5 papers
What Do Current Systematic Generalization Tasks Miss? A Reasoning-Centered Analysis
Chengwen Qi, Deheng Ye, Yatao Bian
Systematic generalization, the ability to solve novel problems by recombining known atomic elements, is central to human intelligence but difficult to study rigorously under contro…
From Coefficients to Directions: Rethinking Model Merging with Directional Alignment
Zhikang Chen, Sen Cui, Deheng Ye +5
Model merging has emerged as a practical paradigm for integrating multiple independently trained models into a single model without joint retraining. Previous studies have demonstr…
Merging without Forgetting: Continual Fusion of Task-Specific Models via Optimal Transport
Zecheng Pan, Zhikang Chen, Ding Li +9
Merging models fine-tuned for different tasks into a single unified model has become an increasingly important direction for building versatile, efficient multi-task systems. Exist…
Think Consistently, Reason Efficiently: Energy-Based Calibration for Implicit Chain-of-Thought
Zhikang Chen, Sen Cui, Deheng Ye +3
Large Language Models (LLMs) have demonstrated strong reasoning capabilities through \emph{Chain-of-Thought} (CoT) prompting, which enables step-by-step intermediate reasoning. How…
Point-In-Context: Understanding Point Cloud via In-Context Learning
Mengyuan Liu, Zhongbin Fang, Xia Li +4
The rise of large-scale models has catalyzed in-context learning as a powerful approach for multitasking, particularly in natural language and image processing. However, its applic…