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
arXiv:2606. 00991v1 Announce Type: new Abstract: Transportation systems management and operations (TSMO) increasingly depends on timely interpretation of heterogeneous data, from various sensor streams, incident reports, traveler feedback, and visual observations.
By Siyan Li, Zehao Wang, Jiachen Li, Kanok Boriboonsomsin, Matthew J. Barth, Guoyuan Wu
arXiv:2607. 11889v1 Announce Type: cross Abstract: Large language models trained on unrestricted internet corpora inevitably embed information from the future, introducing lookahead bias that compromises the validity of backtests and causal inference in finance and the social sciences.
By Bryan Kelly, Semyon Malamud, Johannes Schwab, Teng Andrea Xu
The paper introduces LM-DSB, a lightweight multi‑agent system that automates the creation of daily safety bulletins (DSBs) for construction sites using a retrieval‑augmented generation approach with a locally deployable large language model. The system employs specialized planner, researcher, and writer agents coordinated in a hierarchical architecture, fine‑tuned on construction safety documents and grounded in 37 HSE documents. Validation in two phases showed that the multi‑agent RAG system outperformed alternatives in lexical alignment, coherence, and document completeness, and expert reviewers rated it highly for regulatory compliance and factual accuracy.
By Xianxiang Zhao, Advik Mehta, Falak Sethi, Brian Gue, Qipei Mei, Lingzi Wu
Construction safety management still struggles to produce daily safety bulletins (DSBs), structured daily communications that identify site-specific hazards, convey safety protocols, and set preventiv...
The paper presents InstruBPM, an instruction‑tuned open‑weight large language model that generates BPMN process models from natural language descriptions. By preparing paired text‑diagram data and applying parameter‑efficient fine‑tuning with quantization, the authors demonstrate that the tuned model outperforms both untuned open‑weight baselines and strong proprietary models in sequence, structural, and guideline‑conformance metrics. The approach is cost‑effective, privacy‑preserving, and supports on‑prem deployment, with publicly released models and scripts for reproducibility.
By G\"okberk \c{C}elikmasat, Atay \"Ozg\"ovde, Fatma Ba\c{s}ak Aydemir
The paper reviews data strategies for large language models, emphasizing that data strategy—selecting, weighting, and scheduling training data—determines model capabilities within a fixed data budget. It treats high‑quality data as having training‑dependent utility, varying with the target objective, model, and training state. The authors organize existing methods by decision granularity, model dependence, and temporal adaptivity, comparing their computational costs and empirical support, and note limited evidence for transfer across model scales and end‑to‑end cost parity.
By Yuanjian Xu, Jianing Hao, Tianze Sun, Yongyao Duan, Sam Chen, Jinyi Han, Wanbo Zhang, Yuxuan Sun, Guang Zhang, Zhong Li
arXiv:2610.17305v1 Announce Type: new
Abstract: Large Language Models (LLMs) have recently improved Text-to-SQL generation, but their computational cost can hinder deployment in resource-constrained...
By Bilel Abderrahmane Benziane, Xavier Schepler
The study evaluates local open‑weight large language model agents for data‑engineering tasks, using a benchmark of fifteen mobility‑workflow tasks that cover discovery, processing, enrichment, and reporting. Ten local model configurations were tested in one‑shot and closed‑loop conditions, producing 1,500 scored attempts on a consumer‑grade GPU. Results show that models larger than two billion parameters achieve up to 85.3% artifact‑level success, with closed‑loop workspaces boosting pass rates by 26.7–52.0 percentage points, and a quantized 9‑billion‑parameter model reaching 69.3% success with a 6.5 GB memory footprint.
By Jorge Garc\'ia-Carrasco, Javier Sanchis, Alejandro Reina-Reina, Alejandro Mat\'e, Juan Trujillo
The paper introduces FlockMTL, an extension for database management systems that deeply integrates large language models and retrieval‑augmented generation into DuckDB. It provides model‑driven scalar and aggregate functions, cost‑based optimizations like batching and caching, and new SQL DDL abstractions (PROMPT and MODEL) to treat LLMs as first‑class schema objects. These features aim to simplify the development of knowledge‑intensive analytical applications by reducing the effort required to orchestrate heterogeneous data systems and manage LLM context.
By Anas Dorbani, Sunny Yasser, Jimmy Lin, Amine Mhedhbi
arXiv:2606. 15396v1 Announce Type: cross Abstract: Malicious content generated from large language models (LLMs) could pose severe safety risks and ethical concerns.
By Wenbo Yu, Bohua Wang, Hao Fang, Kuofeng Gao, Jingru Zeng, Xiaochen Yang, Tianyi Zhang, Xiaoxiao Ma, Jiawei Kong, Hao Wu, Bin Chen, Shu-Tao Xia, Min Zhang
arXiv:2607. 14387v1 Announce Type: new Abstract: Validating autonomous driving systems requires diverse, regulation-compliant test scenarios.
By Yuan Gao, Wenting Miao, Mattia Piccinini, Haoyu Wang, Qunying Song, Johannes Betz