🔁 𝐖𝐡𝐚𝐭 𝐢𝐟 𝐥𝐨𝐨𝐩𝐞𝐝 𝐋𝐌𝐬 𝐜𝐨𝐮𝐥𝐝 𝐮𝐬𝐞 𝟑× 𝐟𝐞𝐰𝐞𝐫 𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬 𝐚𝐧𝐝 𝐚 𝟑× 𝐬𝐦𝐚𝐥𝐥𝐞𝐫 𝐊𝐕 𝐜𝐚𝐜𝐡𝐞 𝐚𝐧𝐝 𝐬𝐭𝐢𝐥𝐥 𝐛𝐞 𝐜𝐨𝐦𝐩𝐚𝐫𝐚𝐛𝐥𝐞 𝐭𝐨 𝐚 𝐬𝐭𝐚𝐧𝐝𝐚𝐫𝐝 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫? Introducing 𝐑𝐞𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐚𝐭 𝐅𝐢𝐱𝐞𝐝 𝐏𝐨𝐢𝐧𝐭𝐬. We argue that looped LMs don't have to pay for every loop. They can scale by adding FLOPs at constant memory. The key is the fixed point of the loop. We introduce: – 𝐅𝐢𝐱𝐞𝐝-𝐏𝐨𝐢𝐧𝐭 𝐒𝐡𝐨𝐫𝐭𝐜𝐮𝐭𝐬: what fixed points buy a looped model in training (pre-training, post-training) and inference (prefill, decoding) – A 𝐥𝐞𝐚𝐫𝐧𝐞𝐝 𝐝𝐞𝐩𝐭𝐡 𝐩𝐫𝐢𝐨𝐫 and 𝐨𝐫𝐭𝐡𝐨𝐠𝐨𝐧𝐚𝐥 𝐢𝐧𝐣𝐞𝐜𝐭𝐢𝐨𝐧: two ways to shape those fixed points 🧵 Long thread ahead (0/n).

Most decisions don't need words. 🤫⚡ Meet K2-Type-0.9B 🧠: an open decision model. One state + many typed questions in, calibrated probabilities out, in a single forward pass. No text generated. Apache-2.0 weights 👇 #OpenSource# #LLM# #AI#

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)