3 citations · 5 across the 13 of their papers we have counts for
13 papers
Mechanistic Attention Guidance for Agent Memory Refinement
Yechao Hong, Haiquan Qiu, Yaqing Wang +1
Existing self-evolving memory systems mainly improve agent memory based on textual outputs, such as task trajectories and reflections. However, this text-based paradigm rarely inco…
PSPA-Bench: A Personalized Benchmark for Smartphone GUI Agent
Hongyi Nie, Xunyuan Liu, Yudong Bai +4
Smartphone GUI agents execute tasks by operating directly on app interfaces, offering a path to broad capability without deep system integration. However, real-world smartphone use…
DGNet: Discrete Green Networks for Data-Efficient Learning of Spatiotemporal PDEs
Yingjie Tan, Quanming Yao, Yaqing Wang
Spatiotemporal partial differential equations (PDEs) underpin a wide range of scientific and engineering applications. Neural PDE solvers offer a promising alternative to classical…
Self-Generative Adversarial Fine-Tuning for Large Language Models
Shiguang Wu, Yaqing Wang, Quanming Yao
Fine-tuning large language models (LLMs) for alignment typically relies on supervised fine-tuning or reinforcement learning from human feedback, both limited by the cost and scarci…
LLM-Empowered Representation Learning for Emerging Item Recommendation
Ziying Zhang, Quanming Yao, Yaqing Wang
In this work, we tackle the challenge of recommending emerging items, whose interactions gradually accumulate over time. Existing methods often overlook this dynamic process, typic…
Spectral Alignment as Predictor of Loss Explosion in Neural Network Training
Haiquan Qiu, You Wu, Yingjie Tan +2
Loss explosions in training deep neural networks can nullify multi-million dollar training runs. Conventional monitoring metrics like weight and gradient norms are often lagging an…
Attending on Multilevel Structure of Proteins enables Accurate Prediction of Cold-Start Drug-Target Interactions
Ziying Zhang, Yaqing Wang, Yuxuan Sun +2
Cold-start drug-target interaction (DTI) prediction focuses on interaction between novel drugs and proteins. Previous methods typically learn transferable interaction patterns betw…
Searching Meta Reasoning Skeleton to Guide LLM Reasoning
Ziying Zhang, Yaqing Wang, Quanming Yao
Meta reasoning behaviors work as a skeleton to guide large language model (LLM) reasoning, thus help to improve reasoning performance. However, prior researches implement meta reas…
PERSCEN: Learning Personalized Interaction Pattern and Scenario Preference for Multi-Scenario Matching
Haotong Du, Yaqing Wang, Fei Xiong +5
With the expansion of business scales and scopes on online platforms, multi-scenario matching has become a mainstream solution to reduce maintenance costs and alleviate data sparsi…
Language Model Networks: Supervision-Efficient Learning through Dense Communication
Shiguang Wu, Yaqing Wang, Quanming Yao
Language models are increasingly used not only as standalone predictors but also as components in larger inference systems, from test-time scaling to multi-agent collaboration. We…
Learning to Learn with Contrastive Meta-Objective
Shiguang Wu, Yaqing Wang, Yatao Bian +1
Meta-learning enables learning systems to adapt quickly to new tasks, similar to humans. Different meta-learning approaches all work under/with the mini-batch episodic training fra…
Warming Up Cold-Start CTR Prediction by Learning Item-Specific Feature Interactions
Yaqing Wang, Hongming Piao, Daxiang Dong +2
In recommendation systems, new items are continuously introduced, initially lacking interaction records but gradually accumulating them over time. Accurately predicting the click-t…
Beyond Scaleup: Knowledge-aware Parsimony Learning from Deep Networks
Quanming Yao, Yongqi Zhang, Yaqing Wang +3
The brute-force scaleup of training datasets, learnable parameters and computation power, has become a prevalent strategy for developing more robust learning models. However, due t…