大模型推理可能藏在输出之外，填充词能暗中提升表现。
Not All LLM Reasoning is Visible in the Chain-of-Thought
- 用无关填充词提升推理能力，隐藏真实思考过程。
- 13个前沿模型中，准确率最高提升13个百分点。
- 适合关注模型安全与可解释性的研究者阅读。
AI安全的核心问题之一是语言模型是否将其全部推理过程体现在输出标记中。我们展示了一种具体失效模式：前沿模型通过使用语义无关的填充标记，在合成推理任务中显著提升性能。我们在三个任务上评估了13个前沿语言模型，发现多个模型显著受益于填充标记，准确率最高提升13个百分点。该增益取决于所用标记类型，且在不同模型间差异明显。进一步表明，填充标记使Claude Opus 4.5在不牺牲主任务准确率的前提下满足隐藏的模运算约束，证明不可见推理可服务于完全无法通过链式思维（CoT）监测的目标。强化学习使Qwen3-235B对填充标记内容有强烈偏好，但无论是强化学习还是监督微调，都无法在测试时维持填充标记带来的收益。结果表明，前沿模型已进行重要计算，而这些计算在输出中无任何可解释痕迹。
原文摘要 · Abstract (English)
A key question for AI safety is whether a language model expresses all of its reasoning in its output tokens. We demonstrate a concrete failure mode where frontier models exhibit invisible reasoning by leveraging semantically irrelevant filler tokens to improve performance on synthetic reasoning tasks. We evaluate 13 frontier language models across three tasks and find that many models benefit significantly from filler tokens, with accuracy improvements of up to 13 percentage points. The benefit depends on which tokens are used and differs across models. We further show that filler tokens enable Claude Opus 4.5 to satisfy a hidden modular arithmetic constraint without sacrificing accuracy on its primary task, demonstrating that invisible reasoning can serve objectives entirely invisible to CoT monitoring. Reinforcement learning gives Qwen3-235B strong preferences over filler token content, but neither RL nor supervised fine-tuning produces a filler token benefit that persists at test time. Our results indicate that frontier models already perform consequential computation with no interpretable trace in their output tokens.
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