TMAS: Scaling Test-Time Compute via Multi-Agent Synergy
📰 ArXiv cs.AI
Learn how TMAS scales test-time compute via multi-agent synergy to improve large language models' reasoning ability
Action Steps
- Implement TMAS to scale test-time compute
- Configure multi-agent synergy to coordinate parallel reasoning trajectories
- Apply refinement rounds and verification-based feedback to improve model accuracy
- Test TMAS on large language models to evaluate its effectiveness
- Compare TMAS with existing structured test-time scaling methods to assess its advantages
Who Needs to Know This
AI researchers and engineers working on large language models can benefit from this knowledge to improve their models' performance and scalability
Key Insight
💡 TMAS enables effective coordination of parallel reasoning trajectories, leading to improved model performance and scalability
Share This
🚀 Scale test-time compute with TMAS and improve large language models' reasoning ability! 💡
Key Takeaways
Learn how TMAS scales test-time compute via multi-agent synergy to improve large language models' reasoning ability
Full Article
Title: TMAS: Scaling Test-Time Compute via Multi-Agent Synergy
Abstract:
arXiv:2605.10344v1 Announce Type: new Abstract: Test-time scaling has become an effective paradigm for improving the reasoning ability of large language models by allocating additional computation during inference. Recent structured approaches have further advanced this paradigm by organizing inference across multiple trajectories, refinement rounds, and verification-based feedback. However, existing structured test-time scaling methods either weakly coordinate parallel reasoning trajectories or
Abstract:
arXiv:2605.10344v1 Announce Type: new Abstract: Test-time scaling has become an effective paradigm for improving the reasoning ability of large language models by allocating additional computation during inference. Recent structured approaches have further advanced this paradigm by organizing inference across multiple trajectories, refinement rounds, and verification-based feedback. However, existing structured test-time scaling methods either weakly coordinate parallel reasoning trajectories or
DeepCamp AI