MemoBench: Top 10 Video Models Fail to Remember Object States During Occlusion
jiqizhixin · x · 2026-07-06
Researchers from Harvard, MIT, and Google introduced MemoBench to evaluate whether video generation models can correctly update an object's state after it disappears from view (e.g., when the camera pans away from melting ice or pouring powder and returns). Evaluations of 10 top-tier video models revealed that none could reliably maintain memory across occluded frames, highlighting a major open challenge in building current world models.
Related event: MemoBench Reveals Top Video Models Fail at Object Permanence(2 posts)→
More from Multimodal
- FLUX.2 Klein Drifts Hard on Character Expressions While Free Gemini Holds Likeness — wacomlover · 2026-09-11
- Tencent Hunyuan releases AuK code and weights on GitHub with ComfyUI and fine-tuning support — aigclink · 2026-09-11
- Tencent open-sources AuK, a unified 1.5B speech generation and editing model — aigclink · 2026-09-11
- Creator turns Bahamut vs Tiamat rivalry into an AI cinematic battle with Midjourney, GPT Image 2 and Seedance — azed_ai · 2026-09-11
- invideo launches AI agent-powered editor to automate repetitive editing tasks — azed_ai · 2026-09-11
- fable 5.1 recreates The Starry Night with 256,157 JavaScript brush strokes — cedric_chee · 2026-09-11