4 papers
CausalVAD: De-confounding End-to-End Autonomous Driving via Causal Intervention
Jiacheng Tang, Zhiyuan Zhou, Zhuolin He +3
Planning-oriented end-to-end driving models show great promise, yet they fundamentally learn statistical correlations instead of true causal relationships. This vulnerability leads…
Reward Prediction with Factorized World States
Yijun Shen, Delong Chen, Xianming Hu +4
Agents must infer action outcomes and select actions that maximize a reward signal indicating how close the goal is to being reached. Supervised learning of reward models could int…
Pruning Long Chain-of-Thought of Large Reasoning Models via Small-Scale Preference Optimization
Bin Hong, Jiayu Liu, Kai Zhang +3
Recent advances in Large Reasoning Models (LRMs) have demonstrated strong performance on complex tasks through long Chain-of-Thought (CoT) reasoning. However, their lengthy outputs…
End-to-End Graph Flattening Method for Large Language Models
Bin Hong, Jinze Wu, Jiayu Liu +5
In recent years, the breakthrough of Large Language Models (LLMs) offers new ideas for achieving universal methods on graph data. The common practice of converting graphs into natu…