US EditionAnthropic researchers discover the weird AI problem: Why thinking longer makes models dumber
10 Articles
10 Articles
Longer Thinking, Lower Accuracy: Research Flags Limits of Extended AI Reasoning
New research from Anthropic challenges the long-standing idea that more computational time always benefits AI performance. Instead, their findings show that when language models are given longer reasoning budgets during inference, they may become less accurate, especially in tasks requiring logical consistency or noise resistance. The study evaluated models from Anthropic, OpenAI, and several open-source developers. Researchers found consistent …
Anthropic researchers discover the weird AI problem: Why thinking longer makes models dumber
Anthropic research reveals AI models perform worse with extended reasoning time, challenging industry assumptions about test-time compute scaling in enterprise deployments.
AI models adopt hidden behaviors from seemingly harmless data – even without recognizable clues. Researchers warn: This could be a basic principle of neural networks. The article Anthropic warns: AI systems learn unintentionally problematic behavior patterns first appeared on THE-DECODER.de.
A new Anthropic study shows a surprising phenomenon: AI models become worse in longer thinking processes rather than better. The so-called "Inverse Scaling" affects leading models such as Claude and ChatGPT and this has consequences. (Read more)
A new Anthropic study shows that longer thinking does not make large language models smarter – but more prone to errors. For companies using AI, this finding could have far-reaching consequences. read more on t3n.de
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