Research Documentation
This section contains research documents, experimental results, and technical analyses related to the BSBR (Block Sparse with Block Retrieval) architecture.
Available Research Documents
- Background on BSBR Architecture - Theoretical foundations and design principles of the BSBR architecture
- Benchmarks - Performance benchmarks of BSBR compared to other attention mechanisms
- Experiments - Experimental results from various tests and configurations
- BSBR Conversion Research - Research on converting pre-trained models to BSBR architecture
- BSBR Conversion Evaluation - Comprehensive evaluation of converted BSBR models
BSBR Conversion Evaluation
Our most recent research has focused on evaluating the performance and behavior of models converted from standard transformers to BSBR architecture. Key findings include:
- Performance: At moderate sequence lengths (≤1024 tokens), BSBR doesn't yet show performance advantages on CPU, but theoretical advantages are expected at longer sequences
- Scaling: Original transformer scales as O(n^0.34) vs. BSBR as O(n^0.55) in our tests, contrary to expectations
- Output Similarity: Significant divergence in output behavior, with negative cosine similarity and 0% agreement in next-token predictions
- Use Cases: BSBR conversion is most suitable for very long context processing where approximate outputs are acceptable
Overview of Research Focus Areas
- Architectural Innovations
- Block-sparse attention patterns
- Efficient retrieval mechanisms
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Computational complexity improvements
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Conversion of Pre-trained Models
- Weight transfer methodologies
- Equivalence preservation
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Fine-tuning requirements
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Performance Analysis
- Speed benchmarks
- Memory efficiency
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Scaling behavior
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Output and Behavior Analysis
- Output distribution comparison
- Attention pattern visualization
- Next-token prediction agreement