Haipai Research
AI & ML interests
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Recent Activity
Haipai Research
Overview
Haipai Research is an independent AI research organization focused on developing efficient, scalable, and mathematically grounded learning systems.
Our work emphasizes clarity of design, strong inductive biases, and open experimentation, particularly in regimes where compute and data are constrained.
We aim to explore alternatives to standard large-scale training paradigms by combining theoretical insight with practical model-building.
Research Focus
Our primary research directions include:
- Efficient language models and small-to-mid scale architectures
- Alternative learning rules beyond standard backpropagation
- Representation learning and structured reasoning
- Mathematical foundations of deep learning
- Model compression, quantization, and optimization
- Architecture-level efficiency and memory-aware design
Philosophy
We believe that progress in AI does not come solely from scale, but from:
- better inductive structure
- principled training dynamics
- transparent and reproducible research
Our goal is to build systems that are understandable, efficient, and robust, while remaining fully open to the research community.
Open Research & Reproducibility
- All models, code, and experiments are released openly when possible
- Results are reported with clear assumptions and limitations
- Emphasis on ablations, comparisons, and reproducibility
Intended Use
Models released under Haipai Research are intended for:
- academic research
- experimentation and benchmarking
- educational and exploratory purposes
They are not intended for high-risk or safety-critical applications without further evaluation.
Limitations
- Models may be trained on limited data or compute
- Experimental architectures may not be fully optimized
- Results should be interpreted as research findings, not production guarantees
Contact
For collaboration, discussion, or research questions:
- Hugging Face organization page
- Associated repositories and papers linked per model
Citation
If you use models or ideas from Haipai Research in your work, please cite the corresponding repository or paper when available.
