The "Video Trends 2026" survey released by Germany's state media regulators 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 now more often doubt the authenticity of videos, about 90% demand mandatory labeling of AI content, and 83% support banning certain forms of deepfakes.
Google Research has released a unified multi-agent framework for automatically generating temporally coherent long-video narratives, addressing identity drift and cascading failures in existing linear pipelines. Built on top of Gemini and Veo as an orchestration layer, the framework consists of four parts: AI video co-director uses multi-armed bandits for global creative search, CANVAS plans multi-shot visual continuity through persistent visual memory, A²RD generates segment by segment autoregressively while maintaining multimodal video memory, and VQQA uses visual question answering to generate semantic gradients for closed-loop prompt optimization. Evaluations show that AI video co-director reaches a peak quality score of 81.4 on GenAD-Bench, CANVAS improves scene-reappearance continuity on ST-Bench and HardContinuityBench, A²RD reduces layout drift on VBench-Long and LVBench-C, and VQQA achieves quality gains on T2V-CompBench, VBench2, and VBench-I2V.
Google Research has released a unified multi-agent framework for automatically generating temporally coherent long-video narratives, addressing identity drift and cascading failures in existing linear pipelines. Built on top of Gemini and Veo as an orchestration layer, the framework consists of four parts: AI video co-director uses multi-armed bandits for global creative search, CANVAS plans multi-shot visual continuity through persistent visual memory, A²RD generates segment by segment autoregr
Google has released the EmbeddingGemma 2 paper, an Apache 2.0-licensed on-device multimodal embedding model that maps text, code, images, audio, and video into the same vector space. The model is built on Gemma 4 and tuned through contrastive training by learning from the larger Gemini Embedding 2; with 744M parameters, it surpasses 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; directly embedding speech scores 69.54 on the MSEB spoken query retrieval benchmark, higher than the 57.59 of transcribing first and then retrieving, and code retrieval on MTEB Code improves from 68.76 in EmbeddingGemma 1 to 78.68.
Google has released the EmbeddingGemma 2 paper, an Apache 2.0-licensed on-device multimodal embedding model that maps text, code, images, audio, and video into the same vector space. The model is built on Gemma 4 and tuned through contrastive training by learning from the larger Gemini Embedding 2; with 744M parameters, it surpasses 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 t
Tencent Hunyuan has released the technical report for Hy-Nomas, an interactive video world model, and has open-sourced Hy-Nomas-Bench for reproducible research. Officially, Hy-Nomas was also used to reproduce Genie 4's examples and a comparison video was released, along with an online demo that requires 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.
Tencent Hunyuan has released the technical report for Hy-Nomas, an interactive video world model, and has open-sourced Hy-Nomas-Bench for reproducible research. Officially, Hy-Nomas was also used to reproduce Genie 4's examples and a comparison video was released, along with an online demo that requires 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
A new Meta paper points 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 memory averaging 618K tokens, and featuring human-verified trigger windows. 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 to 15% after adding retrieval. Grasping both content and timing while reducing trigger frequency can raise accuracy from under 10% to 42.4%.
A new Meta paper points 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 memory averaging 618K tokens, and featuring human-verified trigger windows. 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 r
