Phase 3: Neural-Symbolic Synthesis via Custom ggml Kernels

[github-actions]

Phase 3: Neural-Symbolic Synthesis via Custom ggml Kernels\n\n## Objective\nEngineer custom ggml kernels for seamless neural-symbolic computation and inference.\n\n## Implementation Steps\n- [ ] Implement symbolic tensor operations in ggml framework\n- [ ] Design neural inference hooks for AtomSpace integration\n- [ ] Create custom operations for cognitive primitives\n- [ ] Optimize tensor computations for cognitive workloads\n- [ ] Implement gradient-free symbolic reasoning\n- [ ] Validate tensor operations with real data (no mocks)\n- [ ] Document kernel API, tensor shapes, performance metrics\n- [ ] Create comprehensive performance profiles\n- [ ] Implement memory-efficient tensor management\n\n## Technical Specifications\n- Symbolic Operations: Logic-preserving tensor manipulations\n- Neural Integration: Seamless neural network interoperability\n- Memory Management: Efficient tensor allocation and deallocation\n- Performance Targets: 10x improvement over reference implementation\n- Precision Requirements: Maintain logical exactness in symbolic operations\n\n## Success Criteria\n- ✅ Custom ggml kernels handle all cognitive tensor operations\n- ✅ Neural-symbolic synthesis maintains logical consistency\n- ✅ Performance meets or exceeds baseline implementations\n- ✅ Memory usage remains within acceptable bounds\n- ✅ Integration with AtomSpace is seamless and efficient\n\nPart of the Distributed Agentic Cognitive Grammar Network development cycle.