Measuring benchmark optimization in speech recognition
Measuring benchmark optimization in speech recognition
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Introducing Gemini 3.7 Flash
Introducing Gemini 3.7 Flash
In-region inference, open models, and new European infrastructure for sovereign AI.
Mistral is bringing together the inference infrastructure, open models, and long-term commitments Europe needs to control its AI future, and setting a roadmap for the world.
Teaching LLMs to Update Beliefs for Efficient Long-Horizon Interaction
Overview of ABBEL compared to traditional recursive summarization. Beliefs replace the full interaction history as the agent’s working context, and belief grading improves performance by supervising the contents of each belief state.. As task horizons grow, LLM contexts can’t scale forever. Self-summarization enables concise, interpretable contexts, but at a significant performance cost, especially for human assistance domains where high quality data is scarce, e.g., collaborative code generation. We address this with ABBEL: a framework that isolates and supervises the information content of summaries in the form of natural-language belief states. Motivation: the cost of recursive summarization For language models to effectively assist with increasingly complex tasks such as software devel
Introducing Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber
We’re introducing new Gemini models, including Gemini 3.6 Flash, 3.5 Flash-Lite and 3.5 Flash Cyber.
Introducing Gemini 3.5 Flash Cyber
Google introduces Gemini 3.5 Flash Cyber, a lightweight cybersecurity model to find and patch vulnerabilities.
Start building with Nano Banana 2 Lite and Gemini Omni Flash
Start building with Nano Banana 2 Lite and Gemini Omni Flash
Accelerating Gemini Nano models on Pixel with frozen Multi-Token Prediction
Machine Intelligence
Introducing computer use in Gemini 3.5 Flash
Introducing computer use in Gemini 3.5 Flash
Introducing the FFASR Leaderboard: Benchmarking ASR in the Real World
Introducing the FFASR Leaderboard: Benchmarking ASR in the Real World
Introducing Mistral OCR 4
Mistral OCR 4 delivers enterprise document AI with 170-language support, bounding boxes, and self-hosted deployment.
Fluid, natural voice translation with Gemini 3.5 Live Translate
Gemini 3.5 Live Translate brings near real-time, natural speech translation to Google AI Studio, Google Translate and Google Meet.
Measuring the impact of learning with AI in Sierra Leone and beyond
Results from a randomized controlled trial show the potential of Gemini’s Guided Learning feature to boost engagement and accelerate learning.
Introducing physics AI at Mistral: the foundation for engineering acceleration.
A new class of AI models that predict the behavior of physical systems, powering the engineers and hardware products of tomorrow.
Emmi joins Mistral to accelerate the AI-native industry
Emmi joins Mistral to accelerate the AI-native industry
Remote agents in Vibe. Powered by Mistral Medium 3.5.
Introducing Mistral Medium 3.5, remote coding agents in Vibe, plus new Work mode in Le Chat for complex tasks.
Introducing Gemini Omni
Introducing Gemini Omni
Gemini for Science: AI experiments and tools for a new era of discovery
A collection of science tools and experiments to expand the scale and precision of scientific exploration.
Gemini 3.5: frontier intelligence with action
Gemini 3.5 is built to help you execute complex, agentic workflows.
Adaptive Parallel Reasoning: The Next Paradigm in Efficient Inference Scaling
Overview of adaptive parallel reasoning. What if a reasoning model could decide for itself when to decompose and parallelize independent subtasks, how many concurrent threads to spawn, and how to coordinate them based on the problem at hand? We provide a detailed analysis of recent progress in the field of parallel reasoning, especially Adaptive Parallel Reasoning. Disclosure: this post is part landscape survey, part perspective on adaptive parallel reasoning. One of the authors (Tony Lian) co-led ThreadWeaver (Lian et al., 2025), one of the methods discussed below. The authors aim to present each approach on its own terms. Motivation Recent progress in LLM reasoning capabilities has been largely driven by inference-time scaling, in addition to data and parameter scaling (OpenAI et al., 20