FutureWeaver: Planning Test-Time Compute for Multi-Agent Systems with Modularized Collaboration
arXiv:2512.11213v2 Announce Type: replace-cross 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-age
The proliferation of advanced multi-agent AI systems necessitates optimized resource allocation to manage their increasing complexity and computational demands effectively.
This development allows for more efficient and scalable deployment of multi-agent AI, making sophisticated AI collaborations practical under real-world budget constraints.
The ability to plan and optimize compute for multi-agent systems means that complex AI deployments can now run more effectively and economically, expanding the viable applications for advanced AI.
- · AI developers
- · Cloud providers
- · Enterprises adopting AI
- · AI research institutions
- · Inefficient AI frameworks
- · Organizations with static compute infrastructure
More robust and economically feasible multi-agent AI applications will emerge across various sectors.
This framework could accelerate the development of highly autonomous AI systems capable of complex decision-making and task execution.
Sophisticated AI agents, optimized for test-time compute, might begin to automate a wider array of white-collar tasks, impacting labor markets significantly.
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Read at arXiv cs.CL