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Cultural cues can mislead language models into choosing the wrong answers even when they select the correct normative framework, exposing a significant knowledge gap in AI systems.
Systems achieved up to 97.5% accuracy in multilingual financial question answering, revealing the potential for high-performance AI across diverse languages.
The top-performing systems in multilingual financial question answering are separated by less than one percentage point, showcasing the intense competition and subtlety in model performance.
Mandate Salience Decay can lead to a 4.4x behavioral gap in financial agents over time, revealing critical vulnerabilities in their long-term deployment.
