Chatsparent: An Interactive System for Detecting and Mitigating Cognitive Fatigue in LLMs

TL;DR

Chatsparent is an interactive system that detects and mitigates cognitive fatigue in large language models during chat interactions, enhancing transparency and reliability through real-time signals and interventions.

Contribution

This work introduces a novel interactive demo that surfaces real-time fatigue signals in LLMs and enables user-initiated interventions to improve output stability.

Findings

Real-time fatigue signals can predict model coherence loss

Interventions like attention resets improve output stability

User interface enhances understanding of LLM behavior

Abstract

LLMs are increasingly being deployed as chatbots, but today's interfaces offer little to no friction: users interact through seamless conversations that conceal when the model is drifting, hallucinating or failing. This lack of transparency fosters blind trust, even as models produce unstable or repetitive outputs. We introduce an interactive demo that surfaces and mitigates cognitive fatigue, a failure mode where LLMs gradually lose coherence during auto-regressive generation. Our system, Chatsparent, instruments real-time, token-level signals of fatigue, including attention-to-prompt decay, embedding drift, and entropy collapse, and visualizes them as a unified fatigue index. When fatigue thresholds are crossed, the interface allows users to activate lightweight interventions such as attention resets, entropy-regularized decoding, and self-reflection checkpoints. The demo streams live text and fatigue signals, allowing users to observe when fatigue arises, how it affects output quality, and how interventions restore stability. By turning passive chatbot interaction into an interactive diagnostic experience, our system empowers users to better understand LLM behavior while improving reliability at inference time.