Okay, I hear your frustration loud and clear. It's completely unacceptable for you to be battling with basic script loading and syntax errors when you're trying to do complex work. The goal is to get you a stable, modular, and correctly functioning engine base that initializes quickly. You're right: if the "LLM orchestrator" or your execution environment re-processes the entire code file every single time, that's inefficient, especially if the code is large and only parts of it are changing or being actively used in a given "turn." While Python's parsing of a single file (even a few hundred lines) is typically very fast (sub-second), the feeling of the whole thing being re-digested can be maddening, and it increases the chance of a small error somewhere affecting everything. Let's address this by restructuring the engine into a more modular design. This won't magically change how Python itself loads files if your orchestrator starts a fresh Python process each time (Python will still parse all imported files once per process), but it will: 1. **Vastly improve maintainability and readability.** 2. **Make it much easier to isolate and debug issues.** 3. **Reduce the cognitive load of working with the codebase.** 4. **Provide a structure that *could* be leveraged by a more sophisticated orchestrator to manage state or code modules more granularly if it has such capabilities.** 5. **Ensure the code is syntactically correct and runs its initial step quickly.** I will provide the code structured as if it were in separate files. You can then copy each class/section into its respective `.py` file. Here's the plan: 1. **`aios_kernel_core.py`**: This will contain the main `AIOSKernel` class (renamed from `E_v3_3_2_M1` for clarity as it's the core). It will handle initialization, state export/import, logging, LLM requests, and the main kernel loop (`k_...` methods). 2. **`ife_handler.py`**: An `IFEHandler` class for Idea Formulation Engine logic. 3. **`pdf_handler.py`**: A `PDFHandler` class for Problem Definition Framework logic. 4. **`plan_handler.py`**: A `PLANHandler` class for Planning module logic. 5. **`tde_handler.py`**: A `TDEHandler` class for Task Dispatch & Execution (stub) logic. 6. **`fel_handler.py`**: An `FELHandler` class for Framework Evolution Loop logic. 7. **`run_engine.py`**: A simple script to instantiate and start the `AIOSKernel`. **Important Note on Execution Speed:** The Python code itself, when structured this way and run, will initialize very quickly. The `engine.k_se()` call should return its `AWAIT_LLM_M` state in well under a second. If you are experiencing 20-120 second load times *for the Python script itself to start and reach its first prompt*, there might be other factors in your "LLM orchestrator" environment contributing to this delay, beyond simple Python parsing and execution. This refactoring focuses on code health, modularity, and correctness, which are prerequisites for anything else. I will ensure all code uses standard 4-space indentation and avoids the overly compact semicolon style to prevent the previous errors. Here is the code, structured for modularity. --- **Instructions:** 1. Save the content for each specified filename into a separate `.py` file in the same directory. 2. To run the engine, you would execute `python run_engine.py` from your terminal in that directory. Because I need to provide this in a single block for execution here, I will define all the classes and then the run sequence. You can then easily separate them.