GAZE:Governance-Aware pre-annotation for Zero-shot World Model Environments

• URL: http://arxiv.org/abs/2510.14992v1
• Date: Tue, 07 Oct 2025 21:13:03 GMT
• Title: GAZE:Governance-Aware pre-annotation for Zero-shot World Model Environments
• Authors: Leela Krishna, Mengyang Zhao, Saicharithreddy Pasula, Harshit Rajgarhia, Abhishek Mukherji,
• Abstract summary: Training robust world models requires large-scale, precisely labeled multimodal datasets.<n>We present a production-tested GAZE pipeline that automates the conversion of raw, long-form video into rich, task-ready supervision.
• Score: 1.6398143439811486
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training robust world models requires large-scale, precisely labeled multimodal datasets, a process historically bottlenecked by slow and expensive manual annotation. We present a production-tested GAZE pipeline that automates the conversion of raw, long-form video into rich, task-ready supervision for world-model training. Our system (i) normalizes proprietary 360-degree formats into standard views and shards them for parallel processing; (ii) applies a suite of AI models (scene understanding, object tracking, audio transcription, PII/NSFW/minor detection) for dense, multimodal pre-annotation; and (iii) consolidates signals into a structured output specification for rapid human validation. The GAZE workflow demonstrably yields efficiency gains (~19 minutes saved per review hour) and reduces human review volume by >80% through conservative auto-skipping of low-salience segments. By increasing label density and consistency while integrating privacy safeguards and chain-of-custody metadata, our method generates high-fidelity, privacy-aware datasets directly consumable for learning cross-modal dynamics and action-conditioned prediction. We detail our orchestration, model choices, and data dictionary to provide a scalable blueprint for generating high-quality world model training data without sacrificing throughput or governance.

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