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This paper presents a context-aware neural G2P method that scores paths of a discriminative conditional random field over a word lattice constructed from dictionaries that strongly outperforms conventional morphological analyzer-based methods and neural sequence models.
The DeepSeek-V4.1-Flash model, a multimodal Mixture-of-Experts model with 552B backbone parameters and support for contexts of up to one million tokens, is introduced, substantially improving cost efficiency for agentic workloads and pushing the limits of KV cache compression.
VISA's self-evolving framework not only enhances multimodal instruction synthesis but also adapts in real-time to improve training data quality and model performance.
