Min-Seek is a novel sequential test-time scaling method designed to significantly improve and stabilize the accuracy of large reasoning models. It efficiently manages KV pairs to extend reasoning beyond context limits, removing the need for reasoning length fine-tuning.
Min-Seek is a new technique that makes large AI models better at complex reasoning tasks by allowing them to "think" longer without getting confused or less accurate. It also makes this process more efficient and removes the need for tedious adjustments, even letting models handle very long inputs.
Min-Seek
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