Jun Xiao
Jun Xiao has 4 papers in our library, in 2026, most of them on Reasoning, Language models & pretraining; often with Weiming Lu, Yongliang Shen, Wenqi Zhang.
OpenAlex · h-index 1 · 12 works · 1 citations OpenAlex, 2026-10-14
📄 4 works in our library · 🔗 Cited 1 time in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #11049
Affiliations
Fields, topics and concepts
📚 Shelves 9The field of science this work belongs to (OpenAlex fields).Computer Science · 4 worksArtificial Intelligence · 3 worksArts and Humanities · 3 worksLanguage and Linguistics · 3 worksEngineering · 1 workIndustrial and Manufacturing Engineering · 1 workInformation Systems · 1 workSoftware · 1 workComputer Vision and Pattern Recognition · 1 work
🧭 Topics 2Research subjects and languages the work is about.Reasoning · 2 worksLanguage models & pretraining · 1 work
Works by year
2020s
Papers
- CoVerRL: Breaking the Consensus Trap in Label-Free Reasoning via Generator-Verifier Co-Evolution (2026 · 1 citation)
Label-free reinforcement learning for LLM reasoning usually rewards a model for matching its own majority-voted answer. - UI-Copilot: Advancing Long-Horizon GUI Automation via Tool-Integrated Policy Optimization (2026 · 1 citation)
Multimodal GUI agents perform well on short mobile tasks but break down on long, memory-heavy ones. Existing 7B models lose about 90.9% of their accuracy on MemGUI-Bench. The authors trace these failures to three causes: forgetting earlier observations, losing track of task progress, and making arithmetic mistakes. - Experience-driven Multi-turn Reinforcement Learning for GUI Agents (2026)
Training vision-based GUI agents for long, multi-step tasks involves a trade-off. Single-turn RL methods such as GRPO learn from expert histories, but at deployment the agent must rely on its own past actions, so training and real use differ. Online multi-turn RL closes this gap, but it brings sparse rewards, high… - Pause or Fabricate? Training Language Models for Grounded Reasoning (2026)
The paper examines a failure mode the authors call ungrounded reasoning. When a question lacks a needed fact, a language model often notices the gap early but then invents a value and reasons confidently to a wrong answer. The authors argue that the cause is weak awareness of when valid inference is possible, not…
All works
- CoVerRL: Breaking the Consensus Trap in Label-Free Reasoning via Generator-Verifier Co-EvolutionAnnual Meeting of the Association for Computational Linguistics (ACL)Teng Pan, Yuchen Yan, Zixuan Wang, et al.2026 · 1 citation
- UI-Copilot: Advancing Long-Horizon GUI Automation via Tool-Integrated Policy OptimizationAnnual Meeting of the Association for Computational Linguistics (ACL)Zhengxi Lu, Fei Tang, Guangyi Liu, et al.2026 · 1 citation
- Experience-driven Multi-turn Reinforcement Learning for GUI AgentsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhengxi Lu, Jiabo Ye, Fei Tang, et al.2026
- Pause or Fabricate? Training Language Models for Grounded ReasoningFindings of the Association for Computational LinguisticsYiwen Qiu, Linjuan Wu, Yizhou Liu, et al.2026
- PILOT: Planning via Internalized Latent Optimization Trajectories for Large Language Models ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetHaoyu Zheng et al.2026
Co-authors here
- Weiming Lu ×4
- Yongliang Shen ×4
- Wenqi Zhang ×3
- Fei Tang ×2
- Jin Ma ×2
- Xu Tan ×2
- Yuchen Yan ×2
- Yueting Zhuang ×2
- Zhengxi Lu ×2
- Daoxin Zhang
- Fei Huang
- Guangyi Liu
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL) ×4: ACL 2026 ×3
- Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
Cited by
Updated
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