Large-scale analysis of wild LLM chat logs finds that user interaction patterns stabilize quickly after initial use and correlate with long-term outcomes like retention, creating an agency paradox of limited exploration in unconstrained systems.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.HC 3years
2026 3roles
background 1polarities
background 1representative citing papers
CTEM framework links behavioral history to evolving emotional states with user feedback updates, instantiated as Auri agent and tested in a 21-day study showing gains in naturalness, coherence, and emotional harmony.
citing papers explorer
-
Priming, Path-dependence, and Plasticity: Understanding the molding of user-LLM interaction and its implications from (many) chat logs in the wild
Large-scale analysis of wild LLM chat logs finds that user interaction patterns stabilize quickly after initial use and correlate with long-term outcomes like retention, creating an agency paradox of limited exploration in unconstrained systems.
-
Toward Natural and Companionable Virtual Agents via Cross-Temporal Emotional Modeling
CTEM framework links behavioral history to evolving emotional states with user feedback updates, instantiated as Auri agent and tested in a 21-day study showing gains in naturalness, coherence, and emotional harmony.
- Large Language Lovers: Lived Experiences of Negotiating Agency and Platform Control in AI Companionship