Researcher — paper.dinq.me

2026 顶会论文

一作ICML 2026Poster· Social Aspects / Accountability, Transparency, and Interpretability

Position: Uncertainty Quantification in LLMs is Just Unsupervised Clustering

Tiejin Chen · Longchao Da · Xiaoou Liu · Hua Wei

可解释性社会与人本OpenReview

作者位次 #2/6ICML 2026Poster· Social Aspects / Accountability, Transparency, and Interpretability

Diagnosing Multi-step Reasoning Failures in Black-box LLMs via Stepwise Confidence Attribution

Xiaoou Liu · Tiejin Chen · Dengjia Zhang · Yaqing Wang · Lu Cheng · Hua Wei

推理可解释性社会与人本OpenReview

一作ACL 2026Poster· Natural Language Processing

Every Response Counts: Quantifying Uncertainty of LLM -based Multi-Agent Systems through Tensor Decomposition

Tiejin Chen · Huaiyuan Yao · Jia Chen · Evangelos E. Papalexakis · Hua Wei

摘要

While Large Language Model-based Multi-Agent Systems (MAS) consistently outperform single-agent systems on complex tasks, their intricate interactions introduce critical reliability challenges arising from communication dynamics and role dependencies. Existing Uncertainty Quantification methods,...

智能体与工具推理评测与基准OpenReview 项目页

一作ACL 2026Findings· Findings of ACL

Uncertainty Quantification of Large Language Models through Multiple Uncertainty Sources

Tiejin Chen · Xiaoou Liu · Longchao Da · Jia Chen · Evangelos E. Papalexakis · Hua Wei

摘要

Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains. However, the reliability of responses from LLMs remains a question. Uncertainty quantification (UQ) of LLMs is crucial for ensuring their reliability, especially in areas such as healthcare. Existing UQ...

效率与系统评测与基准代码OpenReview 项目页

作者位次 #2/5KDD 2026Oral· Knowledge Discovery & Data Mining

The Sim-to-Real Gap of Foundation Model Agents: A Unified MDP Perspective

Xiaoou Liu · Tiejin Chen · Weibo Li · Xiyang Hu · Hua Wei

智能体与工具检索与知识OpenReview

Tiejin Chen

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