FLock said three papers from its research team have been accepted to ECML PKDD 2026, a major international conference in machine learning, data mining, and knowledge discovery in Europe. The company said the papers focus on recurring problems in complex reasoning by large models, including shortcut-taking, reasoning errors, and drift away from the original objective during long multi-step chains of thought. According to FLock, one of the studies helps models stay closer to their original line of reasoning during extended multi-step inference. It reduced reasoning drift by 63% and improved average accuracy by 7.4 percentage points across models of different sizes. The other two papers target reasoning on unfamiliar problems and the efficiency of partial correction after a model detects an error in its reasoning process. FLock Research said it continues to conduct foundational work around large-model training, inference, and reliability, with the stated goal of improving accuracy, stability, and generalization on complex tasks so models can move from answering questions to reliably solving more complex problems.