Seer Self-Consistency: Advance Budget Estimation for Adaptive Test-Time Scaling

arXiv — cs.CL•Thursday, November 13, 2025 at 5:00:00 AM

On November 13, 2025, a new paper titled 'Seer Self-Consistency: Advance Budget Estimation for Adaptive Test-Time Scaling' was submitted to arXiv, presenting a novel framework called SeerSC. This framework aims to optimize the inference performance of Large Language Models (LLMs) by integrating System 1 and System 2 reasoning. By leveraging rapid computations from System 1 to assess answer entropy, SeerSC enables dynamic self-consistency, which significantly reduces token usage and inference latency. The results indicate a remarkable 47% decrease in token consumption and a 43% reduction in latency, all without substantial performance loss. This development is crucial in the field of AI, as it not only enhances the efficiency of LLMs but also addresses the high computational costs that have been a barrier to their widespread adoption.

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