2 citations · 3 across the 8 of their papers we have counts for
8 papers
DuMate-DeepResearch: An Auditable Multi-Agent System with Recursive Search and Rubric-Grounded Reasoning
Lingyong Yan, Can Xu, Yukun Zhao +13
Deep Research (DR) has emerged as a new agentic paradigm to tackle complex, open-ended research tasks, demanding systems that can iteratively frame problems, acquire evidence, veri…
When Tools Fail: Benchmarking Dynamic Replanning and Anomaly Recovery in LLM Agents
Dongsheng Zhu, Xuchen Ma, Yucheng Shen +5
Existing benchmarks evaluate Tool-Integrated Reasoning (TIR) in LLMs on idealized ''happy paths'', largely overlooking real-world tool failures. We introduce ToolMaze, a benchmark…
DiffuGR: Generative Document Retrieval with Diffusion Language Models
Xinpeng Zhao, Zhaochun Ren, Yukun Zhao +9
Generative retrieval (GR) reframes document retrieval as an end-to-end task of generating sequential document identifiers (DocIDs). Existing GR methods predominantly rely on left-t…
GRAF: Multi-turn Jailbreaking via Global Refinement and Active Fabrication
Hua Tang, Lingyong Yan, Yukun Zhao +3
Large Language Models (LLMs) have demonstrated remarkable performance across diverse tasks. Nevertheless, they still pose notable safety risks due to potential misuse for malicious…
Joint Flashback Adaptation for Forgetting-Resistant Instruction Tuning
Yukun Zhao, Lingyong Yan, Zhenyang Li +4
Large language models have achieved remarkable success in various tasks. However, it is challenging for them to learn new tasks incrementally due to catastrophic forgetting. Existi…
DiQAD: A Benchmark Dataset for End-to-End Open-domain Dialogue Assessment
Yukun Zhao, Lingyong Yan, Weiwei Sun +5
Dialogue assessment plays a critical role in the development of open-domain dialogue systems. Existing work are uncapable of providing an end-to-end and human-epistemic assessment…
Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method
Yukun Zhao, Lingyong Yan, Weiwei Sun +6
Large Language Models (LLMs) have shown great potential in Natural Language Processing (NLP) tasks. However, recent literature reveals that LLMs generate nonfactual responses inter…
Feature-Level Debiased Natural Language Understanding
Yougang Lyu, Piji Li, Yechang Yang +5
Natural language understanding (NLU) models often rely on dataset biases rather than intended task-relevant features to achieve high performance on specific datasets. As a result,…