🧑‍🔬 Yunlong Feng

🏛️ Affiliations

🏢Alibaba GroupHangzhou · China2025 · 1 papercurrent🎓University of Electronic Science and Technology of ChinaChengdu · China2025 · 1 papercurrent🎓Zhejiang Normal UniversityJinhua · China2002–2021 · 103 papers🎓Harbin Institute of TechnologyHarbin · China2023–2024 · 3 papers🎓Anhui University of TechnologyMa'anshan City · China2023 · 2 papers🎓Fuzhou UniversityFuzhou · China1998–2002 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

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

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. CodeElo: Benchmarking Competition-level Code Generation of LLMs with Human-comparable Elo Ratings (2025 · 37 citations)
   Competitive-programming benchmarks for LLMs judge solutions unreliably. Online judges keep their test suites private, so existing benchmarks such as LiveCodeBench, USACO and CodeContests rely on weaker public or synthetic tests that let wrong solutions pass. These benchmarks also cannot handle problems with multiple…
2. ProxyQA: An Alternative Framework for Evaluating Long-Form Text Generation with Large Language Models (2024 · 30 citations)
   Evaluating how well LLMs write long texts such as reports or articles is difficult. Reference-based metrics like ROUGE need gold texts that rarely exist and often disagree with people. Crowdsourced judging is slow and inconsistent, and LLM-as-a-judge can be biased and lack recent knowledge. ProxyQA takes a different…
3. Qwen3-Coder-Next Technical Report (2026 · 15 citations)
   The report asks how much coding-agent capability a model with a small active parameter budget can reach if the training recipe is strong enough.
4. SCALAR: Scientific Citation-based Live Assessment of Long-context Academic Reasoning (2026)
   Evaluating how well LLMs actually understand long documents is difficult. Synthetic retrieval tests such as needle-in-a-haystack are easy for current models, and benchmarks built from existing corpora risk contamination and allow shortcut answers. SCALAR addresses this with academic papers whose citations act as…

🤝 Co-authors here

12 of 33 co-authors with a page, the most papers together first.

• An Yang
• Binyuan Hui
• Bo Zheng
• Bofei Gao
• Bowen Yu
• Dayiheng Liu
• Haonan Li
• Jian Yang
• Jiawei Chen
• Jiaxi Yang
• Junyang Lin
• Kaixin Li

Venues

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

• Annual Meeting of the Association for Computational Linguistics (ACL): ACL 2024
• Conference of the European Chapter of the Association for Computational Linguistics (EACL): EACL 2026

🔗 Cited by

Papers of our library cite their works 70 times (of the 149,439 papers whose bibliographies we read); 20 citing papers with a page here →.

Updated 1 November 2026

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