Turing

Last week we released the Open MM-RL Dataset and we are happy to report that it’s been trending on Hugging Face ever since! A PhD-level multimodal STEM benchmark built for verifiable reasoning across physics, chemistry, biology, and math. Four STEM domains, one dataset -Physics: Quantum and Particle Physics, Condensed Matter and Materials, Electromagnetism, Photonics, and Plasma Systems, Astrophysics and Space Physics -Mathematics: Algebra and Structure, Discrete Mathematics, Analysis and Continuous Mathematics, Probability and Geometry -Biology: Evolutionary Systems, Molecular Mechanisms, Cellular Processes and Neural Biology -Chemistry: Chemical Structure, Reaction Mechanisms, Synthesis, Spectroscopy and Properties The bar is raised, download today: https://lnkd.in/gWTsNtXJ For more datasets, go to the first comment.

One of the reasons we started Turing was because we believed geography is the wrong reason to miss out on great work. Today, we're a $2.2B platform helping the world's leading companies build and deploy AI, and we partner with all of the top Frontier Labs . Our mission shapes everything about how we work. We are client first, because our clients' success is the ultimate measure of our value. We work at startup speed, because momentum is the foundation of perfection. And we are AI forward, not just in what we build for clients, but in how we work every single day. We're looking for people who are energized by that kind of challenge. People who want to move fast, think big, and help redefine how companies and talent find each other in the age of AI. If that sounds like you, we'd love to meet you. Explore open roles at https://careers.turing.com.

Open-MM-RL is trending at #1 on Hugging Face! This is a strong signal that the community wants harder, cleaner datasets for frontier model evaluation and training, and that the community is actively seeking datasets that make multimodal evaluation more rigorous. Take a look and tell us what you think below.

Join us for an evening of insights, light fare, and drinks as we explore Turing’s perspective on the evolving role of LLMs and the future of AI. This event offers a unique opportunity to connect with AI researchers advancing today’s SOTA foundation models and enterprise AI leaders driving real-world innovation. Register here to see the exact location of this event: https://lnkd.in/gafHkfCJ

We built web environments designed to break the best computer-use agents in the world. Not by being buggy. By being realistic. 500+ verifier-backed tasks. 50%+ model-breaking difficulty. Dynamic verification that never goes stale. The full case study on how Turing built production-grade RL environments for browser agent training is live now: https://lnkd.in/g4hm6p_z

Open-MM-RL is trending at #1 on Hugging Face! This is a strong signal that the community wants harder, cleaner datasets for frontier model evaluation and training, and that the community is actively seeking datasets that make multimodal evaluation more rigorous. Take a look and tell us what you think below.

The next frontier in AI reasoning isn't just harder math, it's harder multimodal reasoning. Today, Turing released 𝐎𝐩𝐞𝐧-𝐌𝐌-𝐑𝐋: 𝐚 𝐏𝐡𝐃-𝐥𝐞𝐯𝐞𝐥 𝐦𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐒𝐓𝐄𝐌 𝐝𝐚𝐭𝐚𝐬𝐞𝐭 𝐝𝐞𝐬𝐢𝐠𝐧𝐞𝐝 𝐟𝐨𝐫 𝐭𝐞𝐚𝐦𝐬 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚𝐧𝐝 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐧𝐠 𝐟𝐫𝐨𝐧𝐭𝐢𝐞𝐫 𝐫𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 𝐦𝐨𝐝𝐞𝐥𝐬. Here's what makes it different: → Covers Physics, Mathematics, Biology, and Chemistry at PhD difficulty → Goes beyond single-image QA includes multi-panel and multi-image problem structures → Deterministic, answer-verifiable outputs, purpose-built for reward modeling and RL training → Relevant for both rigorous evaluation and large-scale training workflows We're starting with ~40 public tasks on Hugging Face to share what we're building and get early feedback from the community. If your team is working on multimodal reasoning, scaling RL for reasoning models, or pushing the limits of frontier-model benchmarking — we'd love to hear from you. Explore the dataset and learn more: https://lnkd.in/gK6uc4ik

Building AI that truly understands documents means going far beyond clean, structured text. In our latest case study, Turing delivered a large-scale document understanding dataset designed to reflect the real complexity AI agents face in production environments. We’re talking about: • 15,000+ tasks across OCR, summarization, and translation • 10+ document subdomains, from handwritten notes to financial reports • 10+ languages supported • 95%+ summarization accuracy across single and multi-page documents But scale was only part of the challenge. -This dataset had to handle: -Rotated scans, multi-column layouts, and dense mathematical content -Strict preservation of structure, including tables, checkboxes, and formatting -Multi-page coherence across documents up to 15+ pages -Zero tolerance for hallucinations in summaries To make this work, we built a hybrid pipeline combining automated sourcing, human validation, structured annotation, and multi-layer QA, including agentic review and dual human passes. The result is a production-ready dataset that helps AI systems: • Extract structured information from complex documents • Generate accurate, constraint-based summaries • Scale document understanding across languages and formats If you're building AI systems that interact with real-world documents, this is the level of rigor required. Read the full case study to see how we did it: https://lnkd.in/g3aWTymV

Benchmarks don't show you where AI actually breaks. They show you a model performing well on controlled tests. Clean inputs, known outputs, no ambiguity. That's not your business. Your business has messy data, undocumented processes, and workflows that evolved over years for reasons nobody fully remembers. The models that win in production aren't the ones with the best scores. They're the ones that were tested against reality, failed, got fixed, and got tested again. Most AI projects don't fail at the model level. They fail at the deployment level. And that gap is exactly where the work happens. Learn more: https://lnkd.in/gm9REDez

Three days. One goal: stronger alignment and collaboration. Our Delivery PM Offsite in Goa brought the team together to align on priorities, refine how we work, and prepare for the next phase of growth and scaling. Beyond the sessions, it was about building real connections, creating space for conversations, shared context, and better ways of working together. The impact speaks for itself: 100% found the sessions relevant 100% felt more connected as a team Scaling teams successfully starts with investing in the people and collaboration behind the work.