Kolibri is an English‑German mixture‑of‑experts transformer with 78.1 B total parameters and 3.46 B active parameters per token, released under the Apache 2.0 licence. It was trained on 24 T tokens, with German comprising over 20 % of the data, and includes supervised fine‑tuning and reinforcement learning on more than 1.2 M curated tasks. The model activates only 4.4 % of its parameters per token and, among open models evaluated, sits on the Pareto frontier of quality and serving cost for both English and German.
By Aleph Alpha, :, Moritz Althaus, Artur Baranowski, Michael Barlow, Fabien C. Y. Benureau, Felix Berkenkamp, Amrita Bhattacherjee, Lukas Bl\"ubaum, Duncan Blythe, Augustijn de Boer, Bastian Boll, Thomas F. Burns, Bj\"orn Deiseroth, Niko D\"urr, B\'eni Bal\'azs Egressy, Niklas Finken, Niklas Freymuth, Fabian Gebhart, Max Hammer, Bastian Harren, Andreas Hartel, Johann Higl, Steffen Hirschmann, Pablo Iyu Guerrero, Yasser Jadidi, Andreas Jagusch, Mitja Kleider, Markus Klein, Jonas Knupp, Murali Kondragunta, Garwin Lechner, Dominik L\^e-Kellner, Jurek Leonhardt, Jiaze Li, Sohir Maskey, Omar Mehio, Pit Neitemeier, Letitia Parcalabescu, Alejandro Petry Pacheco, Pranav Ragupathy, Martin Reinhardt, Peter Rodenkirch Nfissi, Dylan Rodriquez, Leonard Salewski, Raziye Sari, Jordan Sassoon, Ivo Schaper, Ilhan Scheer, Frank Schneider, Philipp Scholl, Lukas Schott, Eric Schreiber, Pablo Schumacher, Alessio Serra, Philipp Siedler, Prabhu Teja Sivaprasad, Mohamed Soliman, Till Speicher, Volker Stampa, Simon Thel, Serge Thilges, Irina Vidal Migall\'on, Denis Wa{\ss}mann, Samuel Weinbach, Sascha Wirges, Michael W\"uthrich, Alexei Zacharov
The paper evaluates System One decision models—typed models that output probabilities for branching decisions—against supervised classifiers and generative language models on automated decision gate tasks. Eight checkpoints from six families, including the hosted model Jev, were benchmarked on workflow, intent, and social‑science items, showing that small trained classifiers match or slightly outperform decision models on intent and workflow when labels are available, while decision models outperform zero‑shot classifiers when labels are absent. The study also explores calibration, risk thresholds, and cost‑efficiency trade‑offs, providing condition‑dependent design guidelines for automated decision gates.
By Amir Rafe, Subasish Das
arXiv:2610.19331v1 Announce Type: new
Abstract: This paper presents LLM-jp-4, a family of Japanese sovereign large language models (LLMs) developed with an emphasis on transparency, reproducibility,...
By LLM-jp, :, Chaoran Liu, Daisuke Kawahara, Hideyuki Tachibana, Hirokazu Kiyomaru, Jun Suzuki, Koshi Eguchi, Kouta Nakayama, Qianying Liu, Rio Yokota, Ryo Takahashi, Sadao Kurohashi, Sosuke Hosokawa, Taishi Nakamura, Takashi Kodama, Yun-Ang Wu, Yusuke Miyao, Yusuke Oda
arXiv:2607. 09424v1 Announce Type: cross Abstract: We present Soofi S 30B-A3B, a sovereign, open-source Mixture-of-Experts (MoE) hybrid Mamba Transformer foundation model for German and English.
By The Soofi-Team, :, Benedikt Droste, David Fitzek, Ruben H\"arle, Lukas Helff, Maximilian Idahl, Alex Jude, Abbas Goher Khan, Maurice Kraus, Timm Ruland, Richard Rutmann, Sebastian Sztwiertnia, Markus Frey, Daniil Gurgurov, Jan Pfister, Tom R\"ohr, Sebastian von Rohrscheidt, J\"org Bienert, Nicolas Flores-Herr, Simon Gottschalk, Andreas Hotho, Kristian Kersting, Joachim K\"ohler, Alexander L\"oser, Wolfgang Nejdl, Simon Ostermann, Jan Plogsties, Patrick Putzky, Mehdi Ali, Michael Fromm, Max L\"ubbering
arXiv:2610.17205v1 Announce Type: new
Abstract: LLM-based decision-making systems, also called System One models, have recently gained popularity, most notably through Jev (Almeida, 2026). Given a ty...
By Jonathan Geuter, Andrew Cheng, David Alvarez-Melis
arXiv:2606. 03650v1 Announce Type: cross Abstract: Choosing or ranking language models for a specific application is hardest when no task-specific labeled data exists, and standard public benchmarks cannot be trusted, their items having likely leaked into pretraining, so scores reflect memorization rather than fitness.
By Alexander Apartsin, Yehudit Aperstein