29 citations · 47 across the 15 of their papers we have counts for
16 papers
Rose: Efficient Embodied RL through Graph Optimization and GPU Compute Resource Control
Wen Zeng, Ke Hong, Xu Fu +14
Embodied reinforcement learning (RL) coordinates policy inference, simulation, and model training with highly heterogeneous resource demands. Although individual components frequen…
Knowing When Not to Reuse: Conditional Experience Transfer in Autonomous LLM Post-Training
Tingyun Li, Wenfeng Feng, Weiqing Li +3
Large language models offer broad capabilities, but adapting them to evolving domains, tools, and requirements often entails repeated post-training. Autonomous systems automate par…
Global Context or Local Detail? Adaptive Visual Grounding for Hallucination Mitigation
Yubo Jiang, Xin Yang, Abudukelimu Wuerkaixi +7
Vision-Language Models (VLMs) are frequently undermined by object hallucination--generating content that contradicts visual reality--due to an over-reliance on linguistic priors. W…
Breaking the Illusion: When Positive Meets Negative in Multimodal Decoding
Yubo Jiang, Yitong An, Xin Yang +7
Vision-Language Models (VLMs) are frequently undermined by object hallucination, generating content that contradicts visual reality, due to an over-reliance on linguistic priors. W…
V-tableR1: Process-Supervised Multimodal Table Reasoning with Critic-Guided Policy Optimization
Yubo Jiang, Yitong An, Xin Yang +7
We introduce V-tableR1, a process-supervised reinforcement learning framework that elicits rigorous, verifiable reasoning from multimodal large language models (MLLMs). Current MLL…
AutothinkRAG: Complexity-Aware Control of Retrieval-Augmented Reasoning for Image-Text Interaction
Jiashu Yang, Chi Zhang, Abudukelimu Wuerkaixi +5
Multimodal document question answering requires retrieving dispersed evidence from visually rich long documents and performing reliable reasoning over heterogeneous information. Ex…
Towards Self-Robust LLMs: Intrinsic Prompt Noise Resistance via CoIPO
Xin Yang, Letian Li, Abudukelimu Wuerkaixi +5
Large language models (LLMs) have demonstrated remarkable and steadily improving performance across a wide range of tasks. However, LLM performance may be highly sensitive to promp…
Higher Satisfaction, Lower Cost: A Technical Report on How LLMs Revolutionize Meituan's Intelligent Interaction Systems
Xuxin Cheng, Ke Zeng, Zhiquan Cao +65
Enhancing customer experience is essential for business success, particularly as service demands grow in scale and complexity. Generative artificial intelligence and Large Language…
Decentralized Dynamic Cooperation of Personalized Models for Federated Continual Learning
Danni Yang, Zhikang Chen, Sen Cui +6
Federated continual learning (FCL) has garnered increasing attention for its ability to support distributed computation in environments with evolving data distributions. However, t…
Learning without Isolation: Pathway Protection for Continual Learning
Zhikang Chen, Abudukelimu Wuerkaixi, Sen Cui +10
Deep networks are prone to catastrophic forgetting during sequential task learning, i.e., losing the knowledge about old tasks upon learning new tasks. To this end, continual learn…
Accurate Forgetting for Heterogeneous Federated Continual Learning
Abudukelimu Wuerkaixi, Sen Cui, Jingfeng Zhang +6
Recent years have witnessed a burgeoning interest in federated learning (FL). However, the contexts in which clients engage in sequential learning remain under-explored. Bridging F…
Beyond Similarity: Personalized Federated Recommendation with Composite Aggregation
Honglei Zhang, Haoxuan Li, Jundong Chen +6
Federated recommendation aims to collect global knowledge by aggregating local models from massive devices, to provide recommendations while ensuring privacy. Current methods mainl…
Balancing Similarity and Complementarity for Federated Learning
Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi +5
In mobile and IoT systems, Federated Learning (FL) is increasingly important for effectively using data while maintaining user privacy. One key challenge in FL is managing statisti…
MetaAge: Meta-Learning Personalized Age Estimators
Wanhua Li, Jiwen Lu, Abudukelimu Wuerkaixi +2
Different people age in different ways. Learning a personalized age estimator for each person is a promising direction for age estimation given that it better models the personaliz…
Rethinking Audio-visual Synchronization for Active Speaker Detection
Abudukelimu Wuerkaixi, You Zhang, Zhiyao Duan +1
Active speaker detection (ASD) systems are important modules for analyzing multi-talker conversations. They aim to detect which speakers or none are talking in a visual scene at an…
Reasoning Graph Networks for Kinship Verification: from Star-shaped to Hierarchical
Wanhua Li, Jiwen Lu, Abudukelimu Wuerkaixi +2
In this paper, we investigate the problem of facial kinship verification by learning hierarchical reasoning graph networks. Conventional methods usually focus on learning discrimin…