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CORE-Bench: A Benchmark Consisting of 270 Tasks based on 90 Scientific Papers Across Computer Science, Social Science, and Medicine with Python or R Codebases
Computational reproducibility poses a significant challenge in scientific research across various fields, including psychology, economics, medicine, and computer science. Despite the fundamental importance of reproducing results using provided data and code, recent studies have exposed severe shortcomings in this area. Researchers face numerous obstacles when replicating studies, even when code and data are available. These…
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New Evidence Shows Heat Destroys Quantum Entanglement
While devising a new quantum algorithm, four researchers accidentally established a hard limit on the “spooky” phenomenon.
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RAG, AI Agents, and Agentic RAG: An In-Depth Review and Comparative Analysis of Intelligent AI Systems
Artificial intelligence (AI) has given rise to powerful models capable of performing diverse tasks. Two of the most impactful advancements in this space are Retrieval-Augmented Generation (RAG) and Agents, which play distinct roles in improving AI-driven applications. However, the emerging concept of Agentic RAG presents a hybrid model that utilizes the strengths of both systems.…
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Chain-of-Thought (CoT) Prompting: A Comprehensive Analysis Reveals Limited Effectiveness Beyond Math and Symbolic Reasoning
Chain-of-thought (CoT) prompting has emerged as a popular technique to enhance large language models’ (LLMs) problem-solving abilities by generating intermediate steps. Despite its better performance in mathematical reasoning, CoT’s effectiveness in other domains remains questionable. Current research is focused more on mathematical problems, possibly overlooking how CoT could be applied more broadly. In some areas,…
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Gated Slot Attention: Advancing Linear Attention Models for Efficient and Effective Language Processing
Transformer models have revolutionized sequence modeling tasks, but their standard attention mechanism faces significant challenges when dealing with long sequences. The quadratic complexity of softmax-based standard attention hinders the efficient processing of extensive data in fields like video understanding and biological sequence modeling. While this isn’t a major concern for language modeling during training, it…
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ByteDance Researchers Release InfiMM-WebMath-40: An Open Multimodal Dataset Designed for Complex Mathematical Reasoning
Artificial intelligence has significantly enhanced complex reasoning tasks, particularly in specialized domains such as mathematics. Large Language Models (LLMs) have gained attention for their ability to process large datasets and solve intricate problems. The mathematical reasoning capabilities of these models have vastly improved over the years. This progress has been driven by advancements in training…
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Google AI Researchers Introduce a New Whale Bioacoustics Model that can Identify Eight Distinct Species, Including Multiple Calls for Two of Those Species
Whale species produce a wide range of vocalizations, from very low to very high frequencies, which vary by species and location, making it difficult to develop models that automatically classify multiple whale species. By analyzing whale vocalizations, researchers can estimate population sizes, track changes over time, and help develop conservation strategies, including protected area designation…
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Some Mad Genius Put ChatGPT on a TI-84 Graphing Calculator
Cheaters of the world, rejoice. Although you’re going to need some serious hardware skills.
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Advancing Membrane Science: The Role of Machine Learning in Optimization and Innovation
Machine Learning in Membrane Science:ML significantly transforms natural sciences, particularly cheminformatics and materials science, including membrane technology. This review focuses on current ML applications in membrane science, offering insights from both ML and membrane perspectives. It begins by explaining foundational ML algorithms and design principles, then a detailed examination of traditional and deep learning approaches…
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Microsoft Releases GRIN MoE: A Gradient-Informed Mixture of Experts MoE Model for Efficient and Scalable Deep Learning
Artificial intelligence (AI) research has increasingly focused on enhancing the efficiency & scalability of deep learning models. These models have revolutionized natural language processing, computer vision, and data analytics but have significant computational challenges. Specifically, as models grow larger, they require vast computational resources to process immense datasets. Techniques such as backpropagation are essential for…