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Real-time speech translation poses a complex challenge that requires trouble-free integration of speech recognition, machine translation and text-to-speech synthesis. Traditional cascaded approaches often introduce composite errors, fail to preserve the speaker identity and suffer from slow treatment, making them less suitable for real -time applications such as live interpretation. In addition, existing contemporary translation models … Read more

The integration of visual and textual data into artificial intelligence presents a complex challenge. Traditional models often struggle to interpret structured visual documents such as tables, charts, infographics and diagrams with precision. This limitation affects automated content extraction and understanding, which is crucial to applications in data analysis, obtaining information and decision making. As organizations … Read more

Large language models (LLMs) such as GPT, Gemini and Claude use huge training data sets and complex architectures to generate high quality answers. However, optimization of their inference time calculation remains challenging as rising model size leads to higher calculation costs. Researchers continue to explore strategies that maximize efficiency while maintaining or improving model performance. … Read more