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Surprising insights emerge as low-parameter models accurately replicate the behavior of complex LLM-agent societies, challenging the notion that high computational power is essential for meaningful simulations. WHY_IT MATTERS: This approach could democratize access to agent-based modeling, allowing researchers with limited resources to explore complex interactions in LLM societies without sacrificing accuracy.
Detecting hallucinations in language models is significantly improved by considering the temporal context of tokens, achieving an AUC of 0.840 without relying on model internals.
Delayed verification can turn consensus into oscillation, with the most unstable regime occurring when communication and verification delays coincide.
