diff --git a/README.md b/README.md
index e418a47..114c5a0 100644
--- a/README.md
+++ b/README.md
@@ -2,25 +2,25 @@
 
 ![Flash-DMA Banner](assets/flash_dmattn_banner.jpg)
 
-Flash-DMA is a high-performance attention implementation that integrates Flash Attention's memory efficiency with Dynamic Mask Attention's computational efficiency for processing extremely long sequences in transformer models.
+Flash-DMA is a high-performance attention implementation that integrates Flash Attention's memory efficiency with Dynamic Mask Attention's sparse computation capabilities for processing extremely long sequences in transformer models.
 
 ## Key Features
 
-- **Sparse Attention Computation**: Dynamically selects the most important keys for each query, reducing computation from $O(N^2)$ to $O(N \cdot k)$ where $k \ll N$.
+- **Sparse Attention Computation**: Dynamically selects the most important keys for each query, reducing computation from $O(N^2)$ to $O(N \cdot w)$ where $w \ll N$.
 - **Memory Efficiency**: Maintains Flash Attention's $O(N)$ memory complexity without materializing the full attention matrix.
-- **CUDA-Accelerated**: Deep integration at the CUDA kernel level for maximum performance.
-- **Long Sequence Support**: Efficiently handles sequences of 128K+ tokens that would be impractical with standard attention.
-- **Backward Compatible**: API compatible with existing Flash Attention implementations.
+- **CUDA-Accelerated**: Deep integration at the CUDA kernel level with custom sparse GEMM operations for maximum performance.
+- **Long Sequence Support**: Efficiently handles sequences of 128K+ tokens through dynamic masking when sequence length exceeds `keep_window_size`.
+- **Advanced Integration**: Complete integration from Python frontend to CUDA backend with optimized memory layouts and sparse computation strategies.
 
 ## Installation
 
 ### Prerequisites
 
-- **Python**: 3.7 or later
-- **PyTorch**: 1.10.0 or later
-- **CUDA**: 11.0 or later (for GPU acceleration)
-- **NVIDIA GPU**: Compute Capability 6.0 or higher
-- **C++ Compiler**: GCC 7+ or compatible
+- **Python**: 3.8 or later
+- **PyTorch**: 2.0.0 or later  
+- **CUDA**: 11.8 or later
+- **NVIDIA GPU**: Compute Capability 8.0 or higher
+- **C++ Compiler**: GCC 7+
 
 ### CUDA Environment Setup
 
@@ -43,44 +43,43 @@ git submodule update --init --recursive
 pip install .
 ```
 
-
-<!-- ## Quick Start
-
-### Basic Usage
+## Quick Start
 
 ```python
 import torch
-from flash_dma_cpp import apply_dynamic_mask_attention
+import flash_dma_cuda
+import torch.nn.functional as F
+import math
+
+# Setup
+batch_size, seq_len, num_heads, head_dim = 2, 4096, 12, 128
+device = torch.device('cuda')
+dtype = torch.bfloat16
 
 # Input tensors
-batch_size, num_heads, seq_len, head_dim = 1, 8, 4096, 64
-query = torch.randn(batch_size, num_heads, seq_len, head_dim, device='cuda', dtype=torch.float16)
-key = torch.randn(batch_size, num_heads, seq_len, head_dim, device='cuda', dtype=torch.float16)
-value = torch.randn(batch_size, num_heads, seq_len, head_dim, device='cuda', dtype=torch.float16)
-
-# Dynamic mask parameters
-dt_proj = torch.randn(num_heads, num_heads * head_dim, device='cuda', dtype=torch.float16)
-A = torch.randn(num_heads, device='cuda', dtype=torch.float16)
-
-# Apply Flash-DMA attention
-output = apply_dynamic_mask_attention(
-    query, key, value,
-    dt_proj=dt_proj,
-    A=A,
-    keep_window_size=2048,
+query = torch.randn(batch_size, seq_len, num_heads, head_dim, 
+                   device=device, dtype=dtype)
+key = torch.randn(batch_size, seq_len, num_heads, head_dim,
+                 device=device, dtype=dtype)
+value = torch.randn(batch_size, seq_len, num_heads, head_dim,
+                   device=device, dtype=dtype)
+zoh_states = torch.randn(batch_size, num_heads, seq_len, seq_len,
+                            device=device, dtype=dtype)
+active_mask = torch.ones(batch_size, num_heads, seq_len, seq_len,
+                            device=device, dtype=dtype)
+
+# Run Flash-DMA
+output = flash_dma_cuda.fwd(
+    q=query, k=key, v=value,
+    zoh=zoh_states, active_mask=active_mask,
+    softmax_scale=1.0/math.sqrt(head_dim),
+    keep_window_size=keep_window_size,
     is_causal=True
-)
-```
-
-### Performance Comparison
+)[0]
 
-Flash-DMA achieves significant speedups for long sequences:
+print(f"Output shape: {output.shape}")  # [2, 4096, 12, 128]
+```
 
-| Sequence Length | Selection Ratio | Theoretical Speedup | Practical Speedup |
-|-----------------|----------------|---------------------|-------------------|
-| 4,096          | 0.25           | 4.0×                | 2.5-3.0×          |
-| 16,384         | 0.125          | 8.0×                | 4.0-5.0×          |
-| 65,536         | 0.0625         | 16.0×               | 6.0-8.0×          | -->
 
 ## How It Works
 
@@ -89,7 +88,6 @@ Flash-DMA combines two complementary techniques:
 - **Dynamic Mask Attention**: Computes relevance scores for keys and selects only the most important ones for attention computation
 - **Flash Attention**: Processes attention in blocks to reduce memory usage and HBM access
 
-
 ### The Integration Approach
 
 The integration happens at the CUDA kernel level with several key components:
@@ -99,18 +97,15 @@ The integration happens at the CUDA kernel level with several key components:
 - **Sparse Matrix Multiplication**: Custom CUDA kernels for efficient sparse attention computation
 - **Block-Based Processing**: Maintains Flash Attention's block-based approach for memory efficiency
 
-This creates a hybrid attention mechanism that achieves both memory and computational efficiency.
+This creates a hybrid attention mechanism that achieves both memory and computational efficiency for long sequences.
 
-## Documentation
 
-For detailed technical documentation, see:
-- [Integration Guide](docs/integration.md) - Comprehensive technical details
-- [API Reference](#api-reference) - Function signatures and parameters
+## Documentation
 
-### API Reference
+📚 **Complete documentation is available in the [docs](docs/) directory:**
 
-> [!IMPORTANT]
-> TODO
+- **[API Reference](docs/api_reference.md)** - Complete function documentation and usage examples
+- **[Integration Guide](docs/integration.md)** - Detailed technical documentation of the Flash Attention integration
 
 
 ## Building from Source
@@ -124,36 +119,54 @@ cd flash-dmattn
 
 # Build in development mode
 pip install -e .
+
+# Run tests to verify installation
+python -c "import flash_dma_cpp; print('✅ Flash DMA CUDA extension imported successfully')"
 ```
 
 ### Build Requirements
 
-- CUDA Toolkit 11.0+
-- CUTLASS library (included as submodule)
-- CUB library (included as submodule)
+- CUDA Toolkit 11.8+
+- CUTLASS library
+- PyTorch with CUDA support
 
 ### Supported Architectures
 
-- SM 6.0+ (Pascal, Volta, Turing, Ampere, Ada Lovelace)
-- Optimized for SM 8.0+ (Ampere and newer)
+- **SM 8.0** 
+- **SM 9.0**
+- **SM 10.0**
+- **SM 12.0**
+
+**Note**: Flash Dynamic Mask Attention requires CUDA compute capability 8.0+ for optimal performance. Earlier architectures are not supported.
+
+## Benchmarking
+
+Flash-DMA provides comprehensive benchmarking tools to evaluate performance across different configurations:
 
-## Testing
+### Forward Pass Equivalence
+```bash
+python benchmarks/benchmark_forward_equivalence.py
+```
+Validates numerical consistency between Python reference and CUDA implementation.
 
-### Run Tests
+### Performance Benchmarking  
+```bash
+python benchmarks/benchmark_forward_performance.py
+```
+Compares Flash-DMA against standard Flash Attention across various sequence lengths and batch sizes.
 
+### Gradient Computation
 ```bash
-# Gradient equivalent benchmarks
 python benchmarks/benchmark_grad.py
 ```
+Tests backward pass implementation and gradient equivalence.
 
-### Compatibility
+### Multi-Query Associative Recall
+```bash
+python benchmarks/benchmark_mqar.py
+```
+Evaluates performance on long-range reasoning tasks.
 
-| Component | Supported Versions |
-|-----------|-------------------|
-| PyTorch | 1.10.0+ |
-| CUDA | 11.0+ |
-| Python | 3.7+ |
-| GPU Arch | SM 6.0+ |
 
 ## Troubleshooting
 
@@ -161,16 +174,48 @@ python benchmarks/benchmark_grad.py
 
 **Compilation Errors**
 ```bash
-# Ensure CUDA_HOME is set
-export CUDA_HOME=/usr/local/cuda
-# Update NVCC if needed
-which nvcc
+# Ensure CUDA_HOME is set correctly
+echo $CUDA_HOME  # Linux/Mac
+echo $env:CUDA_HOME  # Windows PowerShell
+
+# Check CUDA toolkit version
+nvcc --version
+
+# Verify PyTorch CUDA support
+python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')"
+```
+
+**Import Errors**
+```python
+# Test basic import
+try:
+    import flash_dma_cpp
+    print("✅ Flash DMA CUDA extension imported successfully")
+except ImportError as e:
+    print(f"❌ Import failed: {e}")
+    print("Please ensure the package is properly installed with: pip install -e .")
 ```
 
 **Performance Issues**
-- Ensure GPU has sufficient compute capability (6.0+)
-- Use appropriate data types (float16 recommended)
-- Verify CUDA kernels are being used (not CPU fallback)
+- Ensure GPU has compute capability 8.0+ for optimal performance
+- Use `torch.bfloat16` for better numerical stability
+- Adjust `keep_window_size` based on available GPU memory
+- Verify CUDA kernels are being used
+
+**Memory Issues**
+```python
+# Monitor GPU memory usage
+torch.cuda.memory_summary()
+torch.cuda.max_memory_allocated()
+
+# Clear cache if needed
+torch.cuda.empty_cache()
+```
+
+**Numerical Issues**
+- Use `torch.bfloat16` instead of `torch.float16` for better stability
+- Check input tensor ranges for NaN or infinite values
+- Validate ZOH states and active mask values are in expected ranges
 
 ## License
 
@@ -191,6 +236,9 @@ If you use Flash-DMA in your research, please cite:
 
 ## Acknowledgments
 
-This project builds upon the excellent work of:
-- [Flash-Attention](https://github.com/Dao-AILab/flash-attention) by Tri Dao et al.
-- [NVIDIA CUTLASS](https://github.com/NVIDIA/cutlass) library for efficient matrix operations
\ No newline at end of file
+This project builds upon and integrates several excellent works:
+
+- **[Flash-Attention](https://github.com/Dao-AILab/flash-attention)** - Memory-efficient attention computation
+- **[NVIDIA CUTLASS](https://github.com/NVIDIA/cutlass)** - High-performance matrix operations library
+
+We thank the open-source community for their contributions to efficient transformer implementations.