Six research papers from professors at the Chinese University of Hong Kong, Shenzhen, have been accepted to ICML 2026, a top-tier machine learning conference. The accepted works span areas such as efficient routing for large language models, risk-aware reasoning, and advanced sequence modeling. Notably, one paper, RACER, introduces a novel routing framework to minimize inference costs while controlling risks, and another, B-PAC Reasoning, offers an online method for efficient reasoning with guaranteed performance loss control. A third paper, MIMOMamba, proposes a new architecture for sequence modeling that jointly models temporal and cross-channel interactions. AI
IMPACT These accepted papers highlight advancements in efficient LLM routing, risk-aware reasoning, and novel sequence modeling architectures, potentially influencing future AI development and deployment.
RANK_REASON Cluster reports on multiple academic papers accepted to a top-tier research conference. [lever_c_demoted from research: ic=1 ai=1.0]
- B-PAC Reasoning
- Chengyao YU
- Feng YIN
- Hao ZENG
- Hongxin WEI
- Huajun ZENG
- Jianguo HUANG
- MIMOMamba
- Richard Cornelius SUWANDI
- Sai HAO
- Wei HUANG
- Yanbo LI
- Yiyong SUN
- Youxin ZHU
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