{"version":1,"type":"story","url":"https://digestai.news/story/study-finds-six-ai-models-act-like-two-due-to-correlated-errors","json":"https://digestai.news/story/study-finds-six-ai-models-act-like-two-due-to-correlated-errors.json","markdown":"https://digestai.news/story/study-finds-six-ai-models-act-like-two-due-to-correlated-errors.md","slug":"study-finds-six-ai-models-act-like-two-due-to-correlated-errors","headline":"Study finds six AI models act like two due to correlated errors","summary":"A new analysis published on Towards Data Science argues that ensembling multiple large language models yields fewer independent opinions than the number of models suggests. The author tested this hypothesis using six open-source models and 1,500 exam questions, finding that the models' mistakes are highly correlated. This means that when one model gets an answer wrong, the others are likely to make the same error, reducing the diversity of the collective output.\n\nThe article introduces a specific mathematical formula to calculate the effective number of independent opinions within a group of models. By applying this formula to the test data, the study demonstrates that six distinct models can function statistically like only two independent entities. This challenges the common assumption that simply adding more models to a voting or consensus mechanism will linearly improve accuracy or robustness.\n\nThe findings suggest that developers and researchers relying on multi-model ensembles for reliability may be overestimating the benefits of diversity. The core issue is not the quantity of models, but the similarity of their training data and architectures, which leads to systematic failures rather than random ones. This insight is crucial for anyone designing AI systems that depend on consensus mechanisms for critical tasks.","keyPoints":["Six open models tested on 1,500 questions showed high error correlation.","A formula calculates effective independent opinions, revealing six models act like two.","Correlated mistakes reduce the reliability benefits of multi-model ensembling."],"whyItMatters":"This research highlights a critical flaw in using model ensembles for reliability. If models fail in similar ways, voting mechanisms provide false confidence, impacting the safety and accuracy of AI systems deployed in high-stakes environments.","category":{"slug":"research","name":"Research","url":"https://digestai.news/category/research"},"entities":{"companies":[],"models":[],"people":[]},"firstPublishedAt":"2026-10-23T11:00:00Z","updatedAt":"2026-10-23T11:00:00Z","sourceCount":1,"hasPrimarySource":false,"sources":[{"outlet":"Towards Data Science","title":"Your Six AI Models Are Really Two: The Math of Correlated Mistakes","url":"https://towardsdatascience.com/your-six-ai-models-are-really-two-the-math-of-correlated-mistakes","publishedAt":"2026-10-23T11:00:00Z","type":"newsletter","primary":false,"lead":true}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"Digest AI, \"Study finds six AI models act like two due to correlated errors\", 23 October 2026, https://digestai.news/story/study-finds-six-ai-models-act-like-two-due-to-correlated-errors","publisher":"Digest AI","title":"Study finds six AI models act like two due to correlated errors","datePublished":"2026-10-23T11:00:00Z","url":"https://digestai.news/story/study-finds-six-ai-models-act-like-two-due-to-correlated-errors"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}