Does anyone know of a convincing definition of 'intent' in LLMs (or a way to identify it)?  In model organisms type work, I find it hard to 'incriminate' LLMs.  Even though the output of the LLM will remain what it is regardless of 'intent', I think this distinction may be important because 'intentionally lying' and 'stochastic parroting' should scale differently with overall capabilities.  

I find this hard for several reasons, but I'm highly uncertain whether these are fundamental limitations:

• LLMs behave very differently depending on context.  Asking it about something it did post-hoc elicits a different 'mode' and doesn't necessarily allow us to make statements about its original behavior.
• Mechanistic techniques seem to be good at generating hypotheses, not validating them.  Pointing at a SAE feature activation that says 'deception' does not seem conclusive because auto-interp pipelines often does not include enough context for robust explanations for complex high level behaviors like deception.