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Frontier LLMs fail to reliably identify and apply domain-specific rules in complex, multi-file scenarios, with top models scoring below 63% on core reasoning tasks.
Real-time emotion predictions can be made more reliable by selectively invoking multimodal reasoning only when necessary, improving accuracy without sacrificing speed.