10 papers
Learning Spatio-Temporal Foundation Models from Pure Synthetic Data
Yutong Feng, Shiyuan Piao, Yutong Xia +5
Spatio-Temporal Foundation Models (STFMs) aim to learn generalizable representations of complex dynamical systems across space and time. However, existing approaches suffer from di…
Activation Steering Induces Emergent Misalignment: A More Comprehensive Evaluation
Qi Cao, Jian Lou, Meiting Liu +4
Activation steering has emerged as a popular inference-time technique for modulating the behavior of large language models (LLMs). By constructing a steering vector from examples o…
TSQAgent: Rating Time Series Data Quality via Dedicated Agentic Reasoning
Shunyu Wu, Dan Li, Haozheng Ye +6
Assessing the quality of time series (TS) data is fundamental yet inherently challenging due to the multifaceted nature of quality dimensions. Recently, large language models (LLMs…
Exploiting Verification-Generation Gap: Test-Time Reinforcement Learning with Confidence-Conditioned Verification
Jiahui Li, Jianfeng Shan, Wenpei Chen +5
Test-time reinforcement learning has emerged as a promising paradigm for enhancing the complex reasoning abilities of large language models in a completely label-free manner. Despi…
Time Series Reasoning via Process-Verifiable Thinking Data Synthesis and Scheduling for Tailored LLM Reasoning
Jiahui Zhou, Dan Li, Boxin Li +6
Time series is a pervasive data type across various application domains, rendering the reasonable solving of diverse time series tasks a long-standing goal. Recent advances in larg…
MsFormer: Enabling Robust Predictive Maintenance Services for Industrial Devices
Jiahui Zhou, Dan Li, Ruibing Jin +5
Providing reliable predictive maintenance is a critical industrial AI service essential for ensuring the high availability of manufacturing devices. Existing deep-learning methods…