DeepSeek researchers have developed a radical new approach to AI memory that converts text into visual tokens. This innovation could dramatically reduce computational requirements while improving long-term context retention in language models.
DeepSeek’s latest research paper introduces a radical approach to optical character recognition, framing it as optical compression rather than traditional text processing. The methodology questions whether pixels might be superior inputs for large language models compared to conventional text tokens, potentially addressing scalability limitations in current AI systems.
DeepSeek has released groundbreaking research that fundamentally reimagines optical character recognition as optical compression, according to technical analysts reviewing the new paper. The approach represents text visually rather than processing individual tokens, potentially addressing significant scalability limitations in current large language models.
