chore: Remove temporary documentation files from tests/

Removed files created during debugging:
- tests/regression/GRADIENT_FLOW_TEST_SUMMARY.md (info now in test docstrings)
- tests/debug_posenc.py (temporary debug script)

Test organization is clean:
- Module tests: tests/XX_modulename/
- Integration tests: tests/integration/
- Regression tests: tests/regression/ (gradient flow tests)
- Milestone tests: tests/milestones/
- System tests: tests/system/

All actual test files remain and pass.

tests/regression/GRADIENT_FLOW_TEST_SUMMARY.md

@@ -1,119 +0,0 @@
-# Gradient Flow Test Suite Summary
-
-## Overview
-Comprehensive test suite verifying gradient flow through all NLP components and regression tests for previously fixed bugs.
-
-## Test Files
-
-### 1. `test_gradient_flow_fixes.py`
-**Purpose**: Regression tests to ensure gradient flow bugs don't reoccur
-
-**Tests**: 9 tests covering critical fixes
-- ✅ Batched 3D matmul (np.dot → np.matmul)
-- ✅ Transpose preserves requires_grad
-- ✅ Subtraction has backward (SubBackward)
-- ✅ Division has backward (DivBackward)
-- ✅ LayerNorm gradient flow (Tensor operations)
-- ✅ Embedding preserves requires_grad
-- ✅ Dropout uses Tensor operations
-- ✅ Transpose has backward (TransposeBackward)
-- ✅ MatmulBackward uses batched operations
-
-**Status**: 9/9 PASS ✅
-
-### 2. `test_nlp_components_gradient_flow.py`
-**Purpose**: Comprehensive gradient flow tests for all NLP modules
-
-**Tests**: 9 tests covering all components
-
-#### Module 10 - Tokenization
-- ✅ Encode/decode functionality
-- Note: No gradients (preprocessing only)
-
-#### Module 11 - Embeddings
-- ✅ Embedding lookup gradient flow
-- ✅ EmbeddingBackward scatter-add
-- ✅ Sparse gradient updates
-- ✅ PositionalEncoding gradient flow
-
-#### Module 12 - Attention
-- ✅ Scaled dot-product attention (Q, K, V gradients)
-- ✅ Causal masking preserves gradients
-- ✅ Multi-head attention (all 4 projections)
-- ✅ Reshape/permute preserve graph
-
-#### Module 13 - Transformer
-- ✅ LayerNorm (gamma, beta gradients)
-- ✅ MLP (both layers)
-- ✅ TransformerBlock (10 parameters)
-- ✅ Full GPT model (37 parameters)
-
-**Status**: 9/9 PASS ✅
-
-## Key Findings
-
-### All Parameters Receive Gradients
-- **Token embeddings**: ✅
-- **Position embeddings**: ✅
-- **Q/K/V projections**: ✅
-- **Attention output projection**: ✅
-- **LayerNorm (gamma, beta)**: ✅
-- **MLP layers (linear1, linear2)**: ✅
-- **LM head**: ✅
-
-### Critical Components Verified
-1. **Embedding lookup**: Sparse gradient accumulation works correctly
-2. **Multi-head attention**: All projections receive gradients
-3. **Transformer blocks**: Complete gradient flow through all paths
-4. **Residual connections**: Don't break gradient flow
-5. **Full GPT model**: End-to-end gradient flow verified
-
-## Test Execution
-
-### Run All Gradient Flow Tests
-```bash
-pytest tests/regression/test_gradient_flow_fixes.py -v
-pytest tests/regression/test_nlp_components_gradient_flow.py -v
-```
-
-### Run Individual Tests
-```bash
-# Regression tests only
-python tests/regression/test_gradient_flow_fixes.py
-
-# NLP component tests only  
-python tests/regression/test_nlp_components_gradient_flow.py
-```
-
-## Results Summary
-
-```
-Total Tests: 18
-- test_gradient_flow_fixes.py: 9/9 PASS ✅
-- test_nlp_components_gradient_flow.py: 9/9 PASS ✅
-
-All gradient flow tests: 18/18 PASS ✅
-```
-
-## Verified Modules
-
-| Module | Component | Gradient Flow | Tests |
-|--------|-----------|---------------|-------|
-| 01 | Tensor | ✅ | matmul, transpose, reshape |
-| 02 | Activations | ✅ | Softmax, GELU |
-| 03 | Layers | ✅ | Linear, Dropout |
-| 05 | Autograd | ✅ | All backward functions |
-| 10 | Tokenization | N/A | (preprocessing) |
-| 11 | Embeddings | ✅ | Embedding, PositionalEncoding |
-| 12 | Attention | ✅ | Single-head, Multi-head |
-| 13 | Transformer | ✅ | LayerNorm, MLP, Block, GPT |
-
-## Conclusion
-
-✅ **All NLP components have correct gradient flow**
-✅ **No regressions detected in previously fixed bugs**
-✅ **Full transformer architecture verified end-to-end**
-✅ **Ready for training**
-
-The gradient flow test suite provides comprehensive coverage and confidence that the transformer implementation is correct and all parameters will update during training.
-