The Shenzhen Institute for Big Data Research has had four of its research papers accepted by ICML 2026, a top-tier international conference in machine learning. Two of the papers introduce novel optimization techniques for large language models: AdaMeZO, an Adam-style zeroth-order optimizer that reduces memory overhead during fine-tuning, and Romberg-ZOGE, a method for higher-order bias reduction in gradient estimation. Another paper presents SCOPE, a framework for efficient video reasoning that uses a cloud-edge collaborative approach to decompose user queries. The fourth paper, MIMOMamba, proposes a new state-space model that jointly models temporal dependencies and cross-channel interactions with linear efficiency. AI
RANK_REASON The cluster reports on multiple research papers accepted to a top-tier academic conference. [lever_c_demoted from research: ic=1 ai=1.0]
- Adam
- AdaMeZO
- NeurIPS
- ICLR
- LLaMA
- Mamba
- MeZO
- MIMOMamba
- RoBERTa
- Romberg-ZOGE
- SCOPE
- Shenzhen Institute for Big Data Research
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