Xiangyu Wu
Xiangyu Wu has 3 papers in our library, in 2026, most of them on Reasoning, Vision & language; often with Guorui Zhou, Hao LU, Jiaqi Tang.
OpenAlex · ORCID · h-index 7 · 31 works · 319 citations OpenAlex, 2026-10-14
📄 3 works in our library · 🔗 Cited 25 times in our library · ⭐ Paper Stars ranking: #8571
Affiliations
🏢KuaishouBeijing ·
China2026 · 1 paper🎓Nanjing University of Science and TechnologyNanjing ·
China2026 · 1 paper🎓China Agricultural UniversityBeijing ·
China2019–2025 · 9 papers🎓Guangdong University of TechnologyGuangzhou ·
China2018–2023 · 6 papers🎓Guangdong Pharmaceutical UniversityGuangzhou ·
China2007–2015 · 5 papers🎓South China Normal UniversityGuangzhou ·
China2017 · 4 papers🎓Hong Kong Polytechnic UniversityHong Kong ·
China2014–2015 · 3 papers🏛️Ministry of Agriculture and Rural AffairsBeijing ·
China2022–2023 · 2 papers
Fields, topics and concepts
📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksInformation Systems · 2 works
🧭 Topics 2Research subjects and languages the work is about.Reasoning · 2 worksVision & language · 1 work
Works by year
2020s
Papers
- Unlocking Exploration in RLVR: Uncertainty-aware Advantage Shaping for Deeper Reasoning (2026 · 15 citations)
Standard RLVR algorithms such as GRPO give every token in a sampled response the same advantage. They cannot tell pivotal reasoning decisions from routine ones, so the policy settles too early on a few safe trajectories and suffers entropy collapse. The authors, from Kuaishou Technology, propose UCAS… - OneRec-Think: In-Text Reasoning for Generative Recommendation (2026 · 10 citations)
Generative recommenders such as OneRec decode item identifiers directly. This makes them implicit predictors: they cannot produce explicit, controllable reasoning the way modern LLMs do. OneRec-Think, developed at Kuaishou, addresses this by placing dialogue, textual reasoning and personalized recommendation in a… - LPO: Towards Accurate GUI Agent Interaction via Location Preference Optimization (2026 · 3 citations)
GUI agents built on multimodal LLMs often click or point at the wrong spot. Supervised Fine-Tuning (SFT) handles coordinates poorly. Earlier reinforcement-learning approaches judge location coarsely, using text matching, fixed IoU thresholds, or rigid decision boundaries. UI-TARS-style DPO also needs hand-built…
Co-authors here
Venues
- Findings of the Association for Computational Linguistics ×2: Findings of the Association for Computational Linguistics 2026 ×2
- Annual Meeting of the Association for Computational Linguistics (ACL): ACL 2026
Cited by
Updated
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