3 papers
cs.AI2026
Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches
Teddy Ferdinan, BartÅomiej Koptyra, MikoÅaj Langner +42
While Reasoning Language Models (RLMs) are rapidly emerging as powerful tools for scientific research, their impact is primarily concentrated in "hard science" fields. The slow --…
cs.CL2026
Chunking Methods on Retrieval-Augmented Generation - Effectiveness Evaluation Against Computational Cost and Limitations
Mateusz Åmigielski, MichaÅ Rajkowski, Mateusz Zbrocki +5
Retrieval-Augmented Generation (RAG) has demonstrated significant capabilities in enhancing the performance of Large Language Models (LLMs). One of the key tasks in RAG systems is…
cs.AI2026
What properties of reasoning supervision are associated with improved downstream model quality?
MikoÅaj Langner, Dzmitry Pihulski, Jan Eliasz +5
Validating training data for reasoning models typically requires expensive trial-and-error fine-tuning cycles. In this work, we investigate whether the utility of a reasoning datas…