TBS is an interval-based multi-agent LLM simulation framework that separates structured internal evaluative states from public utterance generation and shows these states vary systematically with turn-allocation, silence, and memory conditions.
In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25)
2 Pith papers cite this work. Polarity classification is still indexing.
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High agreeableness in LLM voice assistants increases older adults' empathy perceptions and real-time explanations outperform history-based ones, but personality does not affect perceived intelligence.
citing papers explorer
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Think-Before-Speak: From Internal Evaluation to Public Expression in Multi-Agent Social Simulation
TBS is an interval-based multi-agent LLM simulation framework that separates structured internal evaluative states from public utterance generation and shows these states vary systematically with turn-allocation, silence, and memory conditions.
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The Differential Effects of Agreeableness and Extraversion on Older Adults' Perceptions of Conversational AI Explanations in Assistive Settings
High agreeableness in LLM voice assistants increases older adults' empathy perceptions and real-time explanations outperform history-based ones, but personality does not affect perceived intelligence.