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

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Microsoft’s new rStar-Math technique upgrades small models to outperform OpenAI’s o1-preview...

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Diffbot’s AI doesn’t guess

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Microsoft releases powerful Phi-4 model on Hugging Face as a fully...

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BYD accelerates large model development, former Chief technology expert from 01.AI...

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HeyGen Integrates Sora for Advanced AI Avatar Technology Launch

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XPeng Aeroht’s Modular Flying Car Makes Its Debut Overseas

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Quantum? No solace: Nvidia CEO sinks QC stocks with ’20 years...

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Nvidia brings GenAI into the physical world with Cosmos.

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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...