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This paper presents a computational framework for detecting and interpreting dynamic team-process phases in collaborative virtual reality using late chunking and change-point detection to analyze temporal changes in team communication.
An open-source LLM benchmark with 147 coding tasks runs every 4 hours, using 5-trial median with 95% confidence intervals and CUSUM for change-point detection, sparking discussion on its methodology.
This paper argues for a sequential inference framework to enhance LLM trustworthiness by modeling interactions as dependent stochastic processes, ensuring validity under repeated use, and enabling online monitoring for behavioral shifts.