9 citations · 20 across the 15 of their papers we have counts for
16 papers
Tlow: Flow-based Item Tokenizer for Recommendation
Nian Li, Chonggang Song, Jingtao Ding +3
Item tokenizer encodes semantic embeddings into token IDs to replace the randomly assigned item IDs used in traditional recommendation models, fundamentally addressing the problems…
MMTS-BENCH: A Comprehensive Benchmark for Time Series Understanding and Reasoning
Yao Yin, Zhenyu Xiao, Musheng Li +7
Time series data are central to domains such as finance, healthcare, and cloud computing, yet existing benchmarks for evaluating various large language models (LLMs) on temporal ta…
Learning When to Look: A Disentangled Curriculum for Strategic Perception in Multimodal Reasoning
Siqi Yang, Zilve Gao, Haibo Qiu +5
Multimodal Large Language Models (MLLMs) demonstrate significant potential but remain brittle in complex, long-chain visual reasoning tasks. A critical failure mode is "visual forg…
UrbanWorld2.0: A Multimodal Agentic Framework for Reality-Aligned 3D World Generation at City-Scale
Shengyuan Wang, Zhiheng Zheng, Yu Shang +6
The automated generation of high-fidelity, city-scale 3D environments remains a formidable challenge with profound academic and industrial implications. However, existing methods s…
AgentExpt: Automating AI Experiment Design with LLM-based Resource Retrieval Agent
Yu Li, Lehui Li, Lin Chen +3
Large language model agents are becoming increasingly capable at web-centric tasks such as information retrieval, complex reasoning. These emerging capabilities have given rise to…
WeightFlow: Learning Stochastic Dynamics via Evolving Weight of Neural Network
Ruikun Li, Jiazhen Liu, Huandong Wang +2
Modeling stochastic dynamics from discrete observations is a key interdisciplinary challenge. Existing methods often fail to estimate the continuous evolution of probability densit…
Predicting the Dynamics of Complex System via Multiscale Diffusion Autoencoder
Ruikun Li, Jingwen Cheng, Huandong Wang +2
Predicting the dynamics of complex systems is crucial for various scientific and engineering applications. The accuracy of predictions depends on the model's ability to capture the…
AgentSwift: Efficient LLM Agent Design via Value-guided Hierarchical Search
Yu Li, Lehui Li, Zhihao Wu +5
Large language model (LLM) agents have demonstrated strong capabilities across diverse domains, yet automated agent design remains a significant challenge. Current automated agent…
Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One
Yiwen Song, Qianyue Hao, Qingmin Liao +2
Model ensemble is a useful approach in reinforcement learning (RL) for training effective agents. Despite wide success of RL, training effective agents remains difficult due to the…
LLM-Explorer: A Plug-in Reinforcement Learning Policy Exploration Enhancement Driven by Large Language Models
Qianyue Hao, Yiwen Song, Qingmin Liao +2
Policy exploration is critical in reinforcement learning (RL), where existing approaches include greedy, Gaussian process, etc. However, these approaches utilize preset stochastic…
Generative Adaptation of Dynamics to Environmental Shifts via Weight-space Diffusion
Ruikun Li, Huandong Wang, Jingtao Ding +3
Data-driven dynamics prediction often fails under environmental shifts, while traditional fine-tuning remains computationally prohibitive for hardware-constrained or data-scarce ap…
Predicting the Energy Landscape of Stochastic Dynamical System via Physics-informed Self-supervised Learning
Ruikun Li, Huandong Wang, Qingmin Liao +1
Energy landscapes play a crucial role in shaping dynamics of many real-world complex systems. System evolution is often modeled as particles moving on a landscape under the combine…
Predicting Long-term Dynamics of Complex Networks via Identifying Skeleton in Hyperbolic Space
Ruikun Li, Huandong Wang, Jinghua Piao +2
Learning complex network dynamics is fundamental for understanding, modeling, and controlling real-world complex systems. Though great efforts have been made to predict the future…
Modeling User Fatigue for Sequential Recommendation
Nian Li, Xin Ban, Cheng Ling +6
Recommender systems filter out information that meets user interests. However, users may be tired of the recommendations that are too similar to the content they have been exposed…
UV-SAM: Adapting Segment Anything Model for Urban Village Identification
Xin Zhang, Yu Liu, Yuming Lin +2
Urban villages, defined as informal residential areas in or around urban centers, are characterized by inadequate infrastructures and poor living conditions, closely related to the…
EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities
Nian Li, Chen Gao, Mingyu Li +2
The advent of artificial intelligence has led to a growing emphasis on data-driven modeling in macroeconomics, with agent-based modeling (ABM) emerging as a prominent bottom-up sim…