arXiv Machine Learning June 30, 2026 By Shuchang Ye, Jinqiang Yu, Zhujun Xiao, Yajing Kong, Yist Y. Lin, Yang Ma, Jiaxi Liu, Xiaolei Xu, Zheng Yu

From Failure Taxonomy to Intervention: A Diagnostic Methodology for Industry-Scale AVLM in Video and Live-Streaming Platform Moderation

Read the original on arXiv Machine Learning →

arXiv:2606. 30059v1 Announce Type: new Abstract: Industry-scale video and live-streaming moderation imposes requirements that are difficult to satisfy with generic pretrained public models or external APIs, including adaptation to platform-specific data distributions, policy-specific objectives, and product-level safety constraints.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Related stories

UNIVID: Unified Vision-Language Model for Video Moderation

Dynamic Content Moderation in Livestreams: Combining Supervised Classification with MLLM-Boosted Similarity Matching

From Content to Audience: A Multimodal Annotation Framework for Broadcast Television Analytics

AVI-Bench: Toward Human-like Audio-Visual Intelligence of Omni-MLLMs

Do Joint Audio-Video Generation Models Understand Physics?

MatchLM2Lite: A Scalable MLLM-to-Lite Framework for Reproduced Content Identification