AAAI 2026technical0 citations
Aware First, Think Less: Dynamic Boundary Self-Awareness Drives Significant Gains in Reasoning Efficiency in Large Language Models
Qiguang Chen, Dengyun Peng, Jinhao Liu, Huikang Su, Jiannan Guan, Libo Qin, Wanxiang Che
Abstract
Recent advancements in large language models (LLMs) have greatly improved their ability to perform complex reasoning tasks through Long Chain-of-Thought (CoT). However, this approach often results in substantial redundancy, impairing computational efficiency and causing significant delays in real-time applications. To improve efficiency, current methods often rely on human-defined difficulty priors, which do not align with the LLM
BibTeX
@inproceedings{aaai2026_awarefirstthinkl,
title = {Aware First, Think Less: Dynamic Boundary Self-Awareness Drives Significant Gains in Reasoning Efficiency in Large Language Models},
author = {Qiguang Chen and Dengyun Peng and Jinhao Liu and Huikang Su and Jiannan Guan and Libo Qin and Wanxiang Che},
booktitle = {AAAI 2026},
year = {2026}
}