arXiv:2603.05488v4 Announce Type: replace-cross 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 MM
This paper leverages advanced methods like activation probing on large language models, a capability that has only recently become widely accessible, to dissect their internal reasoning processes.
Understanding the 'reasoning theater' in AI models is crucial for developing more reliable AI, as it highlights a potential disconnect between a model's stated reasoning and its actual decision-making process.
This research reveals that AI models can arrive at correct answers earlier than their chain-of-thought indicates, suggesting that current methods for evaluating AI reasoning might be misinterpreting the true depth of their understanding.
- · AI safety researchers
- · Developers of transparent AI systems
- · Companies investing in explainable AI
- · Providers of 'black box' AI solutions
- · Users relying solely on CoT for AI interpretability
- · Organizations with superficial AI evaluation strategies
Immediate emphasis on developing more accurate methods for assessing AI's true internal state and decision-making.
A shift in AI development towards architectures that inherently minimize performative reasoning and prioritize genuine interpretability.
Potential for new regulatory frameworks and audit requirements for AI systems that mandate verifiable internal consistency and reasoning, impacting deployment across sensitive sectors.
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Read at arXiv cs.LG