Year: 2026
Type: conference-paper
Abstract: Test-Time Scaling (TTS) has emerged as an effective paradigm for improving the reasoning performance of large language models (LLMs).However, existing methods -most notably majority voting and heuristic token-level scoringtreat reasoning traces or tokens equally, thereby being susceptible to substantial variations in trajectory quality and localized logical failures.In this work, we introduce Chronos, a lightweight and plug-and-play chronological reasoning scorer that models each trajectory as a time series.Specifically, Chronos learns to capture trajectory features of token probabilities, assigns quality scores accordingly, and employs a weighted voting mechanism.Extensive evaluations on both in-domain and out-of-domain benchmarks demonstrate that Chronos consistently delivers substantial gains across a variety of models, with negligible computational overhead.Notably, Chronos@128 achieves relative improvements of 34.21% over Pass@1 and 22.70% over Maj@128 on HMMT25 using Qwen3-4B-Thinking-2507, highlighting its effectiveness.Our code is released on https: //github.com/Hizkai/Chronos.
Institution: University of Science and Technology of China
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G3184738894
OpenAlex ID: W7166837648