GRADE: Efficient Multi-Agent Reasoning via Gated Routing and Adaptive Depth
Score
81论文提出了新的门控机制和协同训练方法，解决了多智能体效率的核心问题，信息增量大。
实验设计全面，消融分析详细，结果有力，方法描述清晰。
热替换和高效推理机制对实际部署有直接价值，但需要特定基础设施。
论文日期为2026年7月（未来），但内容紧跟大模型推理效率热点，时效性极高。
arXiv预印本，但来自权威学科分类，且方法可信度高。
Base 89.5 × T2 Source weight ×0.9 = 81
Curation line 70Selected
Why selected
The paper presents novel methods, solid experiments, significant efficiency gains, and practical hot-swapping, making it a exemplary work.
AI commentary
「这篇论文对于研究多智能体协作和大模型推理效率的从业者来说很有价值。它提出了实用的门控机制和训练方法，并支持模型热替换，有望降低实际系统的部署成本。尤其是CoGRPO的无评论家训练思路，或可启发类似场景。」
Quick overview
This paper proposes GRADE, a hierarchical multi-agent system where four lightweight learned gates jointly govern agent selection, hierarchy depth, inter-agent communication, and branch pruning. Training uses CoGRPO, a critic-free algorithm. With ~17B average active parameters, GRADE outperforms all baselines on GSM8K, MMLUPro, and GPQA, surpassing the strongest baseline by 4.8 points on MMLUPro at half the active compute. Ablations identify hierarchy and masked cross-attention as key contributors, and show per-agent calibration is necessary for safe hot-swapping.
- GRADE uses four gates to dynamically select agents, control reasoning depth, decide communication timing, and prune branches.
- CoGRPO training method is critic-free and adapted to multi-agent hierarchies.
- With ~17B active parameters, GRADE outperforms stronger baselines on multiple benchmarks, doubling compute efficiency.
- Per-agent calibration enables hot-swapping of expert models at inference without retraining.
Full text
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