{"schema_version":"onlylabs.public_signal.v1","title":"Zhipu AI (GLM) Model: zai-org/SSVAE","description":"Zhipu AI (GLM) model signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/90530351-c08d-4a44-a0dc-083dff68f9a0","json_url":"https://onlylabs.fyi/signals/90530351-c08d-4a44-a0dc-083dff68f9a0/signal.json","generated_at":"2026-06-11T01:33:27.520029+00:00","org":{"slug":"zhipu","name":"Zhipu AI (GLM)","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/zhipu","dossier_json_url":"https://onlylabs.fyi/labs/zhipu/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/90530351-c08d-4a44-a0dc-083dff68f9a0","signal_json":"https://onlylabs.fyi/signals/90530351-c08d-4a44-a0dc-083dff68f9a0/signal.json","source":"https://huggingface.co/zai-org/SSVAE","lab_dossier":"https://onlylabs.fyi/labs/zhipu","lab_dossier_json":"https://onlylabs.fyi/labs/zhipu/dossier.json","analysis":"https://onlylabs.fyi/analysis/zhipu","analysis_json":"https://onlylabs.fyi/analysis/zhipu/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/zhipu/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":"https://onlylabs.fyi/topics/releases","topic_signals_json":"https://onlylabs.fyi/topics/releases/signals.json","topic_feed":"https://onlylabs.fyi/topics/releases/feed.xml","data_business":null},"answer_pack":{"answer":"Zhipu AI (GLM) published zai-org/SSVAE. This model signal is evidence of what shipped on model infrastructure and how the release is positioned. 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Our research identifies properties of video VAE latent spaces that facilitate diffusion training through statistical analysis of VAE latents. Our key finding is that biased, rather than uniform, spectra lead to improved diffusability. Motivated by this, we introduce **SSVAE (Spectral-Structured VAE)**, which optimizes the * *spectral properties** of the latent space to enhance its **\"Diffusability\"**. 🔥 Key Highlights * **Spectral Analysis of Latents**: We identify two statistical properties essential for efficient diffusion training: a **low-frequency biased spatio-temporal..."},"evidence_pages":[{"url":"https://huggingface.co/zai-org/SSVAE/raw/main/README.md","final_url":"https://huggingface.co/zai-org/SSVAE/raw/main/README.md","title":"zai-org/SSVAE model card","http_status":200,"content_type":"text/plain; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-11T01:33:27.520029+00:00","bytes":2533,"raw_path":"c6101efa38869929426f623d088d7daae3665d9cff6139a70d942eb036681238.md","content_hash":"a5e04c458c51e03bcfe85ce41b6afe5b2745a54f420748049632b0a19e218b8d","excerpt_chars":1200,"truncated":true,"excerpt":"--- license: mit --- Delving into Latent Spectral Biasing of Video VAEs for Superior Diffusability [](https://zhazhan.github.io/ssvae.github.io) [](https://arxiv.org/abs/2512.05394) Most existing video VAEs prioritize reconstruction fidelity, often overlooking the latent structure's impact on downstream diffusion training. 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