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BioStance reveals that nuanced bioethical discussions on social media can be effectively modeled with high reliability, challenging the limitations of existing stance detection datasets.
LLMs exhibit a surprising phase transition in error patterns as semantic complexity increases, challenging conventional approaches to stance detection.
ARMOR-MAD achieves up to 96.5% accuracy in multi-agent debate tasks by dynamically routing debate processes, showcasing the power of adaptive computation in large language models.
