Customer Relationship Management (CRM) has become integral to business operations as the center for managing customer interactions, data, and processes. Integrating advanced AI into CRM can transform these systems by automating routine processes, delivering personalized experiences, and streamlining customer service efforts. As organizations increasingly adopt AI-driven approaches, the need for intelligent agents capable of performing… →

Retrieval-augmented generation (RAG) represents a great advancement in the capability of large language models (LLMs) to perform tasks accurately by incorporating relevant external information into their processing workflows. This approach, blending information retrieval techniques with generative modeling, has seen growing utility in complex applications such as machine translation, question answering, and comprehensive content generation. By… →

In recent years, there has been significant development in the field of large pre-trained models for learning robot policies. The term “policy representation” here refers to the different ways of interfacing with the decision-making mechanisms of robots, which can potentially facilitate generalization to new tasks and environments. Vision-language-action (VLA) models are pre-trained with large-scale robot… →

Natural language processing (NLP) continues to evolve with new methods like in-context learning (ICL), which offers innovative ways to enhance large language models (LLMs). ICL involves conditioning models on specific example demonstrations without directly modifying the model’s parameters. This method is especially valuable for training LLMs quickly for various tasks. However, ICL can be highly… →

Biomolecular dynamics simulations are crucial for life sciences, offering insights into molecular interactions. While classical molecular dynamics (MD) simulations are efficient, they lack chemical precision. Methods like density functional theory (DFT) achieve high accuracy but are too computationally intense for large biomolecules. MD simulations allow observation of molecular behavior, with classical MD using interatomic potentials… →

Large language models (LLMs) have shown exceptional capabilities in comprehending human language, reasoning, and knowledge acquisition, suggesting their potential to serve as autonomous agents. However, training high-performance web agents based on open LLMs within online environments, such as WebArena, faces several critical challenges. The first challenge is insufficient predefined training tasks in online benchmarks. The… →

Large language models (LLMs) have revolutionized natural language processing by making strides in text generation, summarization, and translation. Even though they excel at language tasks, they need help handling complex, multi-step reasoning tasks that require careful progression through each step. Researchers have been exploring structured frameworks that enhance these models’ reasoning abilities, moving beyond conventional… →

Machine learning research has advanced toward models that can autonomously design and discover data structures tailored to specific computational tasks, such as nearest neighbor (NN) search. This shift in methodology allows models to learn not only the structure of data but also how to optimize query responses, minimizing storage needs and computation time. Machine learning… →

In the rapidly evolving field of household robotics, a significant challenge has emerged in executing personalized organizational tasks, such as arranging groceries in a refrigerator. These tasks require robots to balance user preferences with physical constraints while avoiding collisions and maintaining stability. While Large Language Models (LLMs) enable natural language communication of user preferences, this… →