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AI tool uses face photos to estimate biological age and predict...

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OpenAI is having a rough week–it could be the start of...

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Sam Altman’s Sister is suing OpenAI CEO for sexual abuse

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Emotionwave – Unveiling XR & Holographic Virtual Human Concert line-up at...

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HONOR Magic7 Lite

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Asus is developing the ROG Flow Z13 to make more sense...

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Nvidia CEO: PC gaming will never be rendered entirely by AI

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Nvidia’s AI Snake is feeding itself. Announces GeForce GTX 5090 GPU....

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Nvidia unveils $3,000 desktop AI computer for home researchers

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Is Automated Hallucination Detection in LLMs Feasible? A Theoretical and Empirical...

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This AI Paper Introduce WebThinker: A Deep Research Agent that Empowers...

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A Step-by-Step Guide to Implement Intelligent Request Routing with Claude

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Researchers from Fudan University Introduce Lorsa: A Sparse Attention Mechanism That...

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Is Automated Hallucination Detection in LLMs Feasible? A Theoretical and Empirical...

Recent advancements in LLMs have significantly improved natural language understanding, reasoning, and generation. These models now excel at diverse tasks like mathematical problem-solving and generating contextually appropriate text. However, a persistent challenge remains: LLMs often generate hallucinations—fluent but factually incorrect responses. These hallucinations undermine the reliability of LLMs, especially...