Your comment sparked a thought that might be of value. If LLMs are indeed primarily useful for solving simpler tasks and will systematically balk at complex pr

Your comment sparked a thought that might be of value. If LLMs are indeed primarily useful for solving simpler tasks and will systematically balk at complex problems, perhaps a variant of the “thinking” approach is of value? Where a task that is high iteration is approached by solving a part of the problem and then identifying the next -small part- of the problem, in an iteration loop. I can also see this easily going off the rails, but perhaps posing each following iteration as -small- and encapsulating only the relevant context while trimming off the context tail could work as an agentic behavior spawned from the main supervisory task? Of course maybe just having the model write and run code would probably be better for many classes of problems. Maybe something to be looked at, if it’s not already being used.