not much happened today

**Google DeepMind** released **EmbeddingGemma (308M)**, a small multilingual embedding model optimized for on-device retrieval-augmented generation and semantic search, supporting over 100 languages and running efficiently with quantization and EdgeTPU latency under 15ms. **Jina AI** introduced new code-focused embedding models (0.5B/1.5B) with GGUF quantization, achieving state-of-the-art retrieval across multiple languages and tasks. **LightOn** demonstrated large-scale retrieval training without distillation using contrastive training on billions of passages. **Hugging Face** released the **FineVision** dataset with 17.3M images and 9.5B answer tokens for vision-language model training, showing significant benchmark improvements. The **MiniCPM-V 4.5 (8B)** multimodal model reported surpassing **GPT-4o** and **Gemini-2.0 Pro** on OpenCompass benchmarks with innovative video token compression. Microsoft’s **VibeVoice TTS** and Stanford’s Mixture-of-Contexts video generation also featured. Additionally, a Stanford study benchmarked optimizers like Muon, Soap, Mars, and Sophia, finding diminishing speedups over AdamW at larger scales but advantages at smaller scales. The new ChatGPT branching feature was noted for its simplicity and popularity. *"Everyone's a decacorn now."*