22 citations · 47 across the 12 of their papers we have counts for
12 papers
ABot-N1: Toward a General Visual Language Navigation Foundation Model
Ruiyan Gong, Yingnan Guo, Junjun Hu +43
Visual Language Navigation foundation models aim to unify deep reasoning for grounded spatial decisions with broad versatility for diverse embodied tasks. Current approaches typica…
ABot-AgentOS: A General Robotic Agent OS with Lifelong Multi-modal Memory
Jiayi Tian, Shiao Liu, Yuting Xu +30
Recent VLM and VLA systems have improved robotic perception and action prediction, yet long-horizon embodied agents still require a general runtime layer for reasoning, memory, too…
Guess Where You Go: Generative Next Point-of-Interest Recommendation in Amap
Penglong Zhai, Bowen Zheng, Jie Li +8
Generative retrieval enables recommender systems to retrieve items by generating compact item identifiers, but scaling it to industrial scenarios remains challenging due to redunda…
ABot-N0: Technical Report on the VLA Foundation Model for Versatile Embodied Navigation
Zedong Chu, Shichao Xie, Xiaolong Wu +41
Embodied navigation has long been fragmented by task-specific architectures. We introduce ABot-N0, a unified Vision-Language-Action (VLA) foundation model that achieves a ``Grand U…
Cognitive-Aligned Spatio-Temporal Large Language Models For Next Point-of-Interest Prediction
Penglong Zhai, Jie Li, Fanyi Di +9
The next point-of-interest (POI) recommendation task aims to predict the users' immediate next destinations based on their preferences and historical check-ins, holding significant…
Hierarchical Reinforcement Learning for Temporal Abstraction of Listwise Recommendation
Luo Ji, Gao Liu, Mingyang Yin +2
Modern listwise recommendation systems need to consider both long-term user perceptions and short-term interest shifts. Reinforcement learning can be applied on recommendation to s…
Intra-session Context-aware Feed Recommendation in Live Systems
Luo Ji, Gao Liu, Mingyang Yin +1
Feed recommendation allows users to constantly browse items until feel uninterested and leave the session, which differs from traditional recommendation scenarios. Within a session…
Deep Unified Representation for Heterogeneous Recommendation
Chengqiang Lu, Mingyang Yin, Shuheng Shen +3
Recommendation system has been a widely studied task both in academia and industry. Previous works mainly focus on homogeneous recommendation and little progress has been made for…
Linear-Time Self Attention with Codeword Histogram for Efficient Recommendation
Yongji Wu, Defu Lian, Neil Zhenqiang Gong +4
Self-attention has become increasingly popular in a variety of sequence modeling tasks from natural language processing to recommendation, due to its effectiveness. However, self-a…
Rethinking Lifelong Sequential Recommendation with Incremental Multi-Interest Attention
Yongji Wu, Lu Yin, Defu Lian +4
Sequential recommendation plays an increasingly important role in many e-commerce services such as display advertisement and online shopping. With the rapid development of these se…
Federated Graph Learning -- A Position Paper
Huanding Zhang, Tao Shen, Fei Wu +3
Graph neural networks (GNN) have been successful in many fields, and derived various researches and applications in real industries. However, in some privacy sensitive scenarios (l…
A Quantitative Study of Security Bug Fixes of GitHub Repositories
Daito Nakano, Mingyang Yin, Ryosuke Sato +3
Software is prone to bugs and failures. Security bugs are those that expose or share privileged information and access in violation of the software's requirements. Given the seriou…