Fix broken Markdown table in Preliminary Experimental Results

README.md

@@ -54,6 +54,7 @@ The latest experiments compare CECP against a conventional FP32 baseline using t
 - Hardware: NVIDIA Tesla T4
 
 | Model | Mean Validation Perplexity ↓ |
+|---|---|
 | Dense FP32 | 18.146 ± 0.298 |
 | **CECP-256** | **17.986 ± 0.082** |
 
@@ -71,20 +72,20 @@ Its compressed projection representation can achieve a nominal one bit per weigh
 
 The effective compression ratio depends on model architecture, layer dimensions, and configuration.
 
-Early experiments have demonstrated substantial projection weight storage reductions. Larger models may benefit from reduced relative representation overhead, although this remains an area of ongoing evaluation.
+Early experiments have demonstrated substantial projection weight storage reductions. Large models may benefit from reduced relative representation overhead, although this remains an area of ongoing evaluation.
 
 ---
 
 ## Potential Scaling
 
-One of the central research questions surrounding CECP is how compression aware learning behaves as model size increases.
+One of the central research questions surrounding CECP is how compression aware learning behaviors as model size increases.
 
 Early experiments suggest that CECP can maintain competitive language model performance across the small model sizes evaluated so far.
 
 Future research will investigate whether these properties extend to:
 
 - 100M–500M parameter language models
-- Billion parameter architectures
+- Billon parameter architectures
 - Larger and more diverse training datasets
 - Longer context applications
 - Local deployment on memory constrained consumer hardware
@@ -95,13 +96,13 @@ However, these scaling benefits remain hypotheses until validated through larger
 
 ## Research Status
 
-**Status: Experimental — Active Development**
+**Status: Experiment — Active Development**
 
-CECP has demonstrated functional compression aware language model training and promising early validation perplexity results.
+CECP has demonstrated functional compression aware language model training and promising early validation perplecity results.
 
 Further work is required to establish its performance against established low bit training and quantization methods at larger scales.
 
-Current research priorities include improving training efficiency, evaluating larger models, verifying end to end deployment memory requirements, and strengthening comparative benchmarks.
+Current research priorities include improving training efficiency, evaluating larger models, verifing end to end deployment memory requirements, and strengthening comparative benckmarks.
 
 The complete implementation and selected technical details are not publicly available at this stage.
 
@@ -117,6 +118,6 @@ The long term vision is to explore whether language models can achieve a better
 
 *CECP — Co-Evolving Compositional Pre-Training*
 
-*Independent experimental AI architecture research.*
+*Indepedent experimental AI architecture research.*
 
-*Preliminary results are not evidence of superiority over production scale language models or established quantization methods.*
+*Preliminary results are not evidence of superiorty over production scale language models or established quantization methods.*