What question did this study set out to answer?

The research aims to investigate how long-form conversations with LLMs unfold over time.

January 18, 20260 citationsOpen Access

Beyond Content: Temporal Dynamics of Long-Form Human-LLM Interaction

Key Points

The research aims to investigate how long-form conversations with LLMs unfold over time.
Analyzed 25 extended conversations with a consistent human participant.
Examined response-magnitude trajectories to assess interaction dynamics.
Identified repeatable interaction regimes and their sensitivity to sequential order.
Multiple distinct interaction regimes emerged during conversations.
Regimes were robust against random perturbations but sensitive to changes in order.
Findings suggest that temporal structure is critical in understanding Human-LLM interactions.

Abstract

This preprint presents an exploratory analysis of temporal dynamics in long-form Human-LLM interaction. Rather than focusing on semantic content or task performance, the study examines how conversations unfold over time when represented as normalized response-magnitude trajectories. Analyzing 25 extended conversations involving a single human participant with a consistent interaction style, we observe the emergence of multiple repeatable interaction regimes characterized by distinct temporal envelopes. These regimes are robust to stochastic perturbation of response magnitude but degrade under temporal scrambling, indicating sensitivity to sequential order rather than static statistics. The work is decriptive and conditional in scope and does not claim generalization across users, models, or interaction contexts. It is intended to highlight temporal structure as an underexplored dimension of Human-LLM interaction and to provide a methodological starting point for further investigation of long-form conversational dynamics.

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