RankGuide: Tensor-Rank-Guided Routing and Steering for Efficient Reasoning
A novel framework leveraging tensor-rank signals to optimize collaborative reasoning between small and large models.
Large reasoning models (LRMs) enhance problem-solving capabilities by generating explicit multi-step chains of thought (CoT) reasoning; however, they incur substantial inference latency and computational overhead. To mitigate this issue, recent works have explored model collabora...
ArXiv CS.AI
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RankGuide bridges the efficiency gap in AI reasoning by using hidden-state analysis to selectively deploy large models only when smaller ones falter. This approach significantly lowers computational overhead while maintaining high-performance output, making complex reasoning tasks more practical for real-time applications.
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