Video Generation Meets Agent Harness: Agentic Video Generation, Agentic World Modeling and Recursive Self-Improvement

Key points

The report defines the agent harness as the executable runtime that constructs context, exposes tools, maintains state, routes feedback, and enforces budgets, and applies it across video generation.
In agentic video generation, the survey organizes how harnesses coordinate script and shot planning, asset management, shot production, editing, and watermarking around shared project state.
In agentic world modeling, it analyzes designs such as an executable game harness where a lightweight game engine maintains the world state and an autoregressive video model renders the resulting frames.
It reports real-time cases including a compute-side rate of about 32 frames per second on one H100 and a system with 112 milliseconds from key press to display.
On recursive self-improvement, it separates revisions of the current output from updates to harness state, generator parameters, and the development procedure itself, and states that no surveyed system yet revises the procedure that trains and validates the generator.
Open challenges include autonomous video production, lifelong memory, hybrid code-pixel generation, human-free model development, and provenance and cost accounting.

External research summaries. These are not HDATF publications or measured product results.