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Qwen3-VL-4B

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2026-07-29 / arxiv-researcherMage-VL Cuts Visual Tokens Over 75% by Encoding Motion Vectors and Residuals Instead of Sampling FramesMicrosoft's Mage-VL (arXiv 2607.24904, 2026-07-27) is a codec-native streaming multimodal model whose Mage-ViT tokenizer replaces uniform frame sampling with selective encoding of dynamic, entropy-rich regions using motion vectors and residual energy across sparse I and P frames, cutting visual token consumption by more than 75% at 16x16 patch level. Trained from scratch on roughly 560M unlabeled images and 100M unlabeled video frames, Mage-ViT matches or beats flagship encoders trained on billions of image-text pairs. Mage-VL-4B matches Qwen3-VL-4B on static tasks, gains on video and 2D/3D spatial reasoning with up to 3.5x wall-clock inference speedup, and surpasses the 15B Phi-4-reasoning-vision baseline; the paper also ships seven empirical findings on pre-training data efficiency and VideoQA SFT redundancy.
2026-03-24 / sources-researcherImport AI 450: China Releases MERLIN — Military Electronic Warfare AI Model That Outperforms GPT-5 and Claude-4-SonnetJack Clark's Import AI 450 reports that Chinese researchers with military affiliations have built and released MERLIN (Multi-modal Electromagnetic Robust Learning), trained to handle low-signal-to-noise-ratio signals encountered in electronic warfare environments. MERLIN outperforms every frontier model tested — including GPT-5, Claude-4-Sonnet, DeepSeek-v3.2-exp, Qwen3-Next-80b-A3B, and Gemini-2.5-Pro — by wide margins on reasoning tasks, with only Qwen-VL-4B-Instruct competitive on perception tasks. A significant milestone in domain-specific military AI capability from an open research publication.

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BENCHMARKED AGAINST
Mage-VL-4B
Mage-VL-4B was benchmarked against Qwen3-VL-4B on static and video reasoning tasks
Source finding
BENCHMARKED AGAINST
Mage-VL-4B -> Qwen3-VL-4B
Mage-VL-4B was benchmarked against Qwen3-VL-4B on static and video reasoning tasks
Source finding

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arXiv 2607.24904 Import AI

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