4 citations · 11 across the 15 of their papers we have counts for
15 papers
Cooperative Coevolution for Resource-Constrained Agentic LLM Post-Training
Zhiyuan Wang, Shengcai Liu, Jiahao Wu +5
Tool-using large language model (LLM) agents produce long, multi-turn trajectories, making gradient-based post-training memory-intensive. Evolution strategies (ES) enable memory-ef…
Policy and World Modeling Co-Training for Language Agents
Ning Lu, Baijiong Lin, Shengcai Liu +9
Reinforcement learning (RL) improves large language model (LLM) agents by teaching them which actions lead to high rewards, but provides little supervision on what those actions do…
Train at Moving Edge: Online-Verified Prompt Selection for Efficient RL Training of Large Reasoning Model
Jiahao Wu, Ning Lu, Shengcai Liu +6
Reinforcement learning (RL) has become essential for post-training large language models (LLMs) in reasoning tasks. While scaling rollouts can stabilize training and enhance perfor…
LLM-Driven Instance-Specific Heuristic Generation and Selection
Shaofeng Zhang, Shengcai Liu, Ning Lu +4
Combinatorial optimization problems are widely encountered in real-world applications. A critical research challenge lies in designing high-quality heuristic algorithms that effici…
Safe Delta: Consistently Preserving Safety when Fine-Tuning LLMs on Diverse Datasets
Ning Lu, Shengcai Liu, Jiahao Wu +5
Large language models (LLMs) have shown great potential as general-purpose AI assistants across various domains. To fully leverage this potential in specific applications, many com…
SemDiff: Generating Natural Unrestricted Adversarial Examples via Semantic Attributes Optimization in Diffusion Models
Zeyu Dai, Shengcai Liu, Rui He +5
Unrestricted adversarial examples (UAEs), allow the attacker to create non-constrained adversarial examples without given clean samples, posing a severe threat to the safety of dee…
OneForecast: A Universal Framework for Global and Regional Weather Forecasting
Yuan Gao, Hao Wu, Ruiqi Shu +11
Accurate weather forecasts are important for disaster prevention, agricultural planning, etc. Traditional numerical weather prediction (NWP) methods offer physically interpretable…
Backdoor Graph Condensation
Jiahao Wu, Ning Lu, Zeiyu Dai +5
Graph condensation has recently emerged as a prevalent technique to improve the training efficiency for graph neural networks (GNNs). It condenses a large graph into a small one su…
Discrete Semantic Tokenization for Deep CTR Prediction
Qijiong Liu, Hengchang Hu, Jiahao Wu +3
Incorporating item content information into click-through rate (CTR) prediction models remains a challenge, especially with the time and space constraints of industrial scenarios.…
TF-DCon: Leveraging Large Language Models (LLMs) to Empower Training-Free Dataset Condensation for Content-Based Recommendation
Jiahao Wu, Qijiong Liu, Hengchang Hu +5
Modern techniques in Content-based Recommendation (CBR) leverage item content information to provide personalized services to users, but suffer from resource-intensive training on…
Dataset Condensation for Recommendation
Jiahao Wu, Wenqi Fan, Jingfan Chen +5
Training recommendation models on large datasets requires significant time and resources. It is desired to construct concise yet informative datasets for efficient training. Recent…
Enhancing Graph Collaborative Filtering via Uniformly Co-Clustered Intent Modeling
Jiahao Wu, Wenqi Fan, Shengcai Liu +3
Graph-based collaborative filtering has emerged as a powerful paradigm for delivering personalized recommendations. Despite their demonstrated effectiveness, these methods often ne…
Multi-Domain Learning From Insufficient Annotations
Rui He, Shengcai Liu, Jiahao Wu +2
Multi-domain learning (MDL) refers to simultaneously constructing a model or a set of models on datasets collected from different domains. Conventional approaches emphasize domain-…
FANS: Fast Non-Autoregressive Sequence Generation for Item List Continuation
Qijiong Liu, Jieming Zhu, Jiahao Wu +3
User-curated item lists, such as video-based playlists on Youtube and book-based lists on Goodreads, have become prevalent for content sharing on online platforms. Item list contin…
Disentangled Contrastive Learning for Social Recommendation
Jiahao Wu, Wenqi Fan, Jingfan Chen +3
Social recommendations utilize social relations to enhance the representation learning for recommendations. Most social recommendation models unify user representations for the use…