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

2026-06-29

AI Summary

This work addresses the limitations of generic pretrained models in industrial-scale video and live-stream content moderation, where platform-specific data distributions, policy objectives, and safety constraints are poorly aligned with off-the-shelf solutions, and systematic failure diagnosis and remediation mechanisms are lacking. The paper introduces a diagnostic methodology for audio-visual language models (AVLMs) that pioneers the characterization of model failures through observable feature signatures and establishes a principled mapping between failure categories and targeted intervention strategies, replacing heuristic trial-and-error approaches. Built upon multimodal foundation model architectures and validated on real-world platform traffic, this framework enables precise interventions throughout the model lifecycle. The resulting AVLM system has been deployed across more than 100 regions globally, significantly improving moderation accuracy and traceability for high-noise, semantically ambiguous, and highly diverse content.

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. As a result, platforms must undertake internal model development, naturally turning to shared public research for guidance. However, existing multimodal foundation-model studies primarily report architectures, training recipes, data scaling strategies, and benchmark results, but provide less systematic guidance on how failures should be localized and translated into targeted model-development interventions. Interventions are essential because deployment failures are rarely self-explanatory. Similar failures can originate from different causes. Without targeted interventions, improvement reduces to heuristic trial-and-error, where benchmark improvements are weakly attributable, and failures are difficult to trace to their underlying causes. To address this gap, we present a diagnostic methodology for industry-scale Audio-Visual-Language Models AVLM development. The methodology maps model failures into a taxonomy of observable failure signatures and links each class of failure to an intervention space. We instantiate this methodology across the development and alignment lifecycle of an AVLM foundation model for a large-scale video and live-streaming platform. The resulting system supports over 100 regions and is designed for noisy, ambiguous, and highly diverse content drawn from global platform traffic.

Problem

• AVLM
• failure taxonomy
• model intervention
• content moderation
• multimodal foundation models

Innovation

• failure taxonomy
• targeted intervention
• AVLM
• diagnostic methodology
• content moderation