Cognitive Fatigue in Autoregressive Transformers: Formalization and Measurement
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Computer Science > Computation and Language
Title:Cognitive Fatigue in Autoregressive Transformers: Formalization and Measurement
Abstract:Autoregressive language models frequently degrade during long-horizon generation, producing repetitive text, losing instruction adherence, and exhibiting unstable entropy. Despite the prevalence of these failures, practitioners lack online diagnostics to detect them in real-time as they occur. We formalize this degradation as cognitive fatigue, a measurable generation-time state characterized by decay in attention to the original prompt, representational drift, and entropy miscalibration. We introduce the Fatigue Index (FI), a lightweight, model-agnostic diagnostic that aggregates these three signals under explicit axioms (monotonicity, boundedness, interpretability) enabling reliable runtime monitoring. Across nine models (1B-13B parameters), FI trajectories exhibit structured temporal dynamics, predict task degradation (AUROC = 0.95) and repetition (Spearman rho = 0.94), and reveal non-monotonic scaling behavior: instruction-tuned models below 3B exhibit faster collapse than base models, with this trend reversing at 7B. Stress analyses further show that FI onset accelerates under longer contexts, middle-positioned evidence, and reduced numerical precision. These results establish cognitive fatigue as a coherent and measurable phenomenon, and position FI as a principled tool for runtime reliability monitoring in production LLM systems.
| Comments: | 9 pages, 7 figures. Accepted at the 43rd International Conference on Machine Learning (ICML 2026) |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.30981 [cs.CL] |
| (or arXiv:2605.30981v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.30981
arXiv-issued DOI via DataCite (pending registration)
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