The Shrinking Lifespan of LLMs in Science
Scaling laws describe how language model capabilities grow with compute and data, but say nothing about how long a model matters once released. We introduce time-to-peak and lifespan as measures of model obsolescence and use them to characterize the scientific adoption trajectories of 62 LLMs across more than 108k citing papers (2019-2025), separating active adoption from background citation to recover per-model trajectories that citation counts cannot resolve. We find that a model's longevity is shaped more by when it was released than by its characteristics: release year predicts time-to-peak and lifespan more strongly than architecture, openness, or scale. LLM adoption follows an inver...
| Authors | Ana Trišović |
|---|---|
| Primary category | cs.DL |
| Published | 8 April 2026 |
| Last updated at source | 25 September 2026 |
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