Research has found that large language models, or LLMs, have significant flaws in their reasoning capabilities. Studies have shown that these models can collapse or give up when faced with complex problems, leading to a collapse in accuracy. The extent of this issue appears to be widespread, with some describing it as a "crippling failure" to induce robust models of the world.