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CHALLENGES IN CONSTRUCTION OF EFFECTIVE Prior Data Candes As large language models (LLMS) scale in size and capacity, the choice of prior data remains a critical determinant for downstream performance. Most LLMs are trained on large data sets for web scale such as regular review, which provides broad coverage but lacks explicit domain labels. This … Read more

Despite rapid advances in the vision language modeling, much of the progress of this field is shaped by models trained on proprietary data sets and often depend on distillation from closed source systems. This addiction creates barriers to scientific transparency and reproducibility, especially for tasks involving fine -grained image and video understanding. Benchmark performance may … Read more

Effective reasoning is crucial to solving complex problems in fields such as math and programming, and LLMs have shown significant improvements through long -term reasoning. However, transformer -based models face restrictions due to their square calculation complexity and linear memory requirements, making it challenging to treat long sequences effectively. While techniques such as Chain of … Read more