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

• URL: http://arxiv.org/abs/2511.09345v1
• Date: Thu, 13 Nov 2025 01:47:53 GMT
• Title: Seer Self-Consistency: Advance Budget Estimation for Adaptive Test-Time Scaling
• Authors: Shiyu Ji, Yixuan Wang, Yijun Liu, Qingfu Zhu, Wanxiang Che,
• Abstract summary: Test-time scaling improves the inference performance of Large Language Models (LLMs) but also incurs substantial computational costs.<n>We propose SeerSC, a dynamic self-consistency framework that simultaneously improves token efficiency and latency.
• Score: 55.026048429595384
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Test-time scaling improves the inference performance of Large Language Models (LLMs) but also incurs substantial computational costs. Although recent studies have reduced token consumption through dynamic self-consistency, they remain constrained by the high latency of sequential requests. In this paper, we propose SeerSC, a dynamic self-consistency framework that simultaneously improves token efficiency and latency by integrating System 1 and System 2 reasoning. Specifically, we utilize the rapid System 1 to compute the answer entropy for given queries. This score is then used to evaluate the potential of samples for scaling, enabling dynamic self-consistency under System 2. Benefiting from the advance and accurate estimation provided by System 1, the proposed method can reduce token usage while simultaneously achieving a significant decrease in latency through parallel generation. It outperforms existing methods, achieving up to a 47% reduction in token consumption and a 43% reduction in inference latency without significant performance loss.

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