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

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Character.AI will no longer allow its chatbots to romance teenagers

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Character.AI takes teen safety seriously after bots are alleged to have...

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Stable Diffusion 3.5 is now available on Amazon Bedrock. What does...

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Hugging Face shows that test-time scaling can help small language models...

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Hugging Face’s SmolVLM can reduce AI costs by a large margin...

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The excellent isometric RPG Underrail is back

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A new robotic surgery procedure was tested at the University of...

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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...