New Financial AI Dataset Reveals Systematic Reasoning Flaws in Language Models

Researchers introduce SenseAI, a 1,439-sample human-validated dataset capturing financial sentiment reasoning across 40 US equities, designed for RLHF model fine-tuning. The study identifies predictable AI failures including "Latent Reasoning Drift"—where models inject unfounded information—and confidence miscalibration, suggesting structured human feedback can systematically improve financial AI reliability.