Why LLMs Fail at Causal Discovery and How Interventional Agents Escape

This research paper reveals that large language models fundamentally fail at causal discovery due to their inability to distinguish between causal graphs generating similar observational data. It introduces a "kernel obstruction theorem" to formalize this intrinsic limitation of current learning paradigms.