Turing

2026 is not just another year for AI: it’s the turning point where experimentation becomes execution. Are you ready to rethink AI governance, model strategy, and human-AI collaboration? Explore the key trends shaping the future https://bit.ly/49oGTII Which of these shifts do you think will define competitive advantage over the next few years? Share your thoughts

Here’s a new case study on how automation is transforming financial modeling workflows. By automating MMR model creation, our team helped cut build time from 5 days to just 3 hours and reduce manual effort to only ~20% unlocking real efficiency for finance teams. Read how we made it happen https://bit.ly/3YuVygp

Newsletter: AGI Advance Weekly AI and AGI Insights We released a new symbolic reasoning testbed designed to surface failure modes that standard benchmarks miss. What we shipped 1,000+ HLE-grade math prompts built to break state-of-the-art LLMs. Coverage across 10+ domains, including algebra, discrete math, topology, and analysis. 100% dual-layer QA for correctness, novelty, and formatting. Every prompt broke at least two internal models. 50%+ also broke external models, including Nova, R1, Sonnet, and Qwen. Why it matters Symbolic reasoning remains a core bottleneck. This testbed exposes where reasoning actually fails and provides a reusable foundation for training evaluators and reward models that understand real math. Subscribe to AGI Advance Weekly to stay ahead. https://bit.ly/4pzgjCJ

AI models don’t fail in production because they’re weak. They fail because they aren’t engineered into real systems. This piece breaks down what an AI Engineer actually does today, from GenAI apps and retrieval pipelines to guardrails, evaluation, and deployment. If you’re building AI beyond demos, this role matters more than you think. Read the full article here https://bit.ly/3YnGuRP

This week, we’re highlighting Code Review Bench, a 6,296-task benchmark built to evaluate LLMs on code review, not just code generation. Most agents look strong when fixes are verified by unit tests. Real engineering is messier. Code review tests deeper signals: bug severity, design critique, contextual judgment, and productivity tradeoffs. What we built: Grounded in real PRs: Tasks drawn from real GitHub workflows, labeled APPROVE or REQUEST_CHANGES, with reviewer-intent hints to reduce ambiguity. Open + commercial split: A 1,200-task subset is open-sourced on Hugging Face; the full 6,296-task dataset is available for licensing. Frontier model evaluations: Claude Sonnet 4.5 leads overall success (50.8%), while GPT-5 Codex excels at bug catching (89.15%), highlighting distinct agent strengths. Code Review Bench helps answer a harder question: how well can models reason through ambiguity, critique tradeoffs, and actually raise code quality? Explore the benchmark https://bit.ly/4jKi3bb

Frontier models don’t improve on easy problems. They improve when the tasks break them. In this case study, we delivered model-breaking coding tasks across multiple programming languages to help a leading AI lab expose real failure modes, strengthen reasoning, and push performance beyond benchmarks. This is what post-training looks like when rigor matters. Read the case study →https://bit.ly/49z3Biu

Happy New Year from Turing! Grateful for the researchers, builders, and partners pushing the frontier forward with us. Onward to another year of meaningful progress in AI.

2025 marked a turning point for enterprise AI. The focus shifted from experimentation to intelligence built on proprietary data, clear rules, and real governance, driving production workflows and measurable impact. Explore the trends that defined the year and what’s next for AI in business. https://bit.ly/4qiHGlT

You voted, and the winner is clear. Multimodal reasoning took the top spot with 35% of the vote. From combining vision, text, and structured data to reasoning across real-world workflows, this is where frontier AI is being stress-tested today. We’re unpacking the use case next, with concrete insights, tradeoffs, and takeaways, read more here https://lnkd.in/eyS9twgS

As the year comes to a close, we’re grateful for the builders, researchers, customers, and partners who make this work possible. Wishing you a restful holiday season and a strong start to the year ahead. We’re excited for what we’ll build together next. Happy holidays from the Turing team.