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Introduces JAM, a theory-agnostic framework for personality recognition that uses LLMs as judges to improve metric alignment in prototypical networks, achieving better cross-framework generalization.
Introduces PRISM, a prototype language model architecture that uses sparse, non-negative mixtures of learned prototypes for interpretable sequence modeling, achieving competitive performance and enabling fast training data attribution and model editing without fine-tuning.