Unverified50% confidenceFactExact time
For reasoning models, TTFT is generally far higher than for ordinary models, because the model must first complete invisible chain-of-thought reasoning before outputting the first visible token.
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50%
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Long-term
Relevance
10/12/2026
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Related Claims
UnverifiedReasoning models can constructively understand never-before-seen scenarios by combining existing knowledge, possessing zero-shot generalization ability, which is difficult for pure perception models to achieve.68% similarVerifiedResearch has found that the chain-of-thought reasoning generated by models does not fully correspond to their actual internal computation processes, and may provide post-hoc explanations unrelated to the true decision-making path.68% similarUnverifiedHidden Chains of Thought do not require explicitly displaying each step of the thinking process in the output, allowing the model to perform reasoning iterations within its internal state space.68% similarUnverifiedTemporal multimodal large language models (TS-MLLMs) leverage the reasoning capabilities of large language models to handle question-answering tasks, but struggle to genuinely capture dynamic temporal patterns, typically providing implicit reasoning that lacks explicit explanation of the logic behind conclusions.67% similarUnverifiedA general backbone model with strong reasoning capabilities is theoretically more likely to generalize to long-tail scenarios it has never encountered.66% similar
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