12 papers
The Sirens' Song: When Proximal Background Context Overshadows Distant Evidence
Xiaoyu Yang, Jie Lu, Wei Duan +1
Long-context LLMs focus on retrieving distant evidence from extensive context, yet existing work has largely focused on overcoming distance alone. In this work, we identify the Pro…
FreKoo++: Learning Continuous Spectral Dynamics for Temporal Domain Generalization
En Yu, Xiaoyu Yang, Wei Duan +2
Temporal Domain Generalization (TDG) aims to learn from historical domains and generalize to unseen future distributions under concept drift. Nevertheless, prevailing TDG methods s…
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…
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…
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…
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…
Drift-aware Collaborative Assistance Mixture of Experts for Heterogeneous Multistream Learning
En Yu, Jie Lu, Kun Wang +2
Learning from multiple data streams in real-world scenarios is fundamentally challenging due to intrinsic heterogeneity and unpredictable concept drifts. Existing methods typically…
Walking the Tightrope: Disentangling Beneficial and Detrimental Drifts in Non-Stationary Custom-Tuning
Xiaoyu Yang, Jie Lu, En Yu
This paper uncovers a critical yet overlooked phenomenon in multi-modal large language models (MLLMs): detrimental concept drift within chain-of-thought (CoT) reasoning during non-…
Learning Robust Spectral Dynamics for Temporal Domain Generalization
En Yu, Jie Lu, Xiaoyu Yang +2
Modern machine learning models struggle to maintain performance in dynamic environments where temporal distribution shifts, \emph{i.e., concept drift}, are prevalent. Temporal Doma…
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…
Adapting Multi-modal Large Language Model to Concept Drift From Pre-training Onwards
Xiaoyu Yang, Jie Lu, En Yu
Multi-modal Large Language Models (MLLMs) frequently face challenges from concept drift when dealing with real-world streaming data, wherein distributions change unpredictably. Thi…