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Yanfang Ye

Yanfang Ye has 4 papers in our library, in 2026, most of them on Question answering, Language models & pretraining, Knowledge graphs & ontologies; often with Chuxu Zhang, Zheyuan Zhang, Kaiwen Shi.

4 papers in our library · 2026

OpenAlex · ORCID · h-index 1 · 7 works · 2 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 5 times in our library · ⭐ Paper Stars ranking: #10102

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of Notre DameNotre Dame · United States2026 · 6 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Computer Science · 4 worksArts and Humanities · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 2 worksInformation Systems · 2 worksDecision Sciences · 1 workManagement Science and Operations Research · 1 work

🧭 Topics 3Research subjects and languages the work is about.Question answering · 2 worksKnowledge graphs & ontologies · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20264 papers

 Papers

The most cited first (OpenAlex and our library).

  1. AgentRouter: A Knowledge-Graph-Guided LLM Router for Collaborative Multi-Agent Question Answering (2026 · 4 citations)
    With so many LLMs and agent strategies available, it is hard to know which configuration will work best for a given QA task.
  2. MAPRO: Recasting Multi-Agent Prompt Optimization as Maximum a Posteriori Inference (2026 · 3 citations)
    LLM-based multi-agent systems are fragile. Each agent reacts strongly to its prompt, and mistakes spread along the chain of hand-offs. Automating prompt design for such systems is hard for two reasons. The joint space of prompt candidates grows exponentially, and it is unclear which agent caused a failure. MAPRO…
  3. What Makes LLMs Effective Sequential Recommenders? A Study on Preference Intensity and Temporal Context (2026 · 1 citation)
    LLM-based sequential recommenders are usually tuned with DPO-style alignment, which reduces user feedback to simple preferred-versus-dispreferred pairs.
  4. NG-Router: Graph-Supervised Multi-Agent Collaboration for Nutrition Question Answering (2026)
    The paper tackles nutrition question answering, where a system must judge whether a food suits a particular person given their health conditions, habits and the food's nutritional profile.

 Co-authors here

11 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 5 times (of the 149,439 papers whose bibliographies we read); 3 citing papers with a page here →.

Updated

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