Evidence for Daily and Weekly Periodic Variability in GPT-4o Performance
arXiv:2602.15889v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in research both as tools and as objects of investigation. Much of this work implicitly assumes that LLM performance under fixed conditions (identical model snapshot, hyperparameters, and prompt) is time-invariant. If average output quality changes systematically over time, this assumption is violated, threatening the reliability, validity, and reproducibility of findings. To empirically examine this assumption, we conducted a longitudinal study on the temporal variability of GPT-4o's average performance. Using a fixed model snapshot, fixed hyperparameters, and identical prompting, GPT-4o was queried via the API to solve the same multiple-choice physics task every three hours for approximately three months. Ten independent responses were generated at each time point and their scores were averaged. Spectral (Fourier) analysis of the resulting time series revealed notable periodic variab
arXiv:2602.15889v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in research both as tools and as objects of investigation. Much of this work implicitly assumes that LLM performance under fixed conditions (identical model snapshot, hyperparameters, and prompt) is time-invariant. If average output quality changes systematically over time, this assumption is violated, threatening the reliability, validity, and reproducibility of findings. To empirically examine this assumption, we conducted a longitudinal study on the temporal variability of GPT-4o's average performance. Using a fixed model snapshot, fixed hyperparameters, and identical prompting, GPT-4o was queried via the API to solve the same multiple-choice physics task every three hours for approximately three months. Ten independent responses were generated at each time point and their scores were averaged. Spectral (Fourier) analysis of the resulting time series revealed notable periodic variability in average model performance, accounting for approximately 20% of the total variance. In particular, the observed periodic patterns are well explained by the interaction of a daily and a weekly rhythm. These findings indicate that, even under controlled conditions, LLM performance may vary periodically over time, calling into question the assumption of time invariance. Implications for ensuring validity and replicability of research that uses or investigates LLMs are discussed.
Executive Summary
This study examines the temporal variability of GPT-4o's performance, querying the model every three hours for approximately three months using a fixed model snapshot, hyperparameters, and identical prompting. The results reveal notable periodic variability in average model performance, accounting for 20% of the total variance. Spectral analysis identifies daily and weekly rhythms as the primary contributors to this periodic variability. The findings challenge the assumption of time invariance in large language model performance, with significant implications for research validity and replicability. This study sheds light on the importance of considering temporal variability in LLM performance, particularly in research that relies on model performance metrics.
Key Points
- ▸ GPT-4o's performance exhibits periodic variability over time, even under controlled conditions.
- ▸ Daily and weekly rhythms contribute significantly to this periodic variability.
- ▸ The assumption of time invariance in large language model performance is challenged.
Merits
Strength in Methodology
The study employs a rigorous longitudinal design, querying GPT-4o every three hours for an extended period, providing robust insights into temporal variability.
Demerits
Limitation in Generalizability
The study's findings may not generalize to other LLMs or tasks, as the experiment is specific to GPT-4o and a multiple-choice physics task.
Expert Commentary
This study highlights the importance of considering temporal variability in LLM performance, which may have significant implications for research validity and replicability. The findings suggest that researchers should carefully evaluate the temporal stability of their models, particularly when relying on performance metrics. Furthermore, the study underscores the need for more sophisticated explainability and transparency techniques to shed light on the underlying mechanisms driving periodic variability in LLM performance. As LLMs become increasingly ubiquitous in research and applications, understanding and accounting for temporal variability will be crucial to ensuring the reliability and trustworthiness of AI-driven results.
Recommendations
- ✓ Future research should investigate the temporal variability of other LLMs and tasks to determine the scope and generalizability of the findings.
- ✓ Developing standardized methods for evaluating and mitigating temporal variability in LLM performance is essential to maintain the integrity of AI-driven research.
