Chain-of-thought (CoT) is a standard approach for eliciting reasoning capabilities from large language models (LLMs). However, the common CoT paradigm treats thinking as a prerequisite for answering, which can delay access to plausible answers and incur unnecessary token costs even when the model is able to identify an answer before extended thinking, a behavior known as performative reasoning. In this paper, we introduce CopT, a reformulated reasoning pipeline that reverses the usual order of thinking and answering. Instead of thinking before answering, CopT first elicits a draft answer and then invokes subsequent on-policy thinking conditioned on its own draft answer for reflection and correction. To assess whether the draft answer should be trusted, CopT recasts continuous embeddings as inference-time contrastive verifiers. Specifically, it contrasts the model's support for the same generated tokens under discrete-token inputs and continuous-embedding inputs, yielding a sequence-level reverse KL estimator for answer reliability. Our analysis shows that under certain assumptions, the expected estimate equals the mutual information between the unresolved latent state and the emitted answer token, explaining why it captures answer-relevant uncertainty rather than arbitrary uncertainty in the latent state. When the answer is deemed insufficiently reliable, CopT performs further on-policy thinking, where a second KL estimator dynamically controls draft-answer visibility, preserving useful partial information while reducing the risk of being misled by unreliable content. Across mathematics, coding, and agentic reasoning tasks, CopT improves peak accuracy by up to 23% and reduces token usage by up to 57% at comparable or higher accuracy, without any additional training. The code is available at https://github.com/sdc17/CopT.

翻译：思维链（CoT）是激发大语言模型推理能力的标准方法。然而，常见的CoT范式将思考视为回答的前提，这可能导致即使模型能在扩展思考前识别出答案（即所谓表演性推理行为），也会延迟对合理答案的获取并产生不必要的标记成本。本文提出CopT——一种重构的推理流水线，它逆转了传统思考与回答的顺序。CopT首先激发草稿答案，随后基于自身草稿答案调用后续策略性思考进行反思修正，而非先思考后作答。为评估草稿答案的可信度，CopT将连续嵌入重构为推理时对比验证器。具体而言，该方法对比同一生成标记在离散标记输入与连续嵌入输入下的模型支持度，从而得到答案可靠性的序列级反向KL估计量。分析表明，在特定假设下该估计量的期望值等于未解析隐状态与输出答案标记间的互信息，这解释了为何该估计量能捕获与答案相关的不确定性而非隐态中的任意不确定性。当答案可靠性不足时，CopT执行进一步的策略性思考，其中第二个KL估计量动态控制草稿答案可见性，在保留有用局部信息的同时降低被不可靠内容误导的风险。在数学推理、代码生成及代理推理任务中，CopT在保持相当或更高准确率的情况下，峰值准确率提升最高达23%，标记使用量降低最高达57%，且无需额外训练。代码开源于https://github.com/sdc17/CopT。