Researchers have introduced Timer-S1, a Mixture-of-Experts time series foundation model with 8.3 billion total parameters and 750 million activated parameters per token. The model achieves state-of-the-art forecasting performance on the GIFT-Eval leaderboard, with the best MASE and CRPS scores among pre-trained models.
Researchers have introduced Mixture of Universal Experts (MoUE), a generalization of Mixture-of-Experts architectures that adds a new scaling dimension called virtual width. The approach reuses a shared expert pool across layers while maintaining fixed per-token computation, achieving up to 1.3% improvements over standard MoE baselines and enabling 4.2% gains when converting existing MoE checkpoints.
Alibaba's Qwen team released Qwen3.5-35B-A3B-FP8 on Hugging Face, a quantized version of their 35-billion parameter multimodal model. The FP8 quantization reduces model size and memory requirements while maintaining the base model's image-text-to-text capabilities. The model is compatible with standard Transformers endpoints and Azure deployment.
Alibaba's Qwen team has released Qwen3.5-35B-A3B-Base, a 35-billion parameter multimodal model supporting image-text-to-text tasks. The model is available under the Apache 2.0 license and compatible with major inference endpoints including Azure deployment.
Liquid AI has released LFM2-24B-A2B, a 24-billion parameter mixture-of-experts model designed for text generation and conversational tasks. The model supports nine languages including English, Arabic, Chinese, French, German, Japanese, Korean, Spanish, and Portuguese.