An agentic architecture with multimodal screening, a five-agent jury, meta-synthesis, and source attribution protocol detects biases in Romanian history textbooks more accurately than zero-shot baselines, achieving 83.3% acceptable excerpts and human preference in 64.8% of blind comparisons.
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Relaxed unitary convolutions for GNNs on meshes balance smoothness preservation with natural smoothing in dynamics, outperforming unitary convolutions and other models on PDEs and weather tasks.
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An Agentic Evaluation Architecture for Historical Bias Detection in Educational Textbooks
An agentic architecture with multimodal screening, a five-agent jury, meta-synthesis, and source attribution protocol detects biases in Romanian history textbooks more accurately than zero-shot baselines, achieving 83.3% acceptable excerpts and human preference in 64.8% of blind comparisons.
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Smoothness Errors in Dynamics Models and How to Avoid Them
Relaxed unitary convolutions for GNNs on meshes balance smoothness preservation with natural smoothing in dynamics, outperforming unitary convolutions and other models on PDEs and weather tasks.