collaborators

9 papers

cs.CL2026

TurnSight: Turn-Level Hindsight Self-Distillation for Tool-Integrated Reasoning

Changle Qu, Sunhao Dai, Hengyi Cai +4

Tool-Integrated Reasoning (TIR) enables LLMs to solve complex tasks through iterative tool interactions. However, existing reinforcement learning methods often rely on trajectory-l…

cs.IR2026

Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems

Xinyu Lin, Yashar Deldjoo, Sunhao Dai +7

The rapid integration of large language model-based agents into recommender systems has driven a shift from static, ranking-based pipelines toward autonomous and interactive system…

cs.LG2026

DR-Venus: Towards Frontier Edge-Scale Deep Research Agents with Only 10K Open Data

Venus Team, Sunhao Dai, Yong Deng +10

Edge-scale deep research agents based on small language models are attractive for real-world deployment due to their advantages in cost, latency, and privacy. In this work, we stud…

cs.HC2026

VenusBench-Mobile: A Challenging and User-Centric Benchmark for Mobile GUI Agents with Capability Diagnostics

Yichen Gong, Zhuohan Cai, Sunhao Dai +4

Existing online benchmarks for mobile GUI agents remain largely app-centric and task-homogeneous, failing to reflect the diversity and instability of real-world mobile usage. To th…

cs.CV2026

UI-Venus-1.5 Technical Report

Venus Team, Changlong Gao, Zhangxuan Gu +24

GUI agents have emerged as a powerful paradigm for automating interactions in digital environments, yet achieving both broad generality and consistently strong task performance rem…

cs.CL2025

Information Gain-based Policy Optimization: A Simple and Effective Approach for Multi-Turn Search Agents

Guoqing Wang, Sunhao Dai, Guangze Ye +5

Large language model (LLM)-based agents are increasingly trained with reinforcement learning (RL) to enhance their ability to interact with external environments through tool use,…

cs.IR2025

Interactive Recommendation Agent with Active User Commands

Jiakai Tang, Yujie Luo, Xunke Xi +12

Traditional recommender systems rely on passive feedback mechanisms that limit users to simple choices such as like and dislike. However, these coarse-grained signals fail to captu…

cs.IR2025

OnePiece: Bringing Context Engineering and Reasoning to Industrial Cascade Ranking System

Sunhao Dai, Jiakai Tang, Jiahua Wu +13

Despite the growing interest in replicating the scaled success of large language models (LLMs) in industrial search and recommender systems, most existing industrial efforts remain…

cs.IR2025

RecGPT Technical Report

Chao Yi, Dian Chen, Gaoyang Guo +51

Recommender systems are among the most impactful applications of artificial intelligence, serving as critical infrastructure connecting users, merchants, and platforms. However, mo…