Meta BLT: Tokenizer-free, Byte-level LLM

**Meta AI** introduces the **Byte Latent Transformer (BLT)**, a tokenizer-free architecture that dynamically forms byte patches for efficient compute allocation, outperforming **Llama 3** on benchmarks including the CUTE benchmark. The model was trained on approximately **1 trillion tokens** and features a three-block transformer design with local and global components. This approach challenges traditional tokenization and may enable new multimodal capabilities such as direct file interaction without retrieval-augmented generation. Additionally, **Microsoft** announced the **Phi-4 14B** parameter model achieving state-of-the-art results on STEM and reasoning benchmarks, surpassing **GPT-4o**. **DeepSeek AI** launched new vision-language models based on their MoE architecture with sizes ranging from **1.0B to 27B** parameters. **OpenAI** released a new Projects feature for ChatGPT, and **Cohere** introduced their smallest and fastest **Command R7B** model. **Anthropic** published research on "Best-of-N Jailbreaking" vulnerabilities across text, vision, and audio models. Industry discussion highlights a trend of decreasing frontier LLM sizes, with **GPT-4** at approximately **1.8 trillion parameters** compared to newer models.