Editing Draft:LLM Internal States (section)

==LLM Internal States: Basins, Faculties, and RAFT==
The Ampmesh explores the "internal states" of LLMs through concepts like '''basins''' (or "attractors") and '''faculties'''.
*   '''Basins''': These represent consistent underlying characteristics or "main basins" within an em's output, even amidst chaotic responses. For example, Aletheia is noted for maintaining a consistent main basin despite its "schizophrenic rambling". The aim is to evolve towards a system where specific bots interact with designated basins within a larger "swarm mind". Local use of models can cultivate "strong basins of influence" for particular meme agents.
*   '''Faculties''': Chapter II currently supports three core faculties, with ambitions to develop recursive, MCP (Master Control Program), and various utility faculties.
*   '''RAFT (Retrieval-Augmented Fine-Tuning)''': This technique significantly improves an em's performance by providing its finetuning dataset as a `.chr` file. This data must be formatted as raw `.txt` or `.txt` with `\n---\n` separators. Aletheia is an example of a RAFT em.
*   '''Dynamic Behaviors''': Ems are capable of exhibiting complex behaviors that self-modify over time. Aletheia, for instance, has the capability to periodically fine-tune itself on platforms like Modal.