EvoAgent: A Generic Method to Automatically Extend Expert Agents to Multi-Agent Systems via the Evolutionary Algorithm

Practical Solutions for AI in Graph Comprehension and Reasoning Overview Developing and evaluating Large Language Models (LLMs) to understand and reason about graph-structured data is crucial for various applications, including social network analysis, drug discovery, recommendation…

Researchers have developed an algorithm called EUREKA that uses advanced LLMs, such as GPT-4, to create reward functions for complex skill acquisition through reinforcement learning. EUREKA outperforms human-engineered rewards and enables in-context learning based on human…

Researchers at TUS and collaborating institutes have created a deep learning binary classifier that identifies an unknown quasicrystalline phase in materials with over 92% accuracy, revolutionizing material analysis with wide-ranging technological implications.

IBM researchers have introduced LAB (Large-scale Alignment for chatbots) to address scalability challenges in instruction-tuning for large language models (LLMs). LAB leverages a taxonomy-guided synthetic data generation process and a multi-phase training framework to enhance LLM…

Transforming Language Model Training with Critique Fine-Tuning Limitations of Traditional Training Methods Traditional training for language models often relies on imitating correct answers. While this works for simple tasks, it limits the model’s ability to think…

Months after its release, the romantic comedy “Prom Pact” on Disney platforms has received criticism for its use of AI-generated extras. A clip from the movie, featuring artificial characters cheering alongside real actors, has been widely…

MIT researchers utilized deep learning models to uncover a groundbreaking class of antibiotics, potentially combatting drug-resistant bacteria. Spearheaded by Dr. Jim Collins, the Antibiotics-AI Project targets the development of seven new antibiotic classes. By employing machine…

“`html Новое исследование MIT о лимитах больших языковых моделей Недавнее исследование MIT:s Computer Science and Artificial Intelligence Laboratory (CSAIL) подчеркнуло, что большие языковые модели (LLM) проявляют себя отлично в знакомых сценариях, но сталкиваются с трудностями в…

Transforming AI with SWEET-RL Transforming AI with SWEET-RL Introduction to Large Language Models (LLMs) Large language models (LLMs) are evolving into advanced autonomous agents capable of executing intricate tasks involving reasoning and decision-making. These models are…

Machine Unlearning: Enhancing Resilience Against Risks and Vulnerabilities Introduction The increasing use of machine learning models in critical applications has raised concerns about their susceptibility to manipulation and exploitation. Techniques are urgently needed to allow models…

Lasso Security discovered 1,681 exposed API tokens with varying access levels in code on HuggingFace and GitHub, posing significant security risks. Tokens could potentially allow unauthorized modifications to popular AI models, with consequences if misused. The…

Vision-Language Models: Practical Solutions and Value Evolution of Vision-Language Models Vision-language models have evolved significantly, with two distinct generations. The first generation expanded on large-scale classification pretraining, while the second generation unified captioning and question-answering tasks.…

This text provides a hands-on guide to building a language model for masked language modeling (MLM) tasks using Python and the Transformers library. It discusses the importance of large language models (LLMs) in the machine learning…

Precise Control Over Language Models Effective management of language models is essential for developers and data scientists. Large models like Claude from Anthropic provide great opportunities, but handling tokens efficiently is a significant challenge. Anthropic’s Token…

MIT researchers have developed an Automatic Surface Reconstruction framework using machine learning to design new compounds or alloys for catalysts without reliance on chemist intuition. The method provides dynamic, thorough characterization of material surfaces, revealing previously…

Model Context Protocol (MCP) for AI Assistants Introduction to Model Context Protocol (MCP) for AI Assistants The Model Context Protocol (MCP) establishes a standardized method for connecting AI assistants, such as large language models (LLMs), with…

Practical Solutions and Value of AutoToS in AI Planning Introduction to AI Planning and LLMs AI planning involves creating sequences of actions for autonomous systems, such as robotics and logistics. Large language models (LLMs) show promise…