4 citations · 4 across the 10 of their papers we have counts for
6 papers · 1 filter
BDH-CQ: In-Context Learning with Recurrent Latent Reasoning
Björn Engdahl, Adrian Kosowski, Jan Chorowski +6
We introduce BDH-CQ, a reasoning model that combines in-context learning with recurrent latent reasoning. Inputs presented at inference time continuously update the model's recurre…
OmicsLM: A Multimodal Large Language Model for Multi-Sample Omics Reasoning
Maciej Sypetkowski, Joanna Krawczyk, Łukasz Smoli{ń}ski +8
Large language models can reason over biological knowledge, but they are not naturally equipped to work with quantitative omics measurements. Existing approaches either convert exp…
BioResearcher: Scenario-Guided Multi-Agent for Translational Medicine
Remigiusz Kinas, Joanna Krawczyk, Rafał Powalski +6
Translational medicine turns underspecified development goals into evidence synthesis that must combine literature, trials, patents, and quantitative multi-omics analysis while pre…
Advancing Polish Language Modeling through Tokenizer Optimization in the Bielik v3 7B and 11B Series
Krzysztof Ociepa, Łukasz Flis, Remigiusz Kinas +2
The development of the Bielik v3 PL series, encompassing both the 7B and 11B parameter variants, represents a significant milestone in the field of language-specific large language…
Bielik-Minitron-7B: Compressing Large Language Models via Structured Pruning and Knowledge Distillation for the Polish Language
Remigiusz Kinas, Paweł Kiszczak, Sergio P. Perez +4
This report details the creation of Bielik-Minitron-7B, a compressed 7.35B parameter version of the Bielik-11B-v3.0 model, specifically optimized for European languages. By leverag…
Making Bielik LLM Reason (Better): A Field Report
Adam Trybus, Bartosz Bartnicki, Remigiusz Kinas
This paper presents a research program dedicated to evaluating and advancing the reasoning capabilities of Bielik, a Polish large language model. The study describes a number of st…