Why LLMs Fail at Causal Discovery and How Interventional Agents Escape 事件

Why LLMs Fail at Causal Discovery and How Interventional Agents Escape arXiv:2605.27567v1 Announce Type: cross Abstract: Causal discovery is a cornerstone of scientific reasoning, yet whether large language models can perform it reliably remains an open question. Recent benchmarks show that even fine-tuned models plateau on simple causal graphs and degrade as complexity grows, but why they fail has not been established. We prove the failure is fundamental: supervised fine-tuning, direct prefere