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Google Research releases a multi-agent framework for AI video co-directors

Google Research has released a multi-agent framework for automatically generating temporally coherent long-form video narratives, aimed at alleviating identity drift and cascading failure issues in existing linear pipelines. The framework is built on Gemini and Veo and comprises four parts: AI video co-director, CANVAS, A2RD, and VQQA, which are respectively responsible for creative orchestration, visual storyboarding, long-duration generation, and closed-loop optimization. Evaluations show that AI video co-director achieves a peak quality score of 81.4 on GenAD-Bench, and A2RD can generate ten-minute-long videos while maintaining character and environment consistency. No further progress has been disclosed at present.

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English translation is being prepared.

Google Research has released a multi-agent framework for automatically generating temporally coherent long-form video narratives, aimed at alleviating identity drift and cascading failure issues in existing linear pipelines. The framework is built on Gemini and Veo and comprises four parts: AI video co-director, CANVAS, A2RD, and VQQA, which are respectively responsible for creative orchestration, visual storyboarding, long-duration generation, and closed-loop optimization. Evaluations show that AI video co-director achieves a peak quality score of 81.4 on GenAD-Bench, and A2RD can generate ten-minute-long videos while maintaining character and environment consistency. No further progress has been disclosed at present.

View story:Google Research releases a multi-agent framework for AI video co-directors

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English translation is being prepared.

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English translation is being prepared.

English translation is being prepared.

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Google Releases EmbeddingGemma 2 Paper

Google has released the EmbeddingGemma 2 paper, introducing an Apache 2.0-licensed on-device multimodal embedding model that maps text, code, images, audio, and video into the same vector space. Built on Gemma 4, the model learns from the larger Gemini Embedding 2 and is tuned with contrastive training; with 744M parameters, it outperforms models 2 to 12 times larger on multiple tasks. On the Pixel 11 Pro, the quantized text weights occupy about 191MB of active memory, while the full multimodal model takes about 567MB; the context window is expanded 4 times to 8K tokens, accommodating about 5.5 minutes of audio, 29 images, or 58 video frames. Embedding speech directly scores 69.54 on the MSEB spoken query retrieval benchmark, higher than the 57.59 achieved by transcribing first and then retrieving, and code retrieval on MTEB Code improves from 68.76 in EmbeddingGemma 1 to 78.68. There are currently no further developments.

Tencent Hunyuan Releases Hy-Nomas Technical Report

On October 30, 2026, Tencent Hunyuan released the technical report for Hy-Nomas, an interactive video world model, and open-sourced Hy-Nomas-Bench for reproducible research. The official team also reproduced Genie 4's examples using Hy-Nomas and released comparison videos, and provided an online demo with no waitlist. Earlier introductions stated that Hy-Nomas supports free interaction with text prompts and camera trajectories, long-horizon generation with world memory, lightweight LoRA adaptation for fields such as robotics, and optimization for inference on consumer-grade GPUs. There are currently no further developments.

Landesmedienanstalten-Studie: Mehrheit stößt regelmäßig auf

Germany's state media regulators published the "Video Trends 2026" study on October 23, 2026. The survey shows that 79% of respondents have encountered AI-generated or heavily modified videos online, most concentrated on TikTok and Instagram; among users who encountered such content, 68% mistook it for real at least once. 81% of respondents said they are now more often doubtful about the authenticity of videos, about nine in ten respondents demand mandatory labeling of AI content, and 83% support banning certain forms of deepfakes. There have been no subsequent developments so far.

Meta paper argues that proactive assistants fail at autonomously judging when to interrupt

On October 21, 2026, Meta published a paper pointing out that the failure point of proactive assistants (such as AI glasses) lies in autonomously judging when to speak, not just what to say. The team built the ProPerMem benchmark, using first-person video paired with interference-rich full-year memories averaging 618K tokens, and featuring manually verified trigger windows. The paper reports that all baselines scored at most 15%, even frontier models using retrieval-augmented memory; when oracle evidence was provided within the window, GPT-5.3-Chat reached 96%, but when it was left to choose the timing itself, this dropped to 38%, and after adding retrieval it fell to 15%. Grasping both content and timing while reducing trigger frequency can raise accuracy from under 10% to 42.4%. The work is currently released as a paper, with no subsequent progress yet.

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