Update README.md

README.md

@@ -78,38 +78,38 @@ Sinhala and Devanagari serve as the high-complexity proofs-of-concept. The same
 We evaluated WWHO against frontier models across a 1.5 million sentence code-switched corpus containing Sinhala, Hindi (Devanagari), and English. 
 
 ### 1. Sinhala Efficiency
-|Tokenizer            |       Tokens |     TWR | Chr/Tok |  % Reduction
-|----------------------------------------------------------------------
-|**SGPE(WWHO)         |    6,654,288 |   1.274 |    4.83 |            -**
-|OpenAI (o200k_base)  |   17,360,196 |   3.324 |    1.85 |        61.7%
-|Llama 4 Scout        |   18,157,707 |   3.476 |    1.77 |        63.4%
-|DeepSeek V3          |   29,152,698 |   5.581 |    1.10 |        77.2%
+| Tokenizer | Tokens | TWR | Chr/Tok | % Reduction |
+|---|---|---|---|---|
+| **SGPE(WWHO)** | **6,654,288** | **1.274** | **4.83** | **-** |
+| OpenAI (o200k_base) | 17,360,196 | 3.324 | 1.85 | 61.7% |
+| Llama 4 Scout | 18,157,707 | 3.476 | 1.77 | 63.4% |
+| DeepSeek V3 | 29,152,698 | 5.581 | 1.10 | 77.2% |
 
 ### 2. Hindi (Devanagari) Efficiency
-|Tokenizer            |       Tokens |     TWR | Chr/Tok |  % Reduction
-|----------------------------------------------------------------------
-|**SGPE(WWHO)         |   13,433,554 |   1.181 |    4.29 |            -**
-|OpenAI (o200k_base)  |   18,394,075 |   1.617 |    3.13 |        27.0%
-|Llama 4 Scout        |   19,566,121 |   1.720 |    2.94 |        31.3%
-|DeepSeek V3          |   31,682,218 |   2.786 |    1.82 |        57.6%
+| Tokenizer | Tokens | TWR | Chr/Tok | % Reduction |
+|---|---|---|---|---|
+| **SGPE(WWHO)** | **13,433,554** | **1.181** | **4.29** | **-** |
+| OpenAI (o200k_base) | 18,394,075 | 1.617 | 3.13 | 27.0% |
+| Llama 4 Scout | 19,566,121 | 1.720 | 2.94 | 31.3% |
+| DeepSeek V3 | 31,682,218 | 2.786 | 1.82 | 57.6% |
 
 ### 3. English
-|Tokenizer            |       Tokens |     TWR | Chr/Tok |  % Reduction
-|----------------------------------------------------------------------
-|**SGPE(WWHO)         |    7,240,147 |   1.330 |    4.46 |            -**
-|OpenAI (o200k_base)  |    7,420,527 |   1.364 |    4.35 |         2.4%
-|Llama 4 Scout        |    7,512,843 |   1.381 |    4.30 |         3.6%
-|DeepSeek V3          |    7,904,670 |   1.453 |    4.09 |         8.4%
+| Tokenizer | Tokens | TWR | Chr/Tok | % Reduction |
+|---|---|---|---|---|
+| **SGPE(WWHO)** | **7,240,147** | **1.330** | **4.46** | **-** |
+| OpenAI (o200k_base) | 7,420,527 | 1.364 | 4.35 | 2.4% |
+| Llama 4 Scout | 7,512,843 | 1.381 | 4.30 | 3.6% |
+| DeepSeek V3 | 7,904,670 | 1.453 | 4.09 | 8.4% |
 
 *(Note: Because WWHO routes Latin text directly to the native Tiktoken sequence, English performance is mathematically identical. The minor delta in total tokens emerges solely from boundary crossing mechanics.)*
 
 ### 4. Overall (Mixed-Script)
-|Tokenizer            |       Tokens |     TWR | Chr/Tok |  % Reduction
-|----------------------------------------------------------------------
-|**SGPE(WWHO)         |   27,327,989 |   1.240 |    4.47 |            -**
-|OpenAI (o200k_base)  |   43,174,798 |   1.959 |    2.83 |        36.7%
-|Llama 4 Scout        |   45,236,671 |   2.053 |    2.70 |        39.6%
-|DeepSeek V3          |   68,739,586 |   3.119 |    1.78 |        60.2%
+| Tokenizer | Tokens | TWR | Chr/Tok | % Reduction |
+|---|---|---|---|---|
+| **SGPE(WWHO)** | **27,327,989** | **1.240** | **4.47** | **-** |
+| OpenAI (o200k_base) | 43,174,798 | 1.959 | 2.83 | 36.7% |
+| Llama 4 Scout | 45,236,671 | 2.053 | 2.70 | 39.6% |
+| DeepSeek V3 | 68,739,586 | 3.119 | 1.78 | 60.2% |
 
 - **Zero-Breakage Guarantee**: Validated through exhaustive testing permutations across all supported Abugida scripts (0 violations).  
 - **Full-corpus reconstruction**: 1.5M code-switched sentences encoded and decoded with 0 non-UNK mismatches.