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Hang Lv
Observed name:
Lv, Hang
Institution:
University of Science and Technology of China
Observed institution:
University of Science and Technology of China
OpenAlex ID:
A5126307103
Learning from Emptiness: De-biasing Listwise Rerankers with Content-Agnostic Probability Calibration
2026
·
Association for Computational Linguistics 2026
, Chen, Zulong
, et al.
·
Underline Science Inc.
SPARD: Self-Paced Curriculum for RL Alignment via Integrating Reward Dynamics and Data Utility
2026
·
Association for Computational Linguistics 2026
, Chen, Enhong
, et al.
·
Underline Science Inc.
IE as Cache: Information Extraction Enhanced Agentic Reasoning
2026
·
Hang Lv
, Sheng Liang
, et al.
·
International Joint Conference on Neural Networks (IJCNN)
PDF
Rethinking the Necessity of Adaptive Retrieval-Augmented Generation through the Lens of Adaptive Listwise Ranking
2026
·
Jun Feng
, Jiahui Tang
, et al.
·
International Joint Conference on Neural Networks (IJCNN)
PDF
The Next Paradigm Is User-Centric Agent, Not Platform-Centric Service
2026
·
Zhang, Luankang
, Lv, Hang
, et al.
·
arXiv (Cornell University)
PDF
Why Thinking Hurts: Diagnosing and Rectifying Linguistic Inertia in Large Language Models for Recommendation
2026
·
Luankang Zhang
, Yonghao Huang
, et al.
·
Conference on Empirical Methods in Natural Language Processing (EMNLP)
PDF
SpecSteer: Synergizing Local Context and Global Reasoning for Efficient Personalized Generation
2026
·
Hang Lv
, Sheng Liang
, et al.
·
Conference on Empirical Methods in Natural Language Processing (EMNLP)
PDF
SPARD: Self-Paced Curriculum for RL Alignment via Integrating Reward Dynamics and Data Utility
2026
·
Xuyang Zhi
, Peilun Zhou
, et al.
·
Proceedings of the conference - Association for Computational Linguistics. Meeting
PDF
Learning from Emptiness: De-biasing Listwise Rerankers with Content-Agnostic Probability Calibration
2026
·
Hang Lv
, Hongchao Gu
, et al.
·
Proceedings of the conference - Association for Computational Linguistics. Meeting
PDF
AsymSpec: Context-Asymmetric Speculative Decoding for Agentic LLMs
2026
·
Sheng Liang
, Yongyue Zhang
, et al.
·
Conference on Empirical Methods in Natural Language Processing (EMNLP)
PDF
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12
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