GPT5.4 with any effort level is scary when you combine it with tricks like symbolic recursion. I actually had to reduce the effort level to get the model to stop trying to one shot everything. I struggled to come up with BS test cases it couldn't dunk in some clever way. Turning down the reasoning effort made it explore the space better.
The model can call a copy of itself as a tool (i.e., we maintain actual stack frames in the hosting layer). Explicit tools are made available: Call(prompt) & Return(result).
The user's conversation happens at level 0. Any actual tool use is only permitted at stack depths > 0. When the model calls the Return tool at stack depth 0 we end that logical turn of conversation and the argument to the tool is presented to the user. The user can then continue the conversation if desired with all prior top level conversation available in-scope.
It's effectively the exact same experience as ChatGPT, but each time the user types a message an entire depth-first search process kicks off that can take several minutes to complete each time.
Each stack frame has its own isolated context. This pushes the token pressure down the stack. The top level conversation can go on for days in this arrangement. There is no need for summarization or other tricks.
Is this related to the paper on Recursive Language Models? I remember it mentioned something similar about "symbolic recursion", but the way you describe it makes it sound too simple, why is there an entire paper about it?
The RLM paper did inspire me to try it. This is where the term comes from. "Symbolic" should be taken to mean "deterministic" or "out of band" in this context. A lot of other recursive LLM schemes rely on the recursion being in the token stream (i.e.. "make believe you have a call stack and work through this problem recursively"). Clearly this pales in comparison to actual recursion with a real stack.
