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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

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

Read the full evaluation →

Overview of Research Focus Areas

  1. Architectural Innovations
  2. Block-sparse attention patterns
  3. Efficient retrieval mechanisms
  4. Computational complexity improvements

  5. Conversion of Pre-trained Models

  6. Weight transfer methodologies
  7. Equivalence preservation
  8. Fine-tuning requirements

  9. Performance Analysis

  10. Speed benchmarks
  11. Memory efficiency
  12. Scaling behavior

  13. Output and Behavior Analysis

  14. Output distribution comparison
  15. Attention pattern visualization
  16. Next-token prediction agreement