Progress in model reasoning: chain of thought, reasoning models, and breakthroughs and disputes on maths and logic benchmarks.
Liquid AI released DSpark draft model checkpoints for LFM2.5-1.2B-Instruct, LFM2.5-2.6B and LFM2.5-8B-A1B. Speculative decoding accelerates decoding without changing output quality, increasing throughput by up to 3.18 times on GPUs and 2.87 times on devices.
Google Research released the Chain-of-Evidence (CoE) verifiability framework, implemented in a Science One Framework prototype, with automated CoE Audit metrics.
Figure 1: CUDA-to-MLX optimization translation map. CUDA optimization knowledge can be translated into architecture-native MLX strategies rather than copied instruction-for-instruction. We face a new epoch in computing. Hardware is changing rapidly — not just faster GPUs, but a growing range of chips from different vendors, each with its own architecture and often tailored to specific AI workloads.
Thinking Machines released Inkling on Hugging Face, a multimodal MoE model with around one trillion parameters, a one-million-token context and native image, text and audio inputs, alongside Inkling-Small with 276 billion total and 12 billion active parameters.
Mistral released Leanstral 1.5 under Apache-2.0, with 119B total and 6B active parameters and major formal verification improvements. It achieves 100% on miniF2F, solves 587 of 672 PutnamBench problems, and sets current best results of 87% on FATE-H and 34% on FATE-X.
Google Research published an architecture attaching multi-token prediction (MTP) heads to frozen Gemini Nano v3 models, accelerating on-device inference without changing backbone weights. It has shipped with the Pixel 9 and 10 series.
Google DeepMind released experimental open-source model DiffusionGemma, generating text blocks in parallel through text diffusion and achieving up to 4 times faster inference on dedicated GPUs.
Google Research released TurboQuant, compressing the KV cache to three bits without loss of model accuracy or training and fine-tuning, alongside the QJL and PolarQuant methods.
Mistral AI released Mistral Small 4, the next major version in the series and its first model to combine Magistral reasoning, Pixtral multimodal capabilities and Devstral agentic coding in one model, under Apache 2.0.
Mistral AI released Leanstral, the first open-source coding agent for Lean 4, using a sparse architecture with 6B active parameters. Its weights are available under Apache 2.0, and it is integrated into Mistral vibe and the free labs-leanstral-2603 API endpoint.
Mistral added features to Le Chat including preview Deep Research, Voxtral-powered voice mode, multilingual reasoning with Magistral, Projects for organising conversations and image editing with Black Forest Labs.
Mistral AI released its first reasoning model, Magistral, comprising the 24B open-source Magistral Small and enterprise-focused Magistral Medium.