3 papers
cs.AI2026
Formula-R1: Incentivizing LLM Reasoning over Complex Tables with Numerical Computation via Formula-Driven Reinforcement Learning
Lang Cao, Jingxian Xu, Hanbing Liu +5
Tables are a fundamental medium for organizing and analyzing data, making table reasoning a critical capability for intelligent systems. Although large language models (LLMs) exhib…
cs.CL2026
DS-Instruct: Domain-Specific Data Synthesis for Large Language Models Instruction Tuning
Ruiyao Xu, Noelle I. Samia, Han Liu
Adapting Large Language Models (LLMs) to specialized domains requires high-quality instruction tuning datasets, which are expensive to create through human annotation. Existing dat…
cs.CL2025
TwT: Thinking without Tokens by Habitual Reasoning Distillation with Multi-Teachers' Guidance
Jingxian Xu, Mengyu Zhou, Weichang Liu +3
Large Language Models (LLMs) have made significant strides in problem-solving by incorporating reasoning processes. However, this enhanced reasoning capability results in an increa…