1 citations · 3 across the 8 of their papers we have counts for
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ASCENT: Online Test-Time Training of Long-Horizon Agents via Self-Distillation of Verified Experience
Haodong Lu, Dong Gong
A large language model (LLM) agent solves long-horizon tasks through many reasoning-action turns, with one verification signal at termination. Deployed agents face streams of relat…
On Token's Dilemma: Dynamic MoE with Drift-Aware Token Assignment for Continual Learning of Large Vision Language Models
Chongyang Zhao, Mingsong Li, Haodong Lu +1
Multimodal Continual Instruction Tuning aims to continually enhance Large Vision Language Models (LVLMs) by learning from new data without forgetting previously acquired knowledge.…
Model Inversion with Layer-Specific Modeling and Alignment for Data-Free Continual Learning
Ruilin Tong, Haodong Lu, Yuhang Liu +1
Continual learning (CL) aims to incrementally train a model on a sequence of tasks while retaining performance on prior ones. However, storing and replaying data is often infeasibl…
Little by Little: Continual Learning via Incremental Mixture of Rank-1 Associative Memory Experts
Haodong Lu, Chongyang Zhao, Minhui Xue +3
Continual learning (CL) with large pre-trained models aims to incrementally acquire knowledge without catastrophic forgetting. Existing LoRA-based Mixture-of-Experts (MoE) methods…
Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning
Huiyi Wang, Haodong Lu, Lina Yao +1
Continual learning (CL) aims to continually accumulate knowledge from a non-stationary data stream without catastrophic forgetting of learned knowledge, requiring a balance between…
Learning with Mixture of Prototypes for Out-of-Distribution Detection
Haodong Lu, Dong Gong, Shuo Wang +3
Out-of-distribution (OOD) detection aims to detect testing samples far away from the in-distribution (ID) training data, which is crucial for the safe deployment of machine learnin…