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Wei Lin

Wei Lin has 3 papers in our library, in 2026, most of them on Dialogue systems; often with Guojun Yin, Dongbin Zhao, Hongru Wang.

3 papers in our library · 2026

OpenAlex · ORCID · h-index 0 · 7 works · 0 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 7 times in our library · ⭐ Paper Stars ranking: #10249

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

🎓Fujian University of TechnologyFuzhou · China2026 · 3 papersEmploymentcurrent🎓Yuan Ze UniversityTaoyuan District · Taiwan2019–2022Education 2019–2022🎓Fuzhou UniversityFuzhou · China2013–2016Education 2013–2016🎓University of Science and Technology LiaoningAnshan · China2009–2013Education 2009–2013

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 5The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 worksInformation Systems · 1 work

🧭 Topics 1Research subjects and languages the work is about.Dialogue systems · 1 work

 Works by year

2020s

20263 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Mem²Evolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience Distillation (2026 · 5 citations)
    LLM-based self-evolving agents usually improve along one of two separate tracks. Some learn from accumulated experience but stay limited to a fixed, hand-defined set of tools and expert agents. Others build new tools or agents, but they do so without lessons from earlier runs, so successes are hard to repeat and…
  2. Beyond Dialogue Time: Temporal Semantic Memory for Personalized LLM Agents (2026 · 2 citations)
    Memory modules let LLM agents personalize answers across long, multi-session conversations, but current systems handle time poorly.
  3. AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning (2026)
    Agentic RAG systems let LLMs call a retriever over several turns, but agents often keep searching after they already have enough evidence.

 Co-authors here

12 of 19 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 7 times (of the 149,439 papers whose bibliographies we read); 6 citing papers with a page here →.

Updated

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