RecalibrateGPT: AI Fatigue Resilient Conversational Interfaces

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.

The use of Generative AI Conversational User Interfaces (CUI) as a new way to access and analyze data is growing in all sectors, and the industrial one is no exception. There, large amounts of data produced by IoT devices are flowing through user interfaces and may require them a new adaptation to the new analyses needs of decision-makers. LLM-based CUIs are promising a new way to directly interact with those data through the directness of natural language and without the learning costs that every GUI design has. Moreover, the capabilities of LLMs and their agency open up the possibility to automate some tasks and help with the reasoning during decision-making activities. But are this promises well founded? We try to scope this general question with a mixed-approach study comparing a state-of-the-art dashboard with a conversational agent. A total of 20 participants used both interfaces to complete four simulated industrial decision tasks of varying complexity. We combined measures of mental workload, completion time, and decision accuracy with a post-study questionnaire and semi-structured interviews analyzed through thematic analysis. The findings suggest that the conversational agent can reduce interactional effort by supporting more direct access to information, while the dashboard remains valuable for overview and verification. However, these benefits may vary across tasks and require validation through larger-scale studies.

How much does a user's skill with AI shape what AI actually delivers for them? This question is critical for users, AI product builders, and society at large, but it remains underexplored. Using a richly annotated sample of 27K transcripts from WildChat-4.8M, we show that fluent users take on more complex tasks than novices and adopt a fundamentally different interactional mode: they iterate collaboratively with the AI, refining goals and critically assessing outputs, whereas novices take a passive stance. These differences lead to a paradox of AI fluency: fluent users experience more failures than novices -- but their failures tend to be visible (a direct consequence of their engagement), they are more likely to lead to partial recovery, and they occur alongside greater success on complex tasks. Novices, by contrast, more often experience invisible failures: conversations that appear to end successfully but in fact miss the mark. Taken together, these results reframe what success with AI depends on. Individuals should adopt a stance of active engagement rather than passive acceptance. AI product builders should recognize that they are designing not just model behavior but user behavior; encouraging deep engagement, rather than friction-free experiences, will lead to more success overall. Our code and data are available at https://github.com/bigspinai/bigspin-fluency-outcomes