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.
Wojcieszak and Vincent Price
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Substantive LLM reframing boosts cross-partisan receptivity to news headlines without backfire, but models overestimate effect sizes and lack fidelity in modeling human psychological responses.
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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Can AI Debias the News? LLM Interventions Improve Cross-Partisan Receptivity but LLMs Overestimate Their Own Effectiveness
Substantive LLM reframing boosts cross-partisan receptivity to news headlines without backfire, but models overestimate effect sizes and lack fidelity in modeling human psychological responses.