10 papers
Heterogeneous Information-Bottleneck Coordination Graphs for Multi-Agent Reinforcement Learning
Wei Duan, Junyu Xuan, En Yu +2
Coordination graphs are a central abstraction in cooperative multi-agent reinforcement learning (MARL), yet existing sparse-graph learners lack a theoretically grounded mechanism t…
Turning Drift into Constraint: Robust Reasoning Alignment in Non-Stationary Multi-Stream Environments
Xiaoyu Yang, En Yu, Wei Duan +1
This paper identifies a critical yet underexplored challenge in reasoning alignment from multiple multi-modal large language models (MLLMs): In non-stationary environments, the div…
Autonomous Drift Learning in Data Streams: A Unified Perspective
Xiaoyu Yang, En Yu, Jie Lu
In the pursuit of autonomous learning systems, the foundational assumption of stationarity, the premise that data distributions and model behaviors remain constant, is fundamentall…
Towards Robust Endogenous Reasoning: Unifying Drift Adaptation in Non-Stationary Tuning
Xiaoyu Yang, En Yu, Wei Duan +1
Reinforcement Fine-Tuning (RFT) has established itself as a critical paradigm for the alignment of Multi-modal Large Language Models (MLLMs) with complex human values and domain-sp…
STEP: Scientific Time-Series Encoder Pretraining via Cross-Domain Distillation
Chen Zhang, Liwei Liu, Jun Tao +6
Scientific time series are central to scientific AI but are typically sparse, highly heterogeneous, and limited in scale, making unified representation learning particularly challe…
Resilient Contrastive Pre-training under Non-Stationary Drift
Xiaoyu Yang, Jie Lu, En Yu +1
The remarkable success of large-scale contrastive pre-training has been largely driven by by vast yet static datasets. However, as the scaling paradigm evolves, this paradigm encou…