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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.
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
We’re introducing new Gemini models, including Gemini 3.6 Flash, 3.5 Flash-Lite and 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
Machine Intelligence
Introducing computer use in Gemini 3.5 Flash
Introducing the FFASR Leaderboard: Benchmarking ASR in the Real World
Mistral OCR 4 delivers enterprise document AI with 170-language support, bounding boxes, and self-hosted deployment.
Gemini 3.5 Live Translate brings near real-time, natural speech translation to Google AI Studio, Google Translate and Google Meet.
Results from a randomized controlled trial show the potential of Gemini’s Guided Learning feature to boost engagement and accelerate learning.
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
Introducing Mistral Medium 3.5, remote coding agents in Vibe, plus new Work mode in Le Chat for complex tasks.
Introducing Gemini Omni
A collection of science tools and experiments to expand the scale and precision of scientific exploration.
Gemini 3.5 is built to help you execute complex, agentic workflows.
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
Our latest voice model has improved precision and lower latency to make voice interactions more fluid, natural and precise.
Human-Computer Interaction and Visualization
