activity
20242026
most citedStepTool: Enhancing Multi-Step Tool Usage in LLMs via Step-Grained Reinforcement Learning

2 citations · 3 across the 13 of their papers we have counts for

collaborators

13 papers

cs.IR2026

SelfDR: Self-Distillation from Reasoning for LLM-Based Recommendation

Chumeng Jiang, Jiayin Wang, Xinjie Lin +3

Large Language Models (LLMs) have recently emerged as powerful backbones for recommendation. To better elicit their capabilities, reasoning has been widely incorporated to help LLM…

cs.CL2026

Personalized Turn-Level User Conversation Satisfaction Benchmark

Zhefan Wang, Zhiqiang Guo, Weizhi Ma +3

User satisfaction with AI assistants is highly personalized: the same response may satisfy one user but disappoint another depending on what each user expects and what they have as…

cs.IR2026

APAO: Bridging the Training-Inference Gap in Generative Recommendation via Adaptive Prefix-Aware Optimization

Yuanqing Yu, Yifan Wang, Weizhi Ma +2

Generative recommendation has recently emerged as a promising paradigm for sequential recommendation. It formulates the task as an autoregressive generation process, predicting tok…

cs.CV2025

GSE: Evaluating Sticker Visual Semantic Similarity via a General Sticker Encoder

Heng Er Metilda Chee, Jiayin Wang, Zhiqiang Guo +2

Stickers have become a popular form of visual communication, yet understanding their semantic relationships remains challenging due to their highly diverse and symbolic content. In…

cs.CY2025

Integrating LLM and Diffusion-Based Agents for Social Simulation

Xinyi Li, Zhiqiang Guo, Qinglang Guo +3

Large language models (LLMs) offer strong semantic reasoning capabilities for user modeling, but applying LLM-based simulation to an entire social network is computationally expens…

cs.IR2025

Human vs. Agent in Task-Oriented Conversations

Zhefan Wang, Ning Geng, Zhiqiang Guo +2

Task-oriented conversational systems are essential for efficiently addressing diverse user needs, yet their development requires substantial amounts of high-quality conversational…