AI Knowledge Base

With the rapid development and wide application of large-scale language modeling technology, its potential security risks have increasingly become the focus of the industry's attention. In order to address these challenges, many of the world's top technology companies, standardization organizations and research institutions have constructed and released their own security frameworks. In this paper, we will analyze nine of the ...

In the field of Large Language Modeling (LLM) research, the model's Leap-of-Thought ability, i.e., creativity, is no less important than the logical reasoning ability represented by Chain-of-Thought. However, the current LLM creativity ...

Mastering Claude Code: Hands-on Agentic Coding Tips from the Front Lines Claude Code is a command-line tool for Agentic Coding. By agentic coding, we mean giving AI a certain degree of autonomy...

The GPT-4.1 family of models provides significant improvements in coding, instruction adherence, and long context processing capabilities compared to GPT-4o. Specifically, it performs better on code generation and repair tasks, understands and executes complex instructions more accurately, and can efficiently handle longer input text...

1. INTRODUCTION In today's information explosion, a large amount of knowledge is stored in the form of tables in web pages, Wikipedia and relational databases. However, traditional question and answer systems often struggle to handle complex queries across multiple tables, which has become a major challenge in the field of artificial intelligence. To address this challenge, researchers ...

As the capabilities of large-scale language models (LLMs) are rapidly evolving, traditional benchmark tests, such as MMLU, are showing limitations in distinguishing top models. Relying on knowledge quizzes or standardized tests alone, it has become difficult to fully measure the nuanced capabilities of models that are crucial in real-world interactions, such as...

Large Language Models (LLMs) are evolving rapidly, and their reasoning ability has become a key indicator of their intelligence level. In particular, models with long reasoning capabilities, such as OpenAI's o1, DeepSeek-R1, QwQ-32B, and Kimi K1.5 ...

INTRODUCTION In recent years, Large Language Models (LLMs) have made impressive progress in the field of Artificial Intelligence (AI), and their powerful language comprehension and generation capabilities have led to a wide range of applications in several domains. However, LLMs still face many challenges when dealing with complex tasks that require invoking external tools...

The Python ecosystem has never been short of package management and environment management tools, from the classic pip and virtualenv to pip-tools and conda to the modern Poetry, PDM, and so on. Each tool has its area of specialization, but often...

INTRODUCTION In recent years, multi-intelligent systems (MAS) have attracted much attention in the field of artificial intelligence. These systems attempt to solve complex, multi-step tasks through the collaboration of multiple Large Language Model (LLM) intelligences. However, despite the high expectations of MAS, their performance in practical applications...

Large Language Models (LLMs) like Claude are not created by humans writing program code; they are trained on massive amounts of data. In the process, the models learn their own problem-solving strategies. These strategies are hidden in the billions of times the model generates each word...

Recently, Anthropic has introduced a new tool called "think", which is designed to enhance the capabilities of the Claude model for complex problem solving. In this paper, we will delve into the design philosophy of the "think" tool, its performance, and the practical application of the most...

Abstract Information retrieval systems are critical for efficient access to large document collections. Recent approaches utilize Large Language Models (LLMs) to improve retrieval performance through query augmentation, but typically rely on expensive supervised learning or distillation techniques that require significant computational resources and manually labeled data ...

Large reasoning models exploit vulnerabilities when given the opportunity. Research suggests that these exploits can be detected by using large language models (LLMs) to monitor their chains-of-thought (CoT). Punishing models for "bad thoughts" does not prevent most misbehavior...

Background A recent article titled Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learnin...

The GraphRAG project aims to extend the range of questions that AI systems can answer on private datasets by exploiting implicit relationships in unstructured text. A key advantage of GraphRAG over traditional vector RAG (or "semantic search") is its ability to answer questions about...

If you have read Jina's last classic article "Design and Implementation of DeepSearch/DeepResearch", then you may want to dig deeper into some details that can greatly improve the quality of answers. This time, we will focus on two details: extracting optimal text from long web pages...

Gemma 3 Key Information Summary I. Key Metrics Parameters Details Model size 100 million to 27 billion parameters in four versions: 1B, 4B, 12B, 27B Architecture Transformer-based decoder-specific architecture inherited from Gem...

1. Background and Issues With the rapid development of Artificial Intelligence (AI) technology, especially the advancement of diffusion modeling, AI has been able to generate very realistic portrait images. For example, technologies like InstantID require only one photo to generate multiple new images with the same identity...