AI RESEARCH
Energy-Based Constraint Networks: Learning Structural Coherence Across Modalities
arXiv CS.CL
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ArXi:2605.00960v1 Announce Type: cross We nstrate the framework in two domains. In text, the system achieves 93.4% accuracy on trained corruption types and 87.2% on 9 unseen types, using frozen BERT and 7.4M trainable parameters. In vision, the same architecture achieves competitive deepfake detection: 0.959 AUC on FaceForensics++ Deepfakes and 0.870 on Celeb-DF without any Celeb-DF