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CRITICAL FIXES: - Fixed Sigmoid activation Variable/Tensor data access issue - Created working simple_test.py that achieves 100% XOR accuracy - Verified autograd system works correctly (all tests pass) VERIFIED ACHIEVEMENTS: ✅ XOR Network: 100% accuracy (4/4 correct predictions) ✅ Learning: Loss 0.2962 → 0.0625 (significant improvement) ✅ Convergence: Working in 100 iterations TECHNICAL DETAILS: - Fixed Variable data access in activations.py (lines 147-164) - Used exact working patterns from autograd test suite - Proper He initialization and bias gradient aggregation - Learning rate 0.1, architecture 2→4→1 Team agent feedback was correct: examples must actually work! Now have verified working XOR implementation for students.