Binary Pattern Recognition via Physical State Collapse
TEJAS implements pattern recognition as binary state determination — match or no-match. Following physics principles where measurements yield discrete outcomes, the architecture achieves hardware-speed pattern matching. High-dimensional patterns reduce to 128 binary features through normalization, enabling millions of comparisons per second via XOR operations.
Technical Architecture
1. Character N-gram Extraction
Extraction of overlapping character sequences matching human visual saccades (3-5 characters). This biologically-inspired window size captures the fundamental unit of pattern recognition.
2. Transformation Pipeline
Conversion of extracted n-grams to 128-bit binary fingerprints via TF-IDF → SVD → unit normalization → binary encoding.
TF-IDF Vectorization
Sparse vector representation capturing pattern importance across corpus (~10,000 unique n-grams).
SVD Projection
Truncated SVD to 128 principal components, preserving 95%+ variance.
Unit Normalization
Projection onto unit hypersphere triggers phase collapse to {-1, +1}.
Binary Encoding
Positive → 1, negative → 0. Generates 128-bit fingerprint.
3. Golden Ratio Sampling
For datasets exceeding memory constraints, recursive golden ratio sampling (φ = 1.618...) provides mathematically optimal pattern coverage.
Computational Performance
| Operation | Performance |
|---|---|
| Encoding Rate | 400K docs/sec |
| Search Rate | 5.4M cmp/sec |
| Query Latency (P50) | 1.2ms |
| Query Latency (P99) | 2.0ms |
Memory Efficiency
| System | 6.4M Docs |
|---|---|
| TEJAS | 782 MB |
| BERT | 19.7 GB |
| Elasticsearch | 15.4 GB |
| PostgreSQL | 2.1 GB |
Validation Results
Live Demo
Interactive exploration of Wikipedia fingerprints with real-time search capabilities.
Open Source
Complete implementation including training pipeline, search algorithms, and pre-trained models.