Transition AI Jim Fan: Recently, I have been somewhat silent on X. This year has been a transformative journey for me. Although emerging large language models like Grok-4 and Kimi K2 are impressive, the field of robotics still resembles a wondrous western wilderness. It reminds me of the NLP field in 2018, when GPT-1 was … Read more
In the field of artificial intelligence, especially with the rapid development of large language models (LLMs), single-agent systems have shown significant limitations. This article will delve into why we need to shift from single-agent to multi-agent systems, analyze the current technical flaws of large language models, and elaborate on how multi-agent systems can effectively address … Read more
The application of large language models in server environments during the AI era, especially in the inference phase, presents numerous opportunities. AI can be divided into two main stages: training and inference. From pre-training to fine-tuning and reinforcement, a general large model is developed, which is then used to generate application models; the inference side … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP graduate students, university professors, and corporate researchers.The vision of the community is to promote communication and progress between the academic and industrial sectors of natural language processing and machine learning, especially for beginners. Paper Title: Soft Reasoning: … Read more
Source: ZHUAN ZHI This article is approximately 1000 words long and is recommended for a 5-minute read. This article presents SparseLoRA, a method to accelerate the fine-tuning of large language models through contextual sparsity. Fine-tuning large language models (LLMs) is often both computationally intensive and memory-consuming. While parameter-efficient fine-tuning methods such as QLoRA and DoRA … Read more
A professional community focused on the AIGC field, paying attention to the development and application of large language models (LLMs) such as Microsoft & OpenAI, Baidu Wenxin Yiyan, and iFlytek Spark, focusing on market research of LLMs and the AIGC developer ecosystem. Welcome to follow! With the emergence of ChatGPT, large language models have achieved … Read more
Today’s AI agents are capable of perceiving, making decisions, and taking actions independently. With the rise of large language model (LLM)-driven AI agents, we are on the brink of a new era: AI agents may form their own societies and coexist harmoniously with humans. Below, we will discuss what AI agents are, why they are … Read more
Click Follow us by clicking the blue text aboveCover Image:True “Agentic AI” requires intrinsic motivation, predictive models of the world, and sociality. “ 𝕀²·ℙarad𝕚g𝕞 Intelligent Square Paradigm Research: Writing Deconstructs Intelligence, Paradigm Enhances Cognition Current large language models (LLMs), despite exhibiting astonishing intelligence, do not possess true “agency”. The behaviors we perceive as LLM “agents” … Read more
Artificial Intelligence (AI) is spreading at an astonishing rate, like wildfire across the grasslands. In this field, Large Language Models (LLM) have emerged as a focal point of attention. Meanwhile, the concept of AI Agent is rising like a new star, sparking widespread discussion throughout the tech community and society at large. Industry experts and … Read more
This study systematically reviews and analyzes the technological development of Large Multimodal Reasoning Models (LMRMs). It outlines the evolution of the field from early modular, perception-driven architectures to unified, language-centric frameworks, and introduces the cutting-edge concept of Native Large Multimodal Reasoning Models (N-LMRMs). The paper constructs a structured roadmap for the development of multimodal reasoning, … Read more