1 citations · 1 across the 3 of their papers we have counts for
11 papers
MMTS-BENCH: A Comprehensive Benchmark for Time Series Understanding and Reasoning
Yao Yin, Zhenyu Xiao, Musheng Li +7
Time series data are central to domains such as finance, healthcare, and cloud computing, yet existing benchmarks for evaluating various large language models (LLMs) on temporal ta…
Learning When to Look: A Disentangled Curriculum for Strategic Perception in Multimodal Reasoning
Siqi Yang, Zilve Gao, Haibo Qiu +5
Multimodal Large Language Models (MLLMs) demonstrate significant potential but remain brittle in complex, long-chain visual reasoning tasks. A critical failure mode is "visual forg…
AgentExpt: Automating AI Experiment Design with LLM-based Resource Retrieval Agent
Yu Li, Lehui Li, Lin Chen +3
Large language model agents are becoming increasingly capable at web-centric tasks such as information retrieval, complex reasoning. These emerging capabilities have given rise to…
WeightFlow: Learning Stochastic Dynamics via Evolving Weight of Neural Network
Ruikun Li, Jiazhen Liu, Huandong Wang +2
Modeling stochastic dynamics from discrete observations is a key interdisciplinary challenge. Existing methods often fail to estimate the continuous evolution of probability densit…
Predicting the Dynamics of Complex System via Multiscale Diffusion Autoencoder
Ruikun Li, Jingwen Cheng, Huandong Wang +2
Predicting the dynamics of complex systems is crucial for various scientific and engineering applications. The accuracy of predictions depends on the model's ability to capture the…
AgentSwift: Efficient LLM Agent Design via Value-guided Hierarchical Search
Yu Li, Lehui Li, Zhihao Wu +5
Large language model (LLM) agents have demonstrated strong capabilities across diverse domains, yet automated agent design remains a significant challenge. Current automated agent…