16 citations · 25 across the 14 of their papers we have counts for
14 papers
Adapting Embedding Models for Agent Capability Retrieval
Tingwei Chen, Yunxiao Shi, Zhengdong Chu +2
Open agent marketplaces list native agents, tool bundles, and reusable skill packages in the same search interface, yet practitioners still have little guidance on how to retrieve…
AgentSelect: Benchmark for Narrative Query-to-Agent Recommendation
Yunxiao Shi, Wujiang Xu, Tingwei Chen +7
LLM agents are rapidly becoming the practical interface for task automation, yet the ecosystem lacks a principled way to choose among an exploding space of deployable configuration…
Accumulative SGD Influence Estimation for Data Attribution
Yunxiao Shi, Shuo Yang, Yixin Su +2
Modern data-centric AI needs precise per-sample influence. Standard SGD-IE approximates leave-one-out effects by summing per-epoch surrogates and ignores cross-epoch compounding, w…
MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models
Yunxiao Shi, Shuo Yang, Haimin Zhang +4
Neural Collaborative Filtering models are widely used in recommender systems but are typically trained under static settings, assuming fixed data distributions. This limits their a…
sam-llm: interpretable lane change trajectoryprediction via parametric finetuning
Zhuo Cao, Yunxiao Shi, Min Xu
This work introduces SAM-LLM, a novel hybrid architecture that bridges the gap between the contextual reasoning of Large Language Models (LLMs) and the physical precision of kinema…
RSVLM-QA: A Benchmark Dataset for Remote Sensing Vision Language Model-based Question Answering
Xing Zi, Jinghao Xiao, Yunxiao Shi +4
Visual Question Answering (VQA) in remote sensing (RS) is pivotal for interpreting Earth observation data. However, existing RS VQA datasets are constrained by limitations in annot…
PersonaX: A Recommendation Agent Oriented User Modeling Framework for Long Behavior Sequence
Yunxiao Shi, Wujiang Xu, Zeqi Zhang +3
User profile embedded in the prompt template of personalized recommendation agents play a crucial role in shaping their decision-making process. High-quality user profiles are esse…
iAgent: LLM Agent as a Shield between User and Recommender Systems
Wujiang Xu, Yunxiao Shi, Zujie Liang +6
Traditional recommender systems usually take the user-platform paradigm, where users are directly exposed under the control of the platform's recommendation algorithms. However, th…
A Learnable Agent Collaboration Network Framework for Personalized Multimodal AI Search Engine
Yunxiao Shi, Min Xu, Haimin Zhang +2
Large language models (LLMs) and retrieval-augmented generation (RAG) techniques have revolutionized traditional information access, enabling AI agent to search and summarize infor…
Beyond KAN: Introducing KarSein for Adaptive High-Order Feature Interaction Modeling in CTR Prediction
Yunxiao Shi, Wujiang Xu, Haimin Zhang +2
Modeling high-order feature interactions is crucial for click-through rate (CTR) prediction, yet traditional approaches typically predefine a maximum interaction order and exhausti…
Enhancing Retrieval and Managing Retrieval: A Four-Module Synergy for Improved Quality and Efficiency in RAG Systems
Yunxiao Shi, Xing Zi, Zijing Shi +3
Retrieval-augmented generation (RAG) techniques leverage the in-context learning capabilities of large language models (LLMs) to produce more accurate and relevant responses. Origi…
ERAGent: Enhancing Retrieval-Augmented Language Models with Improved Accuracy, Efficiency, and Personalization
Yunxiao Shi, Xing Zi, Zijing Shi +3
Retrieval-augmented generation (RAG) for language models significantly improves language understanding systems. The basic retrieval-then-read pipeline of response generation has ev…
SLMRec: Distilling Large Language Models into Small for Sequential Recommendation
Wujiang Xu, Qitian Wu, Zujie Liang +5
Sequential Recommendation (SR) task involves predicting the next item a user is likely to interact with, given their past interactions. The SR models examine the sequence of a user…
Causal Disentanglement for Regulating Social Influence Bias in Social Recommendation
Li Wang, Min Xu, Quangui Zhang +2
Social recommendation systems face the problem of social influence bias, which can lead to an overemphasis on recommending items that friends have interacted with. Addressing this…