Markdown source

Video Discovery for Agentic World-Model Training

Conference Context

Session Description

Physical AI had its “Attention Is All You Need” moment with the rise of Vision-Language-Action models. The next bottleneck is data: not just more video, but the ability to find the exact real-world moments that teach models how the world works: gravity, motion, causality, human behavior, and object interactions. This session explores a new approach: discovering specific scenes from the vastness of the web. We’ll show how teams can search for moments like objects falling, people interacting with environments, or actions unfolding over time, then collect and structure only the relevant clips for training and evaluation. Attendees will learn how scene-level discovery changes multimodal data pipelines, reducing wasted collection, processing, storage, and review, while making it easier to build targeted datasets for VLA systems, robotics, physical AI, and agentic world models.

Media Evidence

From MCP to Scale: Pipelines That Build Themselves — Rafael Levi, Bright Data (speaker-match related prior/adjacent AI Engineer video; captions: English auto-captions).

Evidence Graph

This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.

Media Signals

Agent Reading Notes

Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence.

Transcript Status

Related video transcript availability: English auto-captions. Treat this as supporting context, not a recording of this exact scheduled session unless later confirmed. Not fetched yet.

People

Synthesis

Synthesized Breakdown

Video Discovery for Agentic World-Model Training ## Conference Context - Date/time: 2026-07-01 · 1:30pm-1:50pm - Track/room: track TBD · Expo Stage 4 SE - Speaker(s): Rafael Levi - Session type/status: session · confirmed - Track: track TBD - Room: Expo Stage 4 SE - Session type: session - Status: confirmed ## Session Description Physical AI had its “Attention Is All You Need” moment with the rise of Vision-Language-Action models. The next bottleneck is data: not just more video, but the ability to find the exact real-world moments that teach models how the world works: gravity, motion, causality, human behavior, and object interactions. This session explores a new approach: discovering specific scenes from the vastness of the web. We’ll show how teams can search for moments like objects falling, people interacting with environments, or actions unfolding over time, then collect and structure only the relevant clips for training and evaluation.

Speaker And Company Context

Topics Covered

Derived Links And Source Material

Novel Concepts / Clever Methods

Evidence Boundary

This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.