Cognitive load in LLMs: When LLMs are faced with syntactic complexity (Lisp/J parentheses/RL-NOP), distractors (cat facts), or unfamiliar paradigms (right-to-left evaluation), the model’s performance degrades because its "attention bandwidth" is split or overwhelmed. This mirrors human cognitive overload.
My question: is there a way to reduce cognitive load in LLMs?, one solution seems to be process the input and output format so that the LLM can use a more common format. I don't know if there is a more general solution.
My question: is there a way to reduce cognitive load in LLMs?, one solution seems to be process the input and output format so that the LLM can use a more common format. I don't know if there is a more general solution.
Edit: Cat attack https://the-decoder.com/cat-attack-on-reasoning-model-shows-...