activity
20232026
most citedLearning Discriminative Features for Crowd Counting

19 citations · 24 across the 11 of their papers we have counts for

collaborators

13 papers

cs.AI2026

Towards a Trustworthy and Governable Internet of Agentics: An Architectural Perspective on Protocols, Systems, and Services

Haoyi Xiong, Yi Liu, Jiang Bian +5

Large language model applications are increasingly being organized as tool-using and multi-agent workflows rather than as isolated chat interfaces. This perspective paper examines…

cs.LG2026

BiasReducer: Adaptive Bias Mitigation for Reward Models

Shuang Liu, Yongliang Miao, Yanguang Liu +2

Reward models score responses from large language models (LLMs) and guide LLM training toward human preferences. However, reward models can favor superficial attributes such as len…

cs.CL2026

Measuring Maximum Activations in Open Large Language Models

Luxuan Chen, Han Tian, Xinran Chen +9

The dynamic range of activations is a first-order constraint for low-bit quantization, activation scaling, and stable LLM inference. Prior work characterized outlier features and m…

cs.CL2026

EndPrompt: Efficient Long-Context Extension via Terminal Anchoring

Han Tian, Luxuan Chen, Xinran Chen +10

Extending the context window of large language models typically requires training on sequences at the target length, incurring quadratic memory and computational costs that make lo…

cs.DC2026

FlexSpec: Frozen Drafts Meet Evolving Targets in Edge-Cloud Collaborative LLM Speculative Decoding

Yuchen Li, Rui Kong, Zhonghao Lyu +11

Deploying large language models (LLMs) in mobile and edge computing environments is constrained by limited on-device resources, scarce wireless bandwidth, and frequent model evolut…

cs.CL2025

Towards AI Search Paradigm

Yuchen Li, Hengyi Cai, Rui Kong +20

In this paper, we introduce the AI Search Paradigm, a comprehensive blueprint for next-generation search systems capable of emulating human information processing and decision-maki…

cs.CL2025★ 1 cited

Evaluating LLM-based Agents for Multi-Turn Conversations: A Survey

Shengyue Guan, Jindong Wang, Jiang Bian +3

This survey examines evaluation methods for large language model (LLM)-based agents in multi-turn conversational settings. Using a PRISMA-inspired framework, we systematically revi…

q-bio.BM2024

Pre-trained Molecular Language Models with Random Functional Group Masking

Tianhao Peng, Yuchen Li, Xuhong Li +7

Recent advancements in computational chemistry have leveraged the power of trans-former-based language models, such as MoLFormer, pre-trained using a vast amount of simplified mole…

cs.IR2024

Generative Pre-trained Ranking Model with Over-parameterization at Web-Scale (Extended Abstract)

Yuchen Li, Haoyi Xiong, Linghe Kong +4

Learning to rank (LTR) is widely employed in web searches to prioritize pertinent webpages from retrieved content based on input queries. However, traditional LTR models encounter…

cs.LG2024

Pre-trained Graphformer-based Ranking at Web-scale Search (Extended Abstract)

Yuchen Li, Haoyi Xiong, Linghe Kong +4

Both Transformer and Graph Neural Networks (GNNs) have been employed in the domain of learning to rank (LTR). However, these approaches adhere to two distinct yet complementary pro…

cs.IR2024★ 2 cited

When Search Engine Services meet Large Language Models: Visions and Challenges

Haoyi Xiong, Jiang Bian, Yuchen Li +5

Combining Large Language Models (LLMs) with search engine services marks a significant shift in the field of services computing, opening up new possibilities to enhance how we sear…

cs.CV2023★ 19 cited

Learning Discriminative Features for Crowd Counting

Yuehai Chen, Qingzhong Wang, Jing Yang +3

Crowd counting models in highly congested areas confront two main challenges: weak localization ability and difficulty in differentiating between foreground and background, leading…

cs.CL2023★ 2 cited

Natural Language based Context Modeling and Reasoning for Ubiquitous Computing with Large Language Models: A Tutorial

Haoyi Xiong, Jiang Bian, Sijia Yang +3

Large language models (LLMs) have become phenomenally surging, since 2018--two decades after introducing context-awareness into computing systems. Through taking into account the s…