5 papers
UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration
Xingsheng Chen, Xianpei Mu, Deyu Yi +6
Multivariate time series forecasting is fundamental to numerous domains such as energy, finance, and environmental monitoring, where complex temporal dependencies and cross-variabl…
Efficient Test-Time Scaling via Temporal Reasoning Aggregation
Jiakun Li, Xingwei He, Kefan Li +3
Test-time scaling improves the reasoning performance of large language models but often results in token-inefficient overthinking, where models continue reasoning beyond what is ne…
MODE: Efficient Time Series Prediction with Mamba Enhanced by Low-Rank Neural ODEs
Xingsheng Chen, Regina Zhang, Bo Gao +5
Time series prediction plays a pivotal role across diverse domains such as finance, healthcare, energy systems, and environmental modeling. However, existing approaches often strug…
ConInstruct: Evaluating Large Language Models on Conflict Detection and Resolution in Instructions
Xingwei He, Qianru Zhang, Pengfei Chen +4
Instruction-following is a critical capability of Large Language Models (LLMs). While existing works primarily focus on assessing how well LLMs adhere to user instructions, they of…
TAG-INSTRUCT: Controlled Instruction Complexity Enhancement through Structure-based Augmentation
He Zhu, Zhiwen Ruan, Junyou Su +4
High-quality instruction data is crucial for developing large language models (LLMs), yet existing approaches struggle to effectively control instruction complexity. We present TAG…