DeepSeek’s cost-saving AI may lead to inaccuracy in model performance: research

New research has exposed a potential flaw in the memory-saving technology underpinning DeepSeek’s models, marking a rare challenge to a breakthrough that has helped the Chinese artificial intelligence lab close the gap with US rivals OpenAI and Anthropic with its low-cost approach.

The findings were published late last month in a paper led by researchers from ByteDance and co-authors affiliated with Princeton University, Stanford University and the University of California, Berkeley.

The study, which has not been peer-reviewed, said that DeepSeek’s efforts to reduce the memory needed to process lengthy inputs may come at the expense of reliability, as the models were more likely to miss the same information when it appeared in certain positions in a document.

“The phenomenon appears in DeepSeek-V4, V4.1, and all compressed models we pretrained,” the paper said.