Reasoning about Reasoning: BAPO Bounds on Chain-of-Thought Token Complexity in LLMs 事件

Reasoning about Reasoning: BAPO Bounds on Chain-of-Thought Token Complexity in LLMs arXiv:2602.02909v2 Announce Type: replace Abstract: Inference-time scaling via chain-of-thought (CoT) reasoning is a major driver of state-of-the-art LLM performance, but it comes with substantial latency and compute costs. We address a fundamental theoretical question: how many reasoning tokens are required to solve a problem as input size grows? By extending the bounded attention prefix oracle (BAPO) model--an