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.
FLock said its research team has had three papers accepted to ECML PKDD 2026.
ECML PKDD is one of the key international academic conferences in Europe covering machine learning, data mining, and knowledge discovery. FLock said the three papers center on common issues that appear when large models handle complex reasoning tasks, including shortcut-taking, errors during reasoning, and gradual drift from the original objective when reasoning chains become too long.
One study cut reasoning drift by 63%
Among the three, one study is designed to help models preserve their original line of thought during long, multi-step reasoning. According to FLock, it reduced drift in the reasoning process by 63% and improved average accuracy by 7.4 percentage points across models of different sizes.
Other papers focus on unfamiliar problems and local error repair
The other two studies improved reasoning on unfamiliar problems and the efficiency of making local corrections after errors are found in the reasoning process, FLock said.
FLock Research said it continues to pursue foundational research on large-model training, inference, and reliability, aiming to improve accuracy, stability, and generalization in complex tasks and push models beyond answering questions toward reliably solving complex problems.
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