Unverified60% confidenceFactExact time
A 2023 Stanford University study found that when finer-grained evaluation metrics are used, capability improvements are smooth and gradual, and emergent phase transitions may be an artifact of evaluation metric selection.
2
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60%
Confidence
Long-term
Relevance
7/8/2026
First Seen
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并非一切都值得消耗Token：何时用AI，何时用确定性代码
hackernewshackernews7/6/2026
Related Claims
VerifiedA 2023 Stanford paper argued that emergence is a measurement artifact caused by the choice of evaluation metrics, and that with smooth metrics the capability improvement is continuous.85% similarUnverifiedIn 2023, Stanford research challenged emergent abilities, pointing out that so-called 'emergence' may be an artifact of the choice of evaluation metrics, and that when re-evaluated using metrics with better continuity, many seemingly abrupt capability jumps actually appear as smooth, gradual improvements.76% similarUnverifiedAgainst the backdrop of increasingly homogenized large-model evaluation, decomposing evaluation dimensions itself may bring new discoveries, and MAPLE's research suggests that asking what happens behind the score is more meaningful than chasing leaderboard rankings.67% similarUnverifiedResearchers found that predictive metrics and correlation metrics are affected by the regularity of the observation grid and the smoothness of trajectories, such that the more regular the data and the smoother the trajectories, the better certain metrics appear.67% similarUnverifiedQuantization requires a small batch of calibration data to measure activation distributions; after a model version update the distribution changes, the old calibration parameters no longer apply, and recalibration is required, otherwise the accuracy loss will noticeably magnify.66% similar
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