😱 A #neurosymbolic# paper as a spotlight at #NeurIPS#!?! 😱 We must have done something right to please the #ML# gods, @GdiGiotto @lohithkonathala! 🙏 Stay tuned to find out how we get scalable and guaranteed constraint satisfaction 👀

🚀🚀 Introducing MAGENTA 🚀🚀 MAGENTA is the first agentic pipeline that brings natural-language reasoning and formal verification into a feedback loop, achieving perfect scores on AIME 2025, AIME 2026, and HMMT (February 2026) with various reasoning models. 🔥 MAGENTA achieved 100% accuracy across 93 problems from AIME 2025, AIME 2026, and HMMT February 2026 using K2-Horizon models of different sizes. 🔥 With the compact K2-Horizon-7B, MAGENTA solved all six IMO 2026 problems. The solutions were assessed using automated grading with reference answers and were separately reviewed by IMO medalists. 🔥 To the best of our knowledge, K2-Horizon-7B paired with MAGENTA is the smallest reported reasoner to solve all six IMO 2026 problems. This result suggests that verification and iterative refinement can allow compact models to achieve performance normally associated with much larger systems.

MAGENTA, from our researchers, keeps failures inside the solving loop. Failures are how science moves forward, and that’s aligned with our mission: AI that contributes to science itself. The loop works. With MAGENTA, K2-Horizon-7B solves all six IMO 2026 problems, the smallest model reported to do so. Try K2-Horizon-7B:

🚀🚀 Introducing MAGENTA 🚀🚀 MAGENTA is the first agentic pipeline that brings natural-language reasoning and formal verification into a feedback loop, achieving perfect scores on AIME 2025, AIME 2026, and HMMT (February 2026) with various reasoning models. 🔥 MAGENTA achieved 100% accuracy across 93 problems from AIME 2025, AIME 2026, and HMMT February 2026 using K2-Horizon models of different sizes. 🔥 With the compact K2-Horizon-7B, MAGENTA solved all six IMO 2026 problems. The solutions were assessed using automated grading with reference answers and were separately reviewed by IMO medalists. 🔥 To the best of our knowledge, K2-Horizon-7B paired with MAGENTA is the smallest reported reasoner to solve all six IMO 2026 problems. This result suggests that verification and iterative refinement can allow compact models to achieve performance normally associated with much larger systems.

I am looking for 1 postdoc and 2 fully funded PhDs at Imperial @ICComputing to join my @ERC_Research project AToM ⚛ We aim to build models that learn to compress what they perceive and remember, for permanent memory, longer horizons, and >10x speedups

This feels like a major moment for open-source AI. K2 Horizon spans a huge range of model sizes while still delivering strong performance. The 0.9B, 3.7B, and 7B models reaching the top of their classes is seriously impressive. What really makes the launch stand out is how much of the stack is being opened up. Open models are great, but open code, data, and recipes take it to another level.

K2 Horizon 375B A23B, a new open weights model from UAE's MBZUAI, scores 47 on the Artificial Analysis Intelligence Index, with relatively strong agentic performance and a 30 point jump over its predecessor K2 Horizon 375B A23B is an open weights Mixture-of-Experts model with 375B total and 23B active parameters from @IFM_MBZUAI, MBZUAI's Institute of Foundation Models. It scores 47 on the Intelligence Index, alongside models such as MiniMax-M3 (45, also a MoE with 23B active parameters), and a large upgrade from its predecessor K2 Think V2 (17, 70B dense model). It leads nearby open weights models on agentic evals and has a low hallucination rate, but trails on knowledge and the hardest reasoning evals. K2 Think V2 ranks among the most open models on our Openness Index; MBZUAI is updating the supporting documentation and code for K2 Horizon and we expect to add it to the Openness Index soon. Key takeaways: ➤ Strong on agentic tasks, weaker on knowledge and deep reasoning. MiniMax-M3, a recent model that is close to it on the Intelligence Index, makes the cleanest comparison: K2 Horizon 375B A23B leads on GDPval-AA, our real-world knowledge work benchmark (Elo 1430 vs 1380), and on τ³-Banking (34.2% vs 15.3%), but trails on GPQA Diamond (87.3% vs 92.9%) and Humanity's Last Exam (32.0% vs 39.0%) ➤ Low hallucination rate, driven by abstention rather than knowledge. K2 Horizon 375B A23B attempts only 40% of AA-Omniscience questions, declining the remaining 60% rather than guessing. The result is a 26% hallucination rate, among the lower rates we have measured, while accuracy is 18%, essentially unchanged from K2 Think V2 ➤ A new architecture over its predecessor. K2 Horizon 375B A23B is a 375B parameter Mixture-of-Experts model with 23B active, succeeding the 70B dense K2 Think V2, and extends context from 262K to 512K tokens. Its 23B active parameters match MiniMax-M3 (428B total, 23B active) Key model details: ➤ Architecture: Mixture-of-Experts, 375B total parameters, 23B active ➤ Context window: 512K tokens ➤ Multimodality: Text input and output only ➤ Pricing and availability: Yet to be announced ➤ Licensing: Open weights (license details to be announced)

Introducing K2 Horizon: a connected fleet of six foundation models ranging from 0.9 billion to 375 billion parameters. - Frontier performance: Across coding and agentic tasks, K2 Horizon delivers top-tier performance in every size class—with the 0.9B, 3.7B and 7B models setting new state of the art at their respective scales. - Radical openness: K2 Horizon represents the largest fully open-source model launch in AI history. The fully open code, training data and recipes are a significant step forward in transparency. Launch page: Tech blog: Hugging Face:

CoT monitoring assumes reasoning traces record what shapes an answer. Yet, we found that models are less likely to verbalize a cue in its CoT when the cue comes from a tool return instead of the user message! 👀 🧵