<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[RSS Feed]]></title><description><![CDATA[RSS Feed]]></description><link>http://direct.ecency.com</link><image><url>http://direct.ecency.com/logo512.png</url><title>RSS Feed</title><link>http://direct.ecency.com</link></image><generator>RSS for Node</generator><lastBuildDate>Mon, 27 Apr 2026 14:37:10 GMT</lastBuildDate><atom:link href="http://direct.ecency.com/@abdelzaher1/rss" rel="self" type="application/rss+xml"/><item><title><![CDATA[Nvidia AI Introduces the Normalized Transformer (nGPT): A Hypersphere-based Transformer Achieving 4-20x Faster Training and Improved Stability for LLMs]]></title><description><![CDATA[Researchers from NVIDIA propose a novel architecture called the Normalized Transformer (nGPT), which incorporates representation learning on the hypersphere. In this approach, all vectors involved in the]]></description><link>http://direct.ecency.com/ai/@abdelzaher1/nvidia-ai-introduces-the-normalized-transformer-ngpt-a-hypersphere-based-transformer-achieving-4-20x-faster-training-and</link><guid isPermaLink="true">http://direct.ecency.com/ai/@abdelzaher1/nvidia-ai-introduces-the-normalized-transformer-ngpt-a-hypersphere-based-transformer-achieving-4-20x-faster-training-and</guid><category><![CDATA[ai]]></category><dc:creator><![CDATA[abdelzaher1]]></dc:creator><pubDate>Tue, 31 Dec 2024 21:20:33 GMT</pubDate><enclosure url="https://images.ecency.com/p/MG5aEqKFcQi7V5gYDHZmv4k3eyosSQtAxkUQXtKGRDzJufmXwXiohbu241dPPNBYvt6bdyJM7TVyp4HYEtQ7NPxGWGmWYYjTt?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Understanding Local Rank and Information Compression in Deep Neural Networks]]></title><description><![CDATA[The proposed framework is centered around the definition and analysis of local rank, which is defined as the expected rank of the Jacobian of the pre-activation function with respect to the input. This]]></description><link>http://direct.ecency.com/ai/@abdelzaher1/understanding-local-rank-and-information-compression-in-deep-neural-networks</link><guid isPermaLink="true">http://direct.ecency.com/ai/@abdelzaher1/understanding-local-rank-and-information-compression-in-deep-neural-networks</guid><category><![CDATA[ai]]></category><dc:creator><![CDATA[abdelzaher1]]></dc:creator><pubDate>Mon, 30 Dec 2024 14:37:00 GMT</pubDate><enclosure url="https://images.ecency.com/p/MG5aEqKFcQi7V5gYDHZmv4k3eyosSQtAxkUQXtKGRDzJufmXwXiohbu241dPPNBYvt6bdyJM7TVyp4HYEtQ7NPxGWGmWYYjTt?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Researchers at Stanford University Propose Locality Alignment: A New Post-Training Stage for Vision Transformers ViTs]]></title><description><![CDATA[Researchers from Stanford University propose a novel solution called Locality Alignment, which involves a post-training stage for Vision Transformers. This process aims to enhance the local semantic extraction]]></description><link>http://direct.ecency.com/ai/@abdelzaher1/researchers-at-stanford-university-propose-locality-alignment-a-new-post-training-stage-for-vision-transformers-vits</link><guid isPermaLink="true">http://direct.ecency.com/ai/@abdelzaher1/researchers-at-stanford-university-propose-locality-alignment-a-new-post-training-stage-for-vision-transformers-vits</guid><category><![CDATA[ai]]></category><dc:creator><![CDATA[abdelzaher1]]></dc:creator><pubDate>Sun, 29 Dec 2024 03:03:15 GMT</pubDate><enclosure url="https://images.ecency.com/p/MG5aEqKFcQi7V5gYDHZmv4k3eyosSQtAxkUQXtKGRDzJufmXwXiohbu241dPPNBYvt6bdyJM7TVyp4HYEtQ7NPxGWGmWYYjTt?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Only thing that can save me now]]></title><link>http://direct.ecency.com/cats/@abdelzaher1/only-thing-that-can-save-me-now</link><guid isPermaLink="true">http://direct.ecency.com/cats/@abdelzaher1/only-thing-that-can-save-me-now</guid><category><![CDATA[cats]]></category><dc:creator><![CDATA[abdelzaher1]]></dc:creator><pubDate>Sat, 28 Dec 2024 09:56:15 GMT</pubDate><enclosure url="https://images.ecency.com/p/PB8ro82ZpZP35bVGjGoE93K3E4U5KX8KtMBJ2rgaWK8R2dnA2TX8ZdGQyYYNdspY6pCq9HizspqpfwsKyw8e52W2pCpATPgkbWfN9NgjWEwXjrCN?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[How Large Language Models (LLMs) can Perform Multiple, Computationally Distinct In-Context Learning (ICL) Tasks Simultaneously]]></title><description><![CDATA[In a recent study from the University of Wisconsin-Madison, the University of Michigan, and Microsoft Research, the occurrence of task superposition across different LLM kinds and scales has been empirically]]></description><link>http://direct.ecency.com/ai/@abdelzaher1/how-large-language-models-llms-can-perform-multiple-computationally-distinct-in-context-learning-icl-tasks-simultaneously</link><guid isPermaLink="true">http://direct.ecency.com/ai/@abdelzaher1/how-large-language-models-llms-can-perform-multiple-computationally-distinct-in-context-learning-icl-tasks-simultaneously</guid><category><![CDATA[ai]]></category><dc:creator><![CDATA[abdelzaher1]]></dc:creator><pubDate>Fri, 27 Dec 2024 00:08:42 GMT</pubDate><enclosure url="https://images.ecency.com/p/MG5aEqKFcQi7V5gYDHZmv4k3eyosSQtAxkUQXtKGRDzJufmXwXiohbu241dPPNBYvt6bdyJM7TVyp4HYEtQ7NPxGWGmWYYjTt?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[IoT-LLM: An AI Framework that Integrates IoT Sensor Data with LLMs to Enhance their Perception and Reasoning Abilities in the Physical World]]></title><description><![CDATA[Rule-based systems, traditional machine learning models, and basic AI-driven methods are conventional models for processing IoT data. Processing dense numerical data and complex time-series inputs are]]></description><link>http://direct.ecency.com/ai/@abdelzaher1/iot-llm-an-ai-framework-that-integrates-iot-sensor-data-with-llms-to-enhance-their-perception-and-reasoning-abilities-in-the</link><guid isPermaLink="true">http://direct.ecency.com/ai/@abdelzaher1/iot-llm-an-ai-framework-that-integrates-iot-sensor-data-with-llms-to-enhance-their-perception-and-reasoning-abilities-in-the</guid><category><![CDATA[ai]]></category><dc:creator><![CDATA[abdelzaher1]]></dc:creator><pubDate>Wed, 25 Dec 2024 22:46:06 GMT</pubDate><enclosure url="https://images.ecency.com/p/32FTXiZsHoAWdK4rNeEuX6jtsX8Br6dTAfBhwY1WwRR5xYYb5k7W5npxfGjw2H9FKNWDVfJkaS67SXm8xwhL6urSZdw2o7bnhyKwt65zDJ6vCHyLoRFXvq92xy8MR9GHsCbQe1J6u6SC516N?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Differentiable Adaptive Merging (DAM): A Novel AI Approach to Model Integration]]></title><description><![CDATA[Researchers from Arcee AI and Liquid AI propose a novel merging technique called Differentiable Adaptive Merging (DAM). DAM aims to tackle the complexities of merging language models by offering an efficient,]]></description><link>http://direct.ecency.com/ai/@abdelzaher1/differentiable-adaptive-merging-dam-a-novel-ai-approach-to-model-integration</link><guid isPermaLink="true">http://direct.ecency.com/ai/@abdelzaher1/differentiable-adaptive-merging-dam-a-novel-ai-approach-to-model-integration</guid><category><![CDATA[ai]]></category><dc:creator><![CDATA[abdelzaher1]]></dc:creator><pubDate>Tue, 24 Dec 2024 16:19:15 GMT</pubDate><enclosure url="https://images.ecency.com/p/TZjG7hXReeVv5zy3dz5CMLEZk5M8Vv7qQc3w3VfW6XqFQ1Ms1htuHj8dK4qVPEegWLm4mvsCereFr7Nk2yGCm9Uzh5zW2Vj5JHyqjPRk7bRHc7RRYsZdkjzwQtNhrM58iQTSxQruBh17L2?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Google AI Research Examines Random Circuit Sampling (RCS) for Evaluating Quantum Computer Performance in the Presence of Noise]]></title><description><![CDATA[Google researchers address the challenge of evaluating quantum computer performance in the noisy intermediate-scale quantum (NISQ) era, where quantum processors are highly susceptible to noise. The fundamental]]></description><link>http://direct.ecency.com/ai/@abdelzaher1/google-ai-research-examines-random-circuit-sampling-rcs-for-evaluating-quantum-computer-performance-in-the-presence-of-noise</link><guid isPermaLink="true">http://direct.ecency.com/ai/@abdelzaher1/google-ai-research-examines-random-circuit-sampling-rcs-for-evaluating-quantum-computer-performance-in-the-presence-of-noise</guid><category><![CDATA[ai]]></category><dc:creator><![CDATA[abdelzaher1]]></dc:creator><pubDate>Mon, 23 Dec 2024 20:00:24 GMT</pubDate><enclosure url="https://images.ecency.com/p/MG5aEqKFcQi7V5gYDHZmv4k3eyosSQtAxkUQXtKGRDzJufmXwXiohbu241dPPNBYvt6bdyJM7TVyp4HYEtQ7NPxGWGmWYYjTt?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Google AI Introduces Gemma-APS: A Collection of Gemma Models for Text-to-Propositions Segmentation]]></title><description><![CDATA[Google AI Releases Gemma-APS, a collection of Gemma models for text-to-propositions segmentation. The models are distilled from fine-tuned Gemini Pro models applied to multi-domain synthetic data, which]]></description><link>http://direct.ecency.com/ai/@abdelzaher1/google-ai-introduces-gemma-aps-a-collection-of-gemma-models-for-text-to-propositions-segmentation</link><guid isPermaLink="true">http://direct.ecency.com/ai/@abdelzaher1/google-ai-introduces-gemma-aps-a-collection-of-gemma-models-for-text-to-propositions-segmentation</guid><category><![CDATA[ai]]></category><dc:creator><![CDATA[abdelzaher1]]></dc:creator><pubDate>Sun, 22 Dec 2024 08:27:33 GMT</pubDate><enclosure url="https://images.ecency.com/p/MG5aEqKFcQi7V5gYDHZmv4k3eyosSQtAxkUQXtKGRDzJufmXwXiohbu241dPPNBYvt6bdyJM7TVyp4HYEtQ7NPxGWGmWYYjTt?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[MEGA-Bench: A Comprehensive AI Benchmark that Scales Multimodal Evaluation to Over 500 Real-World Tasks at a Manageable Inference Cost]]></title><description><![CDATA[A team of researchers from the MEGA-Bench Team introduces MEGA-Bench, an innovative and comprehensive benchmark that scales multimodal evaluation to encompass more than 500 real-world tasks. MEGA-Bench]]></description><link>http://direct.ecency.com/ai/@abdelzaher1/mega-bench-a-comprehensive-ai-benchmark-that-scales-multimodal-evaluation-to-over-500-real-world-tasks-at-a-manageable-inference</link><guid isPermaLink="true">http://direct.ecency.com/ai/@abdelzaher1/mega-bench-a-comprehensive-ai-benchmark-that-scales-multimodal-evaluation-to-over-500-real-world-tasks-at-a-manageable-inference</guid><category><![CDATA[ai]]></category><dc:creator><![CDATA[abdelzaher1]]></dc:creator><pubDate>Sat, 21 Dec 2024 07:02:39 GMT</pubDate><enclosure url="https://images.ecency.com/p/MG5aEqKFcQi7V5gYDHZmv4k3eyosSQtAxkUQXtKGRDzJufmXwXiohbu241dPPNBYvt6bdyJM7TVyp4HYEtQ7NPxGWGmWYYjTt?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Simular Research Introduces Agent S: An Open-Source AI Framework Designed to Interact Autonomously with Computers through a Graphical User Interface]]></title><description><![CDATA[Simular Research introduces Agent S, an open agentic framework designed to use computers like a human, specifically through autonomous interaction with GUIs. This framework aims to transform human-computer]]></description><link>http://direct.ecency.com/ai/@abdelzaher1/simular-research-introduces-agent-s-an-open-source-ai-framework-designed-to-interact-autonomously-with-computers-through-a</link><guid isPermaLink="true">http://direct.ecency.com/ai/@abdelzaher1/simular-research-introduces-agent-s-an-open-source-ai-framework-designed-to-interact-autonomously-with-computers-through-a</guid><category><![CDATA[ai]]></category><dc:creator><![CDATA[abdelzaher1]]></dc:creator><pubDate>Fri, 20 Dec 2024 05:41:12 GMT</pubDate><enclosure url="https://images.ecency.com/p/MG5aEqKFcQi7V5gYDHZmv4k3eyosSQtAxkUQXtKGRDzJufmXwXiohbu241dPPNBYvt6bdyJM7TVyp4HYEtQ7NPxGWGmWYYjTt?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Researchers from UCLA and Stanford Introduce MRAG-Bench: An AI Benchmark Specifically Designed for Vision-Centric Evaluation for Retrieval-Augmented Multimodal Models]]></title><description><![CDATA[Researchers from UCLA and Stanford introduced MRAG-Bench, a vision-centric benchmark designed to evaluate the effectiveness of LVLMs in scenarios where visual information provides a clear advantage over]]></description><link>http://direct.ecency.com/ai/@abdelzaher1/researchers-from-ucla-and-stanford-introduce-mrag-bench-an-ai-benchmark-specifically-designed-for-vision-centric-evaluation-for</link><guid isPermaLink="true">http://direct.ecency.com/ai/@abdelzaher1/researchers-from-ucla-and-stanford-introduce-mrag-bench-an-ai-benchmark-specifically-designed-for-vision-centric-evaluation-for</guid><category><![CDATA[ai]]></category><dc:creator><![CDATA[abdelzaher1]]></dc:creator><pubDate>Wed, 18 Dec 2024 23:14:21 GMT</pubDate><enclosure url="https://images.ecency.com/p/gPCasciUWmF7YQUnfF3MFnkJdgQwpoGnHNGwtfNvFCvZvokEq4chAKWE6o2LYBQZMvgiQXLCmMUr7xYo8NkxNrJbd6H8FJQK65aEvYZoGpkptUzMu5A8M61vQzaBtNnEitfQXcRC4XxPYuUa9U?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[OpenR: An Open-Source AI Framework Enhancing Reasoning in Large Language Models]]></title><description><![CDATA[Researchers from University College London, the University of Liverpool, Shanghai Jiao Tong University, The Hong Kong University of Science and Technology (Guangzhou), and Westlake University introduce]]></description><link>http://direct.ecency.com/ai/@abdelzaher1/openr-an-open-source-ai-framework-enhancing-reasoning-in-large-language-models</link><guid isPermaLink="true">http://direct.ecency.com/ai/@abdelzaher1/openr-an-open-source-ai-framework-enhancing-reasoning-in-large-language-models</guid><category><![CDATA[ai]]></category><dc:creator><![CDATA[abdelzaher1]]></dc:creator><pubDate>Tue, 17 Dec 2024 21:51:57 GMT</pubDate><enclosure url="https://images.ecency.com/p/MG5aEqKFcQi7V5gYDHZmv4k3eyosSQtAxkUQXtKGRDzJufmXwXiohbu241dPPNBYvt6bdyJM7TVyp4HYEtQ7NPxGWGmWYYjTt?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Researchers from Moore Threads AI Introduce TurboRAG: A Novel AI Approach to Boost RAG Inference Speed]]></title><description><![CDATA[Researchers from Moore Threads AI introduce TurboRAG, a novel approach to optimize the inference paradigm of RAG systems by pre-computing and storing the KV caches of documents offline. Instead of computing]]></description><link>http://direct.ecency.com/ai/@abdelzaher1/researchers-from-moore-threads-ai-introduce-turborag-a-novel-ai-approach-to-boost-rag-inference-speed</link><guid isPermaLink="true">http://direct.ecency.com/ai/@abdelzaher1/researchers-from-moore-threads-ai-introduce-turborag-a-novel-ai-approach-to-boost-rag-inference-speed</guid><category><![CDATA[ai]]></category><dc:creator><![CDATA[abdelzaher1]]></dc:creator><pubDate>Mon, 16 Dec 2024 20:30:45 GMT</pubDate><enclosure url="https://images.ecency.com/p/gPCasciUWmF7YQUnfF3MFnkJdgQwpoGnHNGwtfNvFCvZvokEq4chAKWE6o2LYBQZMvgiQXLCmMUr7xYo8NkxNrJbd6H8FJQK65aEvYZoF4AYod7Ctx2sF5Kt9pQfvzwDEBvW2NcDmWgvFrCuDQ?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Exposing Vulnerabilities in Automatic LLM Benchmarks: The Need for Stronger Anti-Cheating Mechanisms]]></title><description><![CDATA[Evaluating open-ended text generation is challenging because a single correct output is needed. Human evaluation is reliable but costly and time-consuming, so LLMs are often used as evaluators for tasks]]></description><link>http://direct.ecency.com/ai/@abdelzaher1/exposing-vulnerabilities-in-automatic-llm-benchmarks-the-need-for-stronger-anti-cheating-mechanisms</link><guid isPermaLink="true">http://direct.ecency.com/ai/@abdelzaher1/exposing-vulnerabilities-in-automatic-llm-benchmarks-the-need-for-stronger-anti-cheating-mechanisms</guid><category><![CDATA[ai]]></category><dc:creator><![CDATA[abdelzaher1]]></dc:creator><pubDate>Mon, 16 Dec 2024 00:11:54 GMT</pubDate><enclosure url="https://images.ecency.com/p/cio98SvSTP7Q89QsqpL61dQTo2QXkqYDfZUsvgcSCKVYQcNR3yutZaAVTXpm1ntGnZXGEyciGH5VCNQrxARQUdzpeNCRqXNSQQzJfsoBpSdppW3iU7VrV88wfH4ERFRMjVP61F3JTtQ1hSKRTR7aydC2EKf2WvH8xstqD6rAcrM4653MZimAoQeoVppxB8TyZ62NBT1qAK597chFHvYN1DgmjM9sTHnmEBrfeXF8JXZtKzhT4RXzSxP7B4WcnLdRCdydaeZVkyvBVDH97AiwkvZ53ZuVEdZMMJk4Mri9d2i3qLqbsdUjbDNqYt?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Researchers at Stanford University Propose ExPLoRA: A Highly Effective AI Technique to Improve Transfer Learning of Pre-Trained Vision Transformers (ViTs) Under Domain Shifts]]></title><description><![CDATA[Vision foundation models (VFMs) like DinoV2 and masked autoencoders (MAE) have shown excellent performance in tasks such as classification and semantic segmentation through self-supervised learning (SSL).]]></description><link>http://direct.ecency.com/ai/@abdelzaher1/researchers-at-stanford-university-propose-explora-a-highly-effective-ai-technique-to-improve-transfer-learning-of-pre-trained</link><guid isPermaLink="true">http://direct.ecency.com/ai/@abdelzaher1/researchers-at-stanford-university-propose-explora-a-highly-effective-ai-technique-to-improve-transfer-learning-of-pre-trained</guid><category><![CDATA[ai]]></category><dc:creator><![CDATA[abdelzaher1]]></dc:creator><pubDate>Sat, 14 Dec 2024 17:42:03 GMT</pubDate><enclosure url="https://images.ecency.com/p/cio98SvSTP7Q89QsqpL61dQTo2QXkqYDfZUsvgcSCKVYQcNR3yutZaAVTXpm1ntGnZXGCvxKdoGNVjvtxvVsKDdbvp424NgK9tHRu3YMmBDvRB29vKEZWCqqAnab4hRTpBNNTyQzAKyGMJb2oZS97UcvqDwcxzrow3Ntg3UrsnmyDLwRj4KYY8BeV8N447az5d9vfq99Zzknew1qEKys8Tkdkb1QsT4D77wKi6z9J3jGViZ7j8PQpQATij9pDV9zKgD12QDDm9W7fyhKyPqFbBHwH1MLTWgHXgMeQi5Ss3WoSbbkn81HXKwooE?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[INTELLECT-1: The First Decentralized 10-Billion-Parameter AI Model Training]]></title><description><![CDATA[Prime Intellect AI launches INTELLECT-1, the first decentralized training run of a 10-billion-parameter model, inviting anyone to contribute compute and participate. This initiative breaks new ground by]]></description><link>http://direct.ecency.com/ai/@abdelzaher1/intellect-1-the-first-decentralized-10-billion-parameter-ai-model-training</link><guid isPermaLink="true">http://direct.ecency.com/ai/@abdelzaher1/intellect-1-the-first-decentralized-10-billion-parameter-ai-model-training</guid><category><![CDATA[ai]]></category><dc:creator><![CDATA[abdelzaher1]]></dc:creator><pubDate>Fri, 13 Dec 2024 06:11:30 GMT</pubDate><enclosure url="https://images.ecency.com/p/MG5aEqKFcQi7V5gYDHZmv4k3eyosSQtAxkUQXtKGRDzJufmXwXiohbu241dPPNBYvt6bdyJM7TVyp4HYEtQ7NPxGWGmWYYjTt?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[OpenAI Releases Swarm: An Experimental AI Framework for Building, Orchestrating, and Deploying Multi-Agent Systems]]></title><description><![CDATA[OpenAI introduces the Swarm Framework as a solution to simplify the complexities inherent in multi-agent orchestration. Swarm is an experimental framework that focuses on making agent coordination, execution,]]></description><link>http://direct.ecency.com/ai/@abdelzaher1/openai-releases-swarm-an-experimental-ai-framework-for-building-orchestrating-and-deploying-multi-agent-systems</link><guid isPermaLink="true">http://direct.ecency.com/ai/@abdelzaher1/openai-releases-swarm-an-experimental-ai-framework-for-building-orchestrating-and-deploying-multi-agent-systems</guid><category><![CDATA[ai]]></category><dc:creator><![CDATA[abdelzaher1]]></dc:creator><pubDate>Fri, 13 Dec 2024 01:06:36 GMT</pubDate><enclosure url="https://images.ecency.com/p/32FTXiZsHoAWdK4rNeEuX6jtsX8Br6dTAfBhwY1WwRR5xYYb5k7W5npxfGjw2H9FKNWDVfJkaS67SXm8xwhL6urSZdw2o7bnhyKwt65zD4wLghrQC2iw5Ke86cAZyanM6zucX54AYAmetY7g?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Researchers from UCSD and Adobe Introduce Presto!: An AI Approach to Inference Acceleration for Score-based Diffusion Transformers via Reducing both Sampling Steps and Cost Per Step]]></title><description><![CDATA[Existing attempts to address the challenges in Text-to-Audio (TTA) and Text-to-Music (TTM) generation have primarily focused on autoregressive (AR) techniques and diffusion models. Diffusion-based methods]]></description><link>http://direct.ecency.com/ai/@abdelzaher1/researchers-from-ucsd-and-adobe-introduce-presto-an-ai-approach-to-inference-acceleration-for-score-based-diffusion-transformers</link><guid isPermaLink="true">http://direct.ecency.com/ai/@abdelzaher1/researchers-from-ucsd-and-adobe-introduce-presto-an-ai-approach-to-inference-acceleration-for-score-based-diffusion-transformers</guid><category><![CDATA[ai]]></category><dc:creator><![CDATA[abdelzaher1]]></dc:creator><pubDate>Thu, 12 Dec 2024 20:02:12 GMT</pubDate><enclosure url="https://images.ecency.com/p/cio98SvSTP7Q89QsqpL61dQTo2QXkqYDfZUsvgcSCKVYQcNR3yutZaAVTXpm1ntGnZXHDTit3wPKHURd5PZASwKRdnumKnvAita8D4aTvLW1HVPeeFMuYn17WerY1Gomkv5aXj8BzR64ibaK3nQVziaxiJ534rTY79narUUpp23nthiJZurTfvUjuYsxge4bUwrG7LZojJfnmwSqFqFjq31AXzqeQVKqCGA9yjyxK4Jo2PeYD43JD4QNSgedpkunEPtNpxN13Fh3kcdij7XJ8mUKHARc5xdaLXUQxtvJVdYDiNdeHTs9istvFG?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[Google AI Researchers Propose Astute RAG: A Novel RAG Approach to Deal with the Imperfect Retrieval Augmentation and Knowledge Conflicts of LLMs]]></title><description><![CDATA[When RAG systems retrieve external data, there is always the risk of pulling in irrelevant, outdated, or malicious information. A major challenge associated with RAG is the issue of imperfect retrieval.]]></description><link>http://direct.ecency.com/ai/@abdelzaher1/google-ai-researchers-propose-astute-rag-a-novel-rag-approach-to-deal-with-the-imperfect-retrieval-augmentation-and-knowledge</link><guid isPermaLink="true">http://direct.ecency.com/ai/@abdelzaher1/google-ai-researchers-propose-astute-rag-a-novel-rag-approach-to-deal-with-the-imperfect-retrieval-augmentation-and-knowledge</guid><category><![CDATA[ai]]></category><dc:creator><![CDATA[abdelzaher1]]></dc:creator><pubDate>Wed, 11 Dec 2024 12:48:39 GMT</pubDate><enclosure url="https://images.ecency.com/p/cio98SvSTP7Q89QsqpL61dQTo2QXkqYDfZUsvgcSCKVYQcNR3yutZaAVTXpm1ntGnZXFiXbwUC1qowykTjiZbqJqqNPpuKE4VctsKADNtY12nnUoim8z1g4M53v9gZh1mKpNTWyjCaQmYTWwgbmsKtGiDUT3FQxk8wYYAGM7kPahM7yJgKjQH9qCMoPXMij3E19sHXzfgJqjnmCWVwh5dA6MvShRPgBJkyCM9RrxUdoxNnvPW6DvHqqKGv4cTf5qCxmWD4fVmPVU9MsK6aUepVkBpD8u7McyLV2ukGCtndAmf8zXrEGdt2QGAc?format=match&amp;mode=fit" length="0" type="false"/></item></channel></rss>