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README.md

@@ -42,7 +42,7 @@ COLM is a novel autoregressive language model that operates entirely in the comp
 | **Age group estimate** | 84% rated age 13-16 |
 | **Training time** | 8.7 hours |
 | **Hardware** | Single RTX 5060 Ti 16GB |
-| **Tokenizer** | 499-token word+character hybrid |
+| **Tokenizer** | 499-token word+character hybrid (396 word tokens, 98 character fallback) |
 | **Domain** | Theological-philosophical prose |
 
 At 498k parameters — roughly half the size of TinyStories' smallest coherent model — COLM generates thematically coherent philosophical prose at temperature 1 with no spell correction.
@@ -114,8 +114,8 @@ Trained on the [DCDM dataset](https://huggingface.co/datasets/edeneldith/DCDM)
 
 ## Limitations
 
-- **Spelling:** The 499-token vocabulary means most words are assembled from character tokens, producing spelling variation
-- **Single domain:** Trained only on theological-philosophical text; cross-domain performance is untested
+- **Spelling:** The 499-token vocabulary contains 396 whole-word tokens covering common English and corpus-specific domain words; words outside this vocabulary require character-level assembly, producing spelling variation on out-of-vocabulary terms
+- **Single trained model:** The released checkpoint has only generated text in the DCDM theological-philosophical register; cross-domain output from the trained model is untested. The data generation pipeline has been validated across approximately 894,000 tokens of private source material spanning archaeology, theology, mythology, philosophy, political history, intelligence studies, science fiction, and AI research.
 - **Batch size:** Final run used batch_size=4 rather than intended 32 — results are a lower bound
 
 ## Citation