending these techniques to multi-agent systems remains challenging: existing approaches lack principled mechanisms for allocating compute to enable effective collaboration, scaling coordination itself, or optimizing compute usage under explicit budget constraints. To address this gap, we propose FutureWeaver, a framework for planning and optimizing test-time compute allocation in multi-agent systems under fixed budgets. It introduces collaboration modules, formalized as modular, callable functions that encapsulate reusable multi-agent workflows and are automatically induced via self-play reflection from recurring interaction patterns. Building on these modules, it employs \emph{a dual-level planning architecture} that jointly performs short-horizon action selection and long-horizon abstract lookahead to optimize inference trajectories under budget constraints. Experiments on complex agent benchmarks demonstrate that FutureWeaver consistently outperforms baselines across diverse budget settings, validating its effectiveness for multi-agent collaboration in inference-time optimization.
Abstract:Scaling test-time computation has been shown to significantly improve large language model (LLM) performance without additional training. However, extending these techniques to multi-agent systems remains challenging: existing approaches lack principled mechanisms for allocating compute to enable effective collaboration, scaling coordination itself, or optimizing compute usage under explicit budget constraints. To address this gap, we propose FutureWeaver, a framework for planning and optimizing test-time compute allocation in multi-agent systems under fixed budgets. It introduces collaboration modules, formalized as modular, callable functions that encapsulate reusable multi-agent workflows and are automatically induced via self-play reflection from recurring interaction patterns. Building on these modules, it employs \emph{a dual-level planning architecture} that jointly performs short-horizon action selection and long-horizon abstract lookahead to optimize inference trajectories under budget constraints. Experiments on complex agent benchmarks demonstrate that FutureWeaver consistently outperforms baselines across diverse budget settings, validating its effectiveness for multi-agent collaboration in inference-time optimization.
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2512.11213 [cs.AI]
(or arXiv:2512.11213v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2512.11213 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 243 words