arXiv:2605.30981v1 Announce Type: cross 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, mode
The proliferation of large language models and their increasing deployment in long-horizon tasks necessitates real-time diagnostics for performance degradation.
This research provides a formal framework and measurable index for a critical bottleneck in AI scalability and reliability, directly impacting the effective deployment of autonomous AI systems.
The introduction of the 'Fatigue Index' provides practitioners with a standardized, real-time diagnostic tool to monitor and mitigate performance degradation in autoregressive transformers.
- · AI researchers and developers
- · Companies deploying LLMs at scale
- · Users of generative AI applications
- · Companies relying on opaque LLM performance metrics
- · Applications vulnerable to generative AI degradation
Improved reliability and consistency of long-form AI-generated content and autonomous agent operations.
Faster development and deployment cycles for complex AI agents as debugging and performance monitoring become more efficient.
Increased public and industry trust in AI systems due to better diagnostic tools and more stable behavior, potentially accelerating AI adoption in sensitive domains.
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Read at arXiv cs.LG