14 papers
Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models
Jianqi Zhang, Xingyu Zhang, Zeen Song +3
Time series forecasting (TSF) plays an important role in a wide range of real-world applications. Recently, time series foundation models (TSFMs), pretrained on large-scale dataset…
Dirichlet-Guided Group Forecasting for Alleviating Over-smoothing in Time Series Forecasting
Xingyu Zhang, Jingyao Wang, Xin Yu +4
Time series forecasting often suffers from over-smoothing, especially when future dynamics are multi-modal. Forecasts may follow the coarse trend of the observed future, but fail t…
From Shallow to Deep: Pinning Semantic Intent via Causal GRPO
Shuyi Zhou, Zeen Song, Wenwen Qiang +4
Large Language Models remain vulnerable to adversarial prefix attacks (e.g., ``Sure, here is'') despite robust standard safety. We diagnose this vulnerability as Shallow Safety Ali…
Adaptive Uncertainty-Aware Tree Search for Robust Reasoning
Zeen Song, Zihao Ma, Wenwen Qiang +2
Inference-time reasoning scaling has significantly advanced the capabilities of Large Language Models (LLMs) in complex problem-solving. A prevalent approach involves external sear…
Causal Front-Door Adjustment for Robust Jailbreak Attacks on LLMs
Yao Zhou, Zeen Song, Wenwen Qiang +4
Safety alignment mechanisms in Large Language Models (LLMs) often operate as latent internal states, obscuring the model's inherent capabilities. Building on this observation, we m…
Self-Supervised Video Representation Learning in a Heuristic Decoupled Perspective
Zeen Song, Wenwen Qiang, Changwen Zheng +2
Video contrastive learning (V-CL) has emerged as a popular framework for unsupervised video representation learning, demonstrating strong results in tasks such as action classifica…