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Today's specialized video hallucination detectors peak at an abysmal 34.63% accuracy under adversarial conditions, revealing that the verifiers meant to guardrail multimodal foundation models are fundamentally untrustworthy.
Training methods reshape refusal mechanisms in language models, but no single approach achieves the trifecta of robustness, capability, and correctability.
Over-refusal isn't just a misapplication of a global "no" switch; it's deeply intertwined with how LLMs represent and execute specific tasks.
Facial micro-movements betray your cognitive load, revealing a new pathway to real-time workload monitoring using just a webcam.
