Is AI reasoning right for the wrong reasons?

A 2025 paper from Northeastern University and the University of California, Berkeley found that 30% to 60% of the "thinking steps" in frontier open-source LRMs had "minimal causal impact" on their answers to math questions. Removing these steps barely affected performance, suggesting that chain-of-thought prompts may not always be linked to the final output. While some state-of-the-art LRMs are guided by "normal" software, like agentic AI systems or Google DeepMind’s AlphaProof Nexus, researchers are also interested in understanding stand-alone reasoning models that rely solely on their self-generated reasoning traces.