1 citations · 1 across the 14 of their papers we have counts for
14 papers
MARA: Flow-Matching-Guided Multi-Agent Resource Allocation for Computational Resource Efficient Learning
Hanye Zhao, Muning Wen, Yong Yu +1
Allocating limited computation among concurrent learning tasks is difficult when each task must reach a target loss before a deadline but its required training effort is unknown. E…
SW--Bench: Benchmarking Autonomous Software Agent Generation for Agentic Web
Linyao Chen, Bo Huang, Qinlao Zhao +13
The Agentic Web is emerging as a paradigm in which autonomous software agents interact with online resources and with each other to accomplish user goals. However, the capacity of…
UniCon: A Unified System for Efficient Robot Learning Transfers
Yunfeng Lin, Li Xu, Yong Yu +2
Deploying learning-based controllers across heterogeneous robots is challenging due to platform differences, inconsistent interfaces, and inefficient middleware. To address these i…
H-Zero: Cross-Humanoid Locomotion Pretraining Enables Few-shot Novel Embodiment Transfer
Yunfeng Lin, Minghuan Liu, Yufei Xue +4
The rapid advancement of humanoid robotics has intensified the need for robust and adaptable controllers to enable stable and efficient locomotion across diverse platforms. However…
Large Language Models are Demonstration Pre-Selectors for Themselves
Jiarui Jin, Yuwei Wu, Haoxuan Li +6
In-context learning (ICL) with large language models (LLMs) delivers strong few-shot performance by choosing few-shot demonstrations from the entire training data. However, existin…
PALo: Learning Posture-Aware Locomotion for Quadruped Robots
Xiangyu Miao, Jun Sun, Hang Lai +4
With the rapid development of embodied intelligence, locomotion control of quadruped robots on complex terrains has become a research hotspot. Unlike traditional locomotion control…
Understanding and Optimizing Agentic Workflows via Shapley value
Yingxuan Yang, Bo Huang, Siyuan Qi +14
Agentic workflows have become the dominant paradigm for building complex AI systems, orchestrating specialized components, such as planning, reasoning, action execution, and reflec…
Beyond Graph Convolution: Multimodal Recommendation with Topology-aware MLPs
Junjie Huang, Jiarui Qin, Yong Yu +1
Given the large volume of side information from different modalities, multimodal recommender systems have become increasingly vital, as they exploit richer semantic information bey…
World Model-based Perception for Visual Legged Locomotion
Hang Lai, Jiahang Cao, Jiafeng Xu +5
Legged locomotion over various terrains is challenging and requires precise perception of the robot and its surroundings from both proprioception and vision. However, learning dire…
DyDiff: Long-Horizon Rollout via Dynamics Diffusion for Offline Reinforcement Learning
Hanye Zhao, Xiaoshen Han, Zhengbang Zhu +4
With the great success of diffusion models (DMs) in generating realistic synthetic vision data, many researchers have investigated their potential in decision-making and control. M…
Look into the Future: Deep Contextualized Sequential Recommendation
Lei Zheng, Ning Li, Yanhuan Huang +3
Sequential recommendation aims to estimate how a user's interests evolve over time via uncovering valuable patterns from user behavior history. Many previous sequential models have…
Retrieval and Distill: A Temporal Data Shift-Free Paradigm for Online Recommendation System
Lei Zheng, Ning Li, Weinan Zhang +1
Current recommendation systems are significantly affected by a serious issue of temporal data shift, which is the inconsistency between the distribution of historical data and that…
DREAM: A Dual Representation Learning Model for Multimodal Recommendation
Kangning Zhang, Yingjie Qin, Jiarui Jin +4
Multimodal recommendation focuses primarily on effectively exploiting both behavioral and multimodal information for the recommendation task. However, most existing models suffer f…
Offline Fictitious Self-Play for Competitive Games
Jingxiao Chen, Weiji Xie, Weinan Zhang +2
Offline Reinforcement Learning (RL) enables policy improvement from fixed datasets without online interactions, making it highly suitable for real-world applications lacking effici…