​[[{“value”:” Designing autonomous agents that can navigate complex web environments raises many challenges, in particular when such agents incorporate both textual and visual information. More classically, agents have limited capability since they are confined to synthetic, text-based environments with well-engineered reward signals, which restricts their… Read More »OpenWebVoyager: Building Multimodal Web Agents via Iterative Real-World Exploration, Feedback and Optimization Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Knowledge distillation (KD) is a machine learning technique focused on transferring knowledge from a large, complex model (teacher) to a smaller, more efficient one (student). This approach is used extensively to reduce large language models’ computational load and resource requirements while retaining as much… Read More »This AI Paper from Google Research Introduces Speculative Knowledge Distillation: A Novel AI Approach to Bridging the Gap Between Teacher and Student Models Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Tactile sensing plays a crucial role in robotics, helping machines understand and interact with their environment effectively. However, the current state of vision-based tactile sensors poses significant challenges. The diversity of sensors—ranging in shape, lighting, and surface markings—makes it difficult to build a universal… Read More »Meta AI Releases Sparsh: The First General-Purpose Encoder for Vision-Based Tactile Sensing Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” A key question about LLMs is whether they solve reasoning tasks by learning transferable algorithms or simply memorizing training data. This distinction matters: while memorization might handle familiar tasks, true algorithmic understanding allows for broader generalization. Arithmetic reasoning tasks could reveal if LLMs apply… Read More »Decoding Arithmetic Reasoning in LLMs: The Role of Heuristic Circuits over Generalized Algorithms Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” In recent years, multimodal large language models (MLLMs) have revolutionized vision-language tasks, enhancing capabilities such as image captioning and object detection. However, when dealing with multiple text-rich images, even state-of-the-art models face significant challenges. The real-world need to understand and reason over text-rich images… Read More »Leopard: A Multimodal Large Language Model (MLLM) Designed Specifically for Handling Vision-Language Tasks Involving Multiple Text-Rich Images Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Quantization is an essential technique in machine learning for compressing model data, which enables the efficient operation of large language models (LLMs). As the size and complexity of these models expand, they increasingly demand vast storage and memory resources, making their deployment a challenge… Read More »Cornell Researchers Introduce QTIP: A Weight-Only Post-Training Quantization Algorithm that Achieves State-of-the-Art Results through the Use of Trellis-Coded Quantization (TCQ) Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs) have emerged as powerful tools in natural language processing, yet understanding their internal representations remains a significant challenge. Recent breakthroughs using sparse autoencoders have revealed interpretable “features” or concepts within the models’ activation space. While these discovered feature point clouds… Read More »Multi-Scale Geometric Analysis of Language Model Features: From Atomic Patterns to Galaxy Structures Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Escalation in AI implies an increased infrastructure expenditure. The massive and multidisciplinary research exerts economic pressure on institutions as high-performance computing (HPC)  costs an arm and a leg. HPC is financially draining and critically impacts energy consumption and the environment. By 2030, AI is… Read More »Researchers at KAUST Use Anderson Exploitation to Maximize GPU Efficiency with Greater Model Accuracy and Generalizability Adeeba Alam Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:” In recent times, large language models (LLMs) built on the Transformer architecture have shown remarkable abilities across a wide range of tasks. However, these impressive capabilities usually come with a significant increase in model size, resulting in substantial GPU memory costs during inference. The… Read More »KVSharer: A Plug-and-Play Machine Learning Method that Shares the KV Cache between Layers to Achieve Layer-Wise Compression Divyesh Vitthal Jawkhede Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The Evidence Lower Bound (ELBO) is a key objective for training generative models like Variational Autoencoders (VAEs). It parallels neuroscience, aligning with the Free Energy Principle (FEP) for brain function. This shared objective hints at a potential unified machine learning and neuroscience theory. However,… Read More »iP-VAE: A Spiking Neural Network for Iterative Bayesian Inference and ELBO Maximization Sana Hassan Artificial Intelligence Category – MarkTechPost