Papers by Huaiyuan Yao
Every Response Counts: Quantifying Uncertainty of LLM-based Multi-Agent Systems through Tensor Decomposition (2026.acl-long)
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| Challenge: | Existing methods for MAS fail to address the unique complexities of multi-step reasoning . Existing uncertainty quantification methods struggle with cascading uncertainty . |
| Approach: | They propose a framework that quantifies uncertainty through tensor decomposition . they show that MATU effectively estimates holistic and robust uncertainty . |
| Outcome: | The proposed framework disentangles and quantifies distinct sources of uncertainty . it is generalizable across different agent structures and can be used for scientific discovery, education, healthcare and transportation. |
Instructional Agents: Reducing Teaching Faculty Workload through Multi-Agent Instructional Design (2026.eacl-long)
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| Challenge: | Existing AI-assisted educational tools focus on isolated tasks, but lack end-to-end workflows for instructional design. |
| Approach: | They propose a multi-agent large language model framework to automate end-to-end course material generation. |
| Outcome: | The proposed framework reduces development time and human workload while reducing human involvement. |