FutureWeaver: Planning Test-Time Compute for Multi-Agent Systems with Modularized Collaboration
📰 ArXiv cs.AI
Learn how FutureWeaver plans test-time compute for multi-agent systems with modularized collaboration to improve performance
Action Steps
- Implement FutureWeaver to plan test-time compute for multi-agent systems
- Configure modularized collaboration to enable effective agent interaction
- Optimize compute usage under explicit budget constraints using FutureWeaver
- Evaluate the performance of multi-agent systems with FutureWeaver
- Compare the results with existing approaches to allocate compute
Who Needs to Know This
AI engineers and researchers working on multi-agent systems can benefit from this approach to optimize compute usage and improve collaboration
Key Insight
💡 FutureWeaver enables effective collaboration and optimizes compute usage in multi-agent systems
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🤖 Improve multi-agent system performance with FutureWeaver, a novel approach to planning test-time compute! 🚀
Key Takeaways
Learn how FutureWeaver plans test-time compute for multi-agent systems with modularized collaboration to improve performance
Full Article
Title: FutureWeaver: Planning Test-Time Compute for Multi-Agent Systems with Modularized Collaboration
Abstract:
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 F
Abstract:
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 F
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