Gemini for Science
Gemini for Science powers scientific discovery with AI tools and resources built to support scientific endeavors. It brings together experimental tools on Google Labs and science workflows in Google Antigravity to accelerate research, sharpen reasoning, and help researchers explore the future of AI-powered scientific discovery. Literature Insights synthesizes scholarly literature to identify new research opportunities, create grounded research artifacts, and extract paper data into queryable tables mapped directly to source evidence. Hypothesis Generation uses a multi-agent system that simulates the scientific method to identify knowledge gaps, generate potential research directions, and propose testable research plans for breakthrough discoveries. Computational Discovery helps researchers discover models and algorithms by using an agentic research engine that generates and scores code variations based on user-defined optimization metrics.
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Mira
Mira is a web-based AI scientist platform for scientific and materials R&D. It helps researchers review literature, organize evidence, develop hypotheses, plan computational workflows, and summarize results. Task-oriented AI agents combine project context with scientific tools and computation to support work from research question through analysis and reporting. Use cases include chemistry and materials research, molecular design, protein-related workflows, and reproducing published procedures. Project workspaces help retain context across tasks, while workflow views and generated reports let users inspect and organize outputs. Mira is designed for scientists and R&D teams seeking assistance with literature-driven investigation and computational research; its outputs support, rather than replace, expert scientific judgment.
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GLM-5.3
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GLM-5.3 is Z.ai’s frontier coding model designed for complex software engineering, long-horizon agent tasks, and advanced post-training research. The model uses the same base model as GLM-5.2, with improvements coming from scaled post-training across more environments, more diverse tasks, and larger compute investment. GLM-5.3 delivers stronger coding performance, better task ownership, improved benchmark results, and greater efficiency across realistic development workflows. It is built to handle complex coding tasks, production-style engineering work, research environments, automation tasks, and agentic workflows that require multi-step execution. The model also shows emergent cyber capabilities in vulnerability discovery and exploitation-chain reasoning, with safety evaluation and hardening planned before open-weight release.
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Inkling
Inkling is an open-weights multimodal AI model from Thinking Machines designed as a customizable foundation model for developers, researchers, and enterprises. The model is a Mixture-of-Experts transformer with 975 billion total parameters, 41 billion active parameters, and support for context windows up to 1 million tokens. Inkling was trained from scratch on text, images, audio, and video, giving it native capabilities across reasoning, coding, agentic tool use, vision, audio, factuality, and instruction following. It is built with controllable thinking effort so users can balance performance, latency, and token efficiency for different workloads. The model is available for fine-tuning on Tinker, with playground access, API availability through ecosystem partners, and full weights published on Hugging Face. Built for customization, Inkling gives teams an open-weights base model for building domain-specific AI systems, multimodal agents, coding workflows, research tools, and more.
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