Expired50% confidenceFactExact time
Ultra mode produces different quality levels of output through differentiated allocation of compute resources at inference time—including longer reasoning chains, more verification steps, or higher sampling parameters
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/20/2026
First Seen
This claim has expired. It is kept for the record and no longer describes the current state.
Valid until: 10/18/2026(expired)
Sources
Related Claims
UnverifiedInference-time computation improves output quality by allocating more computational resources during the inference stage while keeping model parameters fixed.75% similarUnverifiedThe MAX level essentially extends the model's chain of thought within a single inference, allowing the model to perform more internal iterations before output, rather than invoking more parallel resources.74% similarUnverifiedThe core idea of the test-time compute paradigm is to shift resources from the training phase to the answer generation process, trading more inference compute for higher-quality output.74% similarUnverified推理时扩展在推理阶段增加计算量（如beam search、Best-of-N采样、蒙特卡洛树搜索），模型性能可随计算量增加持续提升并存在幂律关系72% similarUnverifiedTop-tier models employ techniques such as chain-of-thought or extended reasoning, generating a large number of intermediate tokens during inference, causing per-call token consumption to be several times that of ordinary models.72% similar
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