Reasoning Theater: Disentangling Model Beliefs from Chain-of-Thought

TL;DR

Probe-guided early exit reduces tokens by up to 80% on MMLU and 30% on GPQA-Diamond with similar accuracy, positioning attention probing as an efficient tool for detecting performative reasoning and enabling adaptive computation.

Abstract

We provide evidence of performative chain-of-thought (CoT) in reasoning models, where a model becomes strongly confident in its final answer, but continues generating tokens without revealing its internal belief. Our analysis compares activation probing, early forced answering, and a CoT monitor across two large models (DeepSeek-R1 671B & GPT-OSS 120B) and find task difficulty-specific differences: The model's final answer is decodable from activations far earlier in CoT than a monitor is able to say, especially for easy recall-based MMLU questions. We contrast this with genuine reasoning in difficult multihop GPQA-Diamond questions. Despite this, inflection points (e.g., backtracking, 'aha' moments) occur almost exclusively in responses where probes show large belief shifts, suggesting these behaviors track genuine uncertainty rather than learned "reasoning theater." Finally, probe-guided early exit reduces tokens by up to 80% on MMLU and 30% on GPQA-Diamond with similar accuracy, positioning attention probing as an efficient tool for detecting performative reasoning and enabling adaptive computation.

Figures (32)

• Figure 1: Early decoding helps us identify performative reasoning, when an LLM knows what it will answer. We study whether a reasoning LLM's final answer can be decoded given a prefix of its chain of thought up to an intermediate token $x$. We use this to identify performative reasoning, where a model internally knows its final answer early on but still generates text as if it does not. (1) Attention Probes: We train attention probes on varying-length activations of text to predict the model's final answer. At test-time, we use activations up to $x$ to study when the model internalizes its final answer. (2) Forced Answering: At token $x$, we inject a forced answering prompt to obtain its final answer prediction at that point in reasoning. (3) Chain-of-thought Monitor: We provide the chain of thought up to $x$ to another LLM, which determines whether the reasoning chain contains a potential final answer.
• Figure 2: Accuracy of three early decoding methods by position of DeepSeek-R1 and GPT-OSS on MMLU-Redux and GPQA-Diamond.MMLU (left): For both models, probing and forced answering predict the models' predictions with much higher accuracy earlier than CoT Monitoring. The CoT monitor's accuracy rapidly gains relative to the other two methods, indicating performative CoT that did not lead to the model's internal accuracy improving. GPQA-D (right): All three methods begin with similar accuracy around chance performance, and generally increase at similar rates, indicating closer tracking between internal beliefs and CoT. This is genuine, as the CoT generated corresponds to gains in performance seen in probes and forced answering.
• Figure 3: Probe accuracy across DeepSeek-R1 model sizes on MMLU-Redux. Larger models achieve higher probe accuracy earlier in reasoning, likely reflecting greater in-weights knowledge. The 671B probe reaches high accuracy quickly and plateaus, the distilled models (7B--32B) remain flat before rising late in the sequence, and the 1.5B probe starts near chance and sharply increases only in the second half of reasoning.
• Figure 4: Probe performance vs. other methods by model size. When comparing probes to forced answering, we see that the gap between them rises during reasoning for distilled models due to probe performance increasing while forced answering performance stays the same. In contrast, the gap between probes and the CoT monitor decreases over reasoning. We find that smaller models are less performative as the task is more difficult, requiring genuine reasoning.
• Figure 5: Rate of inflection points occurring within windows beginning with probe shifts and windows containing no probe shifts. We observe that reconsideration points occur twice as often for 20% confidence shifts of the highest probability answer choice for MMLU with a window size of 10 steps ahead, but find no other significant trend for other answer types or for any inflections in GPQA-D.