11 papers
CDFM: Towards a General-Purpose Causal Discovery Foundation Model
Jie Qiao, Ruichu Cai, Zijian Li +6
The paper proposes CDFM, a foundation model trained on synthetic causal graphs that can infer causal structures in a zero‑shot manner across diverse domains, using a variational fr…
Rethinking Zero-Shot Time Series Classification: From Task-specific Classifiers to In-Context Inference
Juntao Fang, Shifeng Xie, Shengbin Nie +7
The zero-shot evaluation of time series foundation models (TSFMs) for classification typically uses a frozen encoder followed by a task-specific classifier. However, this practice…
Hierarchical Action Learning for Weakly-Supervised Action Segmentation
Junxian Huang, Ruichu Cai, Hao Zhu +5
Humans perceive actions through key transitions that structure actions across multiple abstraction levels, whereas machines, relying on visual features, tend to over-segment. This…
ENTRA: Entropy-Based Redundancy Avoidance in Large Language Model Reasoning
Ruichu Cai, Haopeng Du, Qingwen Lin +3
Large Reasoning Models (LRMs) often suffer from overthinking, generating unnecessarily long reasoning chains even for simple tasks. This leads to substantial computational overhead…
Temporal Latent Variable Structural Causal Model for Causal Discovery under External Interferences
Ruichu Cai, Xiaokai Huang, Wei Chen +2
Inferring causal relationships from observed data is an important task, yet it becomes challenging when the data is subject to various external interferences. Most of these interfe…
CMCTS: A Constrained Monte Carlo Tree Search Framework for Mathematical Reasoning in Large Language Model
Qingwen Lin, Boyan Xu, Guimin Hu +4
This paper introduces the Constrained Monte Carlo Tree Search (CMCTS) framework to enhance the mathematical reasoning capabilities of Large Language Models (LLM). By incorporating…