activity
20242026
most citedUniMS-RAG: A Unified Multi-source Retrieval-Augmented Generation for Personalized Dialogue Systems

8 citations · 11 across the 4 of their papers we have counts for

collaborators

5 papers

cs.AI2026

WorkBorne: Discovering Agentic Tasks from Real-World Artifact Transitions

Junyi Zhou, Yufei Wang, Qiyuan Zhang +7

Developing terminal-based agents for long-horizon execution demands synthetic tasks that tightly couple complex instructions with authentic, interactive environments. However, curr…

cs.CL2025

Rethinking Stateful Tool Use in Multi-Turn Dialogues: Benchmarks and Challenges

Hongru Wang, Wenyu Huang, Yufei Wang +7

Existing benchmarks that assess Language Models (LMs) as Language Agents (LAs) for tool use primarily focus on stateless, single-turn interactions or partial evaluations, such as t…

cs.CL2025★ 3 cited

A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?

Qiyuan Zhang, Fuyuan Lyu, Zexu Sun +10

As enthusiasm for scaling computation (data and parameters) in the pretraining era gradually diminished, test-time scaling (TTS), also referred to as ``test-time computing'' has em…

cs.CL2024

NILE: Internal Consistency Alignment in Large Language Models

Minda Hu, Qiyuan Zhang, Yufei Wang +7

As a crucial step to enhance LLMs alignment with human intentions, Instruction Fine-Tuning (IFT) has a high demand on dataset quality. However, existing IFT datasets often contain…

cs.CL2024★ 8 cited

UniMS-RAG: A Unified Multi-source Retrieval-Augmented Generation for Personalized Dialogue Systems

Hongru Wang, Wenyu Huang, Yang Deng +6

Large Language Models (LLMs) has shown exceptional capabilities in many natual language understanding and generation tasks. However, the personalization issue still remains a much-…