You've raised absolutely crucial points about managing state, handling long processes, and the strategic options for evolving the engine, especially given the execution constraints. Thank you for these reminders and insights. **Regarding State Preservation and Long Executions:** * **The Core Challenge:** You're right, the 600-second timeout and the lack of true instance/class persistence between `tool_code` calls are the main hurdles. My current workaround (re-defining the class and manually resetting state for each minimal step) is functional but inefficient and still risks timeouts if any single step, even after class parsing, becomes too complex. * **Saving State (Ideal but Requires Engine Modification):** The most robust solution would be, as you suggested, to modify the `AIOS_Engine` itself to include methods for serializing its complete internal state to a recoverable format (e.g., JSON) and deserializing it. * `engine.export_state() -> json_string` * `engine.import_state(json_string)` This would allow us to: 1. Run a step. 2. Export state. 3. If the next step is complex, terminate the current run. 4. Start a new run (new `tool_code` block), include the class definition, instantiate `engine`, import the saved state, and then proceed. This is a significant TID for the engine itself: "Implement State Serialization/Deserialization." * **Multi-Thread Strategy:** Your idea of using different chat threads is very insightful. * **Option A (Generate New Code, Run in New Thread):** After a set of TIDs are applied and the engine (via LLM) generates a new version of its own script (`engine_artefact_text`), we could terminate the current simulation. I (as the orchestrator) would then take that new script, and you (as the user) would start a *new chat thread* with me, providing that new script as the baseline. This naturally resets execution time and uses the evolved code. This is clean but means the CCO/operational history within the *engine's memory* is lost unless explicitly saved and reloaded (which again points to state serialization). * **Option B (Alternating Threads for Complex Steps - More Complex):** This is an advanced concept. If a single FEL-MH step (like "apply TIDs to model") is itself too long for one `tool_code` block even with the class definition, we'd need a way for the engine to break that *internal* step into sub-steps, save its partial progress (e.g., within `_fel_s`), and request an LLM action. Then, if that LLM action is also very long, it's harder to manage with thread switching unless the *LLM itself* can be made to operate in a way that its "thinking" can be paused and resumed, which is usually not the case for a single LLM call. This seems more challenging with the current AIOS design where cognitive tasks are single LLM requests. **Self-Termination and Way Forward:** * This is a perfectly valid strategy, especially if an evolution cycle is complete or if we hit an intractable timeout. The key is, as you said, "as long as we have a way forward." * A "way forward" could be: * The newly generated engine script. * A serialized state of the CCO (if implemented). * A clear log of what was achieved and what the next intended step was. **Current Simulation Path (FEL-MH):** For the immediate next step in our current FEL-MH simulation, we are still within one conceptual "evolution proposal" cycle. The engine has: 1. Received TIDs. 2. (Is about to) Receive the calculated next version string. 3. Will then ask the LLM to apply TIDs to its conceptual model of the engine. 4. Will then ask the LLM to regenerate the engine script based on this evolved model. This "regenerate engine script" step is where the new code is produced. At that point, terminating and starting fresh with the new script in a new thread would be a natural transition. **Let's proceed with simulating Turn 5: Providing the calculated next version string.** I will use the "full class definition + minimal single step with manual state restoration" approach, hoping it stays within limits. Simulated LLM response for `COGNITIVE_TASK_FEL_CALC_VERSION` (from previous turn's output): ```json { "next_version_string": "3.3.1-evolved", "status": "Success" } ``` The engine will call `engine.run_mh_fel_step4_process_version()` with this. State to restore before this call: * `engine.current_context_mh = "FEL-MH"` * `engine.Kernel_CurrentMH_ID = "FEL-MH"` * `engine._fel_s` should contain: * `"current_engine_representation": "Conceptual representation of current Python script's logic and structure."` * `"loaded_tids": [...]` (the two TIDs we provided in the previous successful step). * `engine.CCO_data` and `engine.Kernel_ActiveCCO_JsonString` can remain `None` or minimal, as FEL-MH doesn't heavily rely on a CCO for its own operations until the very end when it logs its result.