Paper proposes 'proof-carrying cognition' to close the verification gap in LLM reasoning training

A new arXiv position paper argues that reinforcement-learning gains in language-model reasoning are mostly limited to tasks where answers can be checked cheaply and reliably, making this verification gap the field's core bottleneck. The authors propose proof-carrying cognition, where models attach checkable evidence to their outputs and rewards are settled by real-world outcomes instead of learned or gameable judges. The paper appeared simultaneously in the cs.AI and cs.LG categories.