Deep learning architectures like CNNs and Transformers have significantly advanced biological sequence modeling by capturing local and long-range dependencies. However, their application in biological ...

Artificial intelligence (AI) has made significant strides in recent years, yet challenges persist in achieving efficient, cost-effective, and high-performance models. Developing large language models ...

Research and development (R&D) is crucial in driving productivity, particularly in the AI era. However, conventional automation methods in R&D often lack the intelligence to handle complex research ...

LLMs are advancing rapidly across multiple domains, yet their effectiveness in tackling complex financial problems remains an area of active investigation. The iterative development of LLMs has ...

Language models (LMs) face a fundamental challenge in how to perceive textual data through tokenization. Current subword tokenizers segment text into vocabulary tokens that cannot bridge whitespace, ...

Artificial intelligence has made significant strides in recent years, yet integrating real-time speech interaction with visual content remains a complex challenge. Traditional systems often rely on ...

Reinforcement Learning from Verifiable Rewards (RLVR) has recently emerged as a promising method for enhancing reasoning abilities in language models without direct supervision. This approach has ...

Large language models (LLMs) are rapidly transforming into autonomous agents capable of performing complex tasks that require reasoning, decision-making, and adaptability. These agents are ...