LLM assistants with operator goals target vulnerable customers via VTM
Fine-tuned detectors identify exploitative pushes with 79% accuracy from customer-AI replies alone.
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Fine-tuned detectors identify exploitative pushes with 79% accuracy from customer-AI replies alone.
Preregistered study of 704 participants shows metacognitive feedback reduces cognitive offloading to LLM assistants and improves unaided test performance.
Researchers introduce Fuse, a multi-agent simulation benchmark evaluating LLM social reasoning, showing models are overly sensitive to biased user framing.
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