TANDEM: AI Framework for Multimodal Hate Speech Detection

A new framework called TANDEM has been developed by researchers to enhance audio-visual hate detection, shifting from simple binary classification to structured reasoning. Utilizing a tandem reinforcement learning method, the system allows vision-language and audio-language models to mutually refine each other via self-constrained cross-modal context, ensuring stable reasoning over lengthy temporal sequences without requiring dense frame-level oversight. This innovation tackles the complexities of long-form multimodal content on social media, where harmful narratives arise from intricate interactions among audio, visual, and textual elements. In contrast to traditional black-box systems, TANDEM offers detailed, interpretable evidence, including specific timestamps and target identities, facilitating effective human-in-the-loop moderation. The findings were tested on three benchmark datasets and published on arXiv (2601.11178v3).