What is Mcp Toolsuite

The Multi-Tool AI Agent and MCP Server is a conversational AI platform that utilizes the Model Context Protocol (MCP) to enable seamless communication between AI agents and tools. This system comprises a client that intelligently discovers specialized AI-powered tools hosted on an MCP-compliant server, allowing it to perform tasks ranging from quick web searches to complex data analysis and visualization.

Use cases

The system can be used for various applications, including but not limited to answering factual queries, conducting in-depth research, analyzing code, generating visualizations, and summarizing extensive texts. It is suitable for users who need assistance with academic research, business analysis, code explanations, or general information retrieval.

How to use

Users can interact with the client application by typing their queries or requests. The system uses a Router LLM to analyze input, decide which tool to invoke, and route the request to the appropriate MCP server tool. The user can also reset the conversation or exit the application easily. The client supports multi-line input and generates reports or responses based on the tools’ outputs.

Key features

Key features of the system include dynamic tool discovery, an intelligent Router-Agent architecture for effective task routing, multiple specialized tools for diverse tasks (like quick_search, medium_search, and conduct_deep_research), and robust input handling capabilities for natural language processing and response generation.

Where to use

This platform can be deployed in various environments, from academic institutions needing extensive research support to businesses looking for agile data analysis and visualization capabilities. It is also suitable for developers seeking quick solutions for code analysis or documentation within their projects.

Multi-Tool AI Agent & MCP Server

This project is a powerful, conversational AI agent platform built on the Model Context Protocol (MCP). It features a robust server that exposes a suite of specialized AI-powered tools and a dynamic client that can intelligently discover and use them.

The system is designed to be a versatile AI assistant, capable of everything from quick web searches and in-depth research to data visualization and code analysis. The client maintains conversational context, allowing for natural, follow-up questions.

The logic for conduct_deep_research tool is derived from this multi-agent deep research system, but non-MCP: https://github.com/alberttrann/multi-agent_deep-research

Key Concepts

• Model Context Protocol (MCP): The backbone of this system. MCP provides a standard way for AI agents and tools to communicate. This allows our client to be “agnostic”—it can connect to any MCP-compliant server and use its tools without needing to be pre-programmed with their specific details.

• Dynamic Tool Discovery: Upon connecting, the client asks the server, “What can you do?” The server responds with a machine-readable list of its tools, descriptions, and required inputs. This makes the system incredibly modular and extensible.

• Router-Agent Architecture: The client uses a “Router” LLM as its brain. This Router analyzes the user’s request in the context of the conversation history and the available tools. It then makes an intelligent decision about which tool to call and what arguments to pass, effectively “routing” the request to the correct function.

• Resilient Tools: The search tools (quick_search, medium_search) are not simple, one-shot functions. They contain an internal “Search-Evaluate-Refine” loop. If an initial search fails to yield a good result, the tool will automatically try a different, more specific query before returning an answer, increasing reliability.

Architectural Flow

When a user submits a request, the following sequence of events occurs:

1. Input: The user types a request into the client application.

2. Contextual Prompting: The client constructs a prompt for the “Router” LLM. This prompt includes the user’s latest message, the history of the current conversation (for context), and the manifest of available tools discovered from the server.

3. Tool Selection: The Router LLM processes this prompt and decides which tool is most appropriate. It returns a JSON object specifying the tool’s name and the arguments, which have been inferred from the user’s natural language request.

4. MCP Tool Call: The client makes a formal tool call to the MCP server with the information provided by the Router.

5. Tool Execution: The MCP server receives the request, activates the corresponding tool (e.g., quick_search), and executes its logic. This might involve calling external APIs (like Tavily), running its own internal LLM prompts, or generating files (like charts).

6. Tool Response: The tool on the server completes its task and returns a structured JSON result to the client.

7. Final Synthesis: The client receives the tool’s result. If the result is a finished piece of text (like a summary or a quick search answer), it is displayed directly. If the result is data (like a file path for a chart), a final “Synthesizer” LLM is used to formulate a nice, conversational response to inform the user.

8. Memory Update: The user’s query and the final response are added to the client’s text-based history to provide context for the next turn.

Tool Breakdown

This server exposes a suite of powerful, specialized tools.

quick_search

• Purpose: For single, factual questions that need a fast, real-time web search.
• Best For: “What is the weather in Hanoi?”, “Who won the last World Cup?”, “What is the stock price of Apple?”
• How it Works: Uses an internal LLM to generate 1-2 optimized keywords from the user’s query, performs a web search, evaluates if the result is definitive, and retries with a refined query if necessary.

medium_search

• Purpose: For broader questions that require generating lists, recommendations, or comparisons from several web sources.
• Best For: “What are some fun indoor activities in Hanoi for a rainy day?”, “Compare React vs. Vue for web development”.
• How it Works: Generates multiple search queries to gather a wider range of information and then uses an LLM to synthesize the findings into a single, coherent answer.

conduct_deep_research

• Purpose: For complex, in-depth queries that require a formal, multi-page report with deep analysis and citations.
• Best For: “A detailed report on the architecture of V-JEPA 2”, “Analyze the economic impact of quantum computing”.
• Note: This tool is significantly slower and more resource-intensive than the others.

summarize_text

• Purpose: Condenses a long piece of text into a summary in a specified format.
• How to Use: Start your message with a command like “Summarize this into 5 bullet points:”, then paste the text you want summarized.

visualize_data

• Purpose: Generates a data visualization (bar, line, or pie chart) from a JSON object and saves it as an image.
• How to Use: Clearly state the chart type, title, axis labels, and provide the data in a JSON structure within your prompt.
• Output: The tool returns the file path to the generated image (e.g., generated_charts/chart_12345.png), which you can open on your computer.

explain_code

• Purpose: Analyzes a snippet of code and provides a structured, easy-to-understand explanation for a non-technical audience.
• Output: Generates a mini-report with four sections: Purpose, Analogy, Inputs & Outputs, and Potential Risks & Questions.

Setup and Installation

Follow these steps to get the project running on your local machine.

1. Prerequisites

• Python 3.10 or higher
• A Gemini API Key from Google AI Studio
• A Tavily API Key for the search tools

2. Clone the Repository

If this project is in a git repository, clone it to your local machine:

git clone https://github.com/alberttrann/toolsuite.git
cd MCP_ToolSuite

3. Set Up a Virtual Environment

It’s highly recommended to use a virtual environment to manage dependencies.

# Create a virtual environment
python -m venv .venv

# Activate it
# On Windows:
.venv\Scripts\activate
# On macOS/Linux:
source .venv/bin/activate

4. Install Dependencies

Install all the required Python packages using the requirements.txt file.

pip install -r requirements.txt

5. Create Your Environment File

Create a new file named .env in the root of your project directory (MCP_DeepResearch/). Add your secret API keys to this file.

.env file contents:

GEMINI_API_KEY="YOUR_GOOGLE_GEMINI_API_KEY"
TAVILY_API_KEY="YOUR_TAVILY_API_KEY"

Replace the placeholder text with your actual keys.

How to Run the System

You will need to open two separate terminals or command prompts, both with the virtual environment activated.

In your FIRST terminal, start the MCP Server:

python mcp_server.py

You should see output indicating that the Uvicorn server is running on http://127.0.0.1:8000.

In your SECOND terminal, start the Client:

python llm_tool_caller_client.py

The client will start, discover the tools from the server, and then you will see the prompt to begin the conversation.

How to Use the Client

• Multi-line Input: You can type or paste multiple lines of text. When you are finished, type (end) on a new, empty line and press Enter to submit your request.
• Reset Conversation: To clear the agent’s memory and start a fresh conversation, type /new and press Enter.
• Exit: To close the client application, type exit and press Enter.

FINAL VERSION, SUITE-OF-TOOLS DEMO

(.venv) PS F:\MCP_DeepResearch> python F:\MCP_DeepResearch\llm_tool_caller_client.py
--- Manual Tool-Calling MCP Client (with Text-Based Memory) ---
--> Discovering available tools from the server...
--> Success! Found 6 tools.

New conversation started. Type `/new` to reset memory.

Your Request (type '(end)' on a new line to submit, or '/new', 'exit', 'quit'):
what's the current stock price of Apple
(end)

> Thinking (Step 1/3: Choosing a tool)...
> LLM decided to call 'quick_search' with arguments: {'query': "what's the current stock price of Apple"}

Executing tool on MCP server...

--- ✅ Tool Execution Complete ---
{
  "status": "success",
  "answer": "196.45 USD\n"
}
> Direct answer found in tool result. Presenting directly.

LLM Response: 196.45 USD


Your Request (type '(end)' on a new line to submit, or '/new', 'exit', 'quit'):
what is a latent space in machine learning
(end)

> Thinking (Step 1/3: Choosing a tool)...
> LLM decided to call 'medium_search' with arguments: {'query': 'What is a latent space in machine learning?'}

Executing tool on MCP server...

--- ✅ Tool Execution Complete ---
{
  "status": "success",
  "answer": "A latent space in machine learning is a compressed representation of data, focusing on essential features and underlying structure.  Think of it as a simplified map of complex data.\n\nHere's a breakdown:\n\n* **Compressed Representation:**  It reduces the complexity of high-dimensional data by focusing on the most important information, similar to dimensionality reduction.  Less important or redundant information is discarded.\n* **Essential Features:**  Each dimension within the latent space represents a \"latent variable,\" which is a hidden characteristic influencing the data's distribution. These are often not directly observable in the original data.\n* **Similarity Grouping:** Similar data points are clustered together in the latent space. This makes it easier for machine learning models to identify patterns and relationships.\n* **Dimensionality Reduction:**  Latent spaces are typically lower-dimensional than the original data, making them more computationally efficient.\n* **Applications:** Latent spaces are used in various applications, including:\n    * Image generation\n    * Language processing\n    * Data compression\n\n\nWhile powerful, interpreting latent spaces can be challenging due to their complexity and the \"black box\" nature of some machine learning models.  Visualization techniques can help with interpretation.\n"
}
> Direct answer found in tool result. Presenting directly.

LLM Response: A latent space in machine learning is a compressed representation of data, focusing on essential features and underlying structure.  Think of it as a simplified map of complex data.

Here's a breakdown:

* **Compressed Representation:**  It reduces the complexity of high-dimensional data by focusing on the most important information, similar to dimensionality reduction.  Less important or redundant information is discarded.
* **Essential Features:**  Each dimension within the latent space represents a "latent variable," which is a hidden characteristic influencing the data's distribution. These are often not directly observable in the original data.
* **Similarity Grouping:** Similar data points are clustered together in the latent space. This makes it easier for machine learning models to identify patterns and relationships.
* **Dimensionality Reduction:**  Latent spaces are typically lower-dimensional than the original data, making them more computationally efficient.
* **Applications:** Latent spaces are used in various applications, including:
    * Image generation
    * Language processing
    * Data compression


While powerful, interpreting latent spaces can be challenging due to their complexity and the "black box" nature of some machine learning models.  Visualization techniques can help with interpretation.


Your Request (type '(end)' on a new line to submit, or '/new', 'exit', 'quit'):
Gợi ý cho tôi 3 nhà hàng món Việt ngon ở Quận 1, Sài Gòn cho một bữa tối gia đình.
(end)

> Thinking (Step 1/3: Choosing a tool)...
> LLM decided to call 'medium_search' with arguments: {'query': 'Gợi ý cho tôi 3 nhà hàng món Việt ngon ở Quận 1, Sài Gòn cho một bữa tối gia đình.'}

Executing tool on MCP server...

--- ✅ Tool Execution Complete ---
{
  "status": "success",
  "answer": "D\u1ef1a tr\u00ean k\u1ebft qu\u1ea3 t\u00ecm ki\u1ebfm, \u0111\u00e2y l\u00e0 3 g\u1ee3i \u00fd nh\u00e0 h\u00e0ng m\u00f3n Vi\u1ec7t ngon \u1edf Qu\u1eadn 1, S\u00e0i G\u00f2n cho m\u1ed9t b\u1eefa t\u1ed1i gia \u0111\u00ecnh:\n\n* **Qu\u00e1n B\u1ee5i:**  Nhi\u1ec1u \u0111\u1ecba \u0111i\u1ec3m \u1edf Qu\u1eadn 1, n\u1ed5i ti\u1ebfng v\u1edbi kh\u00f4ng gian \u1ea5m c\u00fang, ho\u00e0i c\u1ed5, th\u1ef1c \u0111\u01a1n \u0111a d\u1ea1ng c\u00e1c m\u00f3n Vi\u1ec7t truy\u1ec1n th\u1ed1ng v\u00e0 c\u00f3 c\u1ea3 m\u00f3n chay. \u0110\u01b0\u1ee3c \u0111\u00e1nh gi\u00e1 cao, ph\u00f9 h\u1ee3p v\u1edbi nhi\u1ec1u l\u1ee9a tu\u1ed5i.\n* **D\u00ec Mai:**  Ph\u1ee5c v\u1ee5 c\u00e1c m\u00f3n \u0103n nh\u00e0 n\u1ea5u trong khung c\u1ea3nh Indochine hi\u1ec7n \u0111\u1ea1i.  K\u1ebft qu\u1ea3 t\u00ecm ki\u1ebfm \u0111\u1ec1 c\u1eadp \u0111\u1ebfn c\u00e1c m\u00f3n nh\u01b0 mi\u1ebfn b\u00f2 x\u00e0o l\u0103n, ph\u1edf cu\u1ed1n g\u00e0 n\u01b0\u1edbng l\u00e1 chanh, t\u00f4m rim D\u00ec Mai, ch\u1ea3 gi\u00f2 v\u1ecbt n\u1ea5m tai m\u00e8o, b\u00fan ngan m\u0103ng kh\u00f4.\n* **Secret Garden:**  Kh\u00f4ng gian \u0111\u01b0\u1ee3c mi\u00eau t\u1ea3 l\u00e0 tuy\u1ec7t v\u1eddi cho gia \u0111\u00ecnh.  Tuy th\u00f4ng tin \u00edt h\u01a1n hai nh\u00e0 h\u00e0ng tr\u00ean, nh\u01b0ng \u0111\u01b0\u1ee3c \u0111\u1ec1 c\u1eadp trong danh s\u00e1ch c\u00e1c nh\u00e0 h\u00e0ng m\u00f3n Vi\u1ec7t n\u00ean c\u0169ng l\u00e0 m\u1ed9t l\u1ef1a ch\u1ecdn \u0111\u00e1ng c\u00e2n nh\u1eafc.\n\nL\u01b0u \u00fd r\u1eb1ng k\u1ebft qu\u1ea3 t\u00ecm ki\u1ebfm t\u1eadp trung nhi\u1ec1u v\u00e0o Qu\u00e1n B\u1ee5i, n\u00ean th\u00f4ng tin v\u1ec1 hai nh\u00e0 h\u00e0ng c\u00f2n l\u1ea1i kh\u00e1 h\u1ea1n ch\u1ebf.  B\u1ea1n n\u00ean t\u00ecm ki\u1ebfm th\u00eam th\u00f4ng tin v\u1ec1 D\u00ec Mai v\u00e0 Secret Garden \u0111\u1ec3 ch\u1eafc ch\u1eafn ch\u00fang ph\u00f9 h\u1ee3p v\u1edbi nhu c\u1ea7u c\u1ee7a gia \u0111\u00ecnh.\n"     
}
> Direct answer found in tool result. Presenting directly.

LLM Response: Dựa trên kết quả tìm kiếm, đây là 3 gợi ý nhà hàng món Việt ngon ở Quận 1, Sài Gòn cho một bữa tối gia đình:

* **Quán Bụi:**  Nhiều địa điểm ở Quận 1, nổi tiếng với không gian ấm cúng, hoài cổ, thực đơn đa dạng các món Việt truyền thống và có cả món chay. Được đánh giá cao, phù hợp với nhiều lứa tuổi.
* **Dì Mai:**  Phục vụ các món ăn nhà nấu trong khung cảnh Indochine hiện đại.  Kết quả tìm kiếm đề cập đến các món như miến bò xào lăn, phở cuốn gà nướng lá chanh, tôm rim Dì Mai, chả giò vịt nấm tai mèo, bún ngan măng khô.
* **Secret Garden:**  Không gian được miêu tả là tuyệt vời cho gia đình.  Tuy thông tin ít hơn hai nhà hàng trên, nhưng được đề cập trong danh sách các nhà hàng món Việt nên cũng là một lựa chọn đáng cân nhắc.

Lưu ý rằng kết quả tìm kiếm tập trung nhiều vào Quán Bụi, nên thông tin về hai nhà hàng còn lại khá hạn chế.  Bạn nên tìm kiếm thêm thông tin về Dì Mai và Secret Garden để chắc chắn chúng phù hợp với nhu cầu của gia đình.


Your Request (type '(end)' on a new line to submit, or '/new', 'exit', 'quit'):
How do I start learning to play the guitar? Give me a list of beginner steps.
(end)

> Thinking (Step 1/3: Choosing a tool)...
> LLM decided to call 'medium_search' with arguments: {'query': 'Beginner steps to learn guitar'}

Executing tool on MCP server...

--- ✅ Tool Execution Complete ---
{
  "status": "success",
  "answer": "Beginner Steps to Learn Guitar:\n\n* **Start with the basics:**  Familiarize yourself with the parts of the guitar, learn the names of the open strings, and practice basic fretting and strumming techniques.  You can begin by plucking the low E string with your thumb.\n* **Learn basic chords:**  Focus on essential chords like G, C, D, E, and A.\n* **Practice scales:** Use your thumb or a pick to practice scales.\n* **Simple songs:**  Once comfortable with basic chords and strumming, try learning simple songs that use single note riffs and open chords.\n* **Power chords:**  Progress to learning power chords, starting with the low E string.  Experiment with different frets and the A string.\n* **Consistent practice:**  Play guitar as frequently as possible, even if it's just for a short time each day.  This will help you improve quickly.\n* **Formal lessons/courses:** Consider structured lessons (online or in-person) for a more guided approach.  Many beginner courses cover tuning, chord boxes, pick holding, and strumming patterns.\n* **Beginner-friendly resources:** Look for resources specifically designed for beginners, such as Andy Guitar's 10-day starter course or Lauren Bateman's beginner guitar system.  These often include easy songs and exercises.\n\n\nIt's important to avoid rushing the learning process.  Focus on building a solid foundation by mastering the fundamentals before moving on to more complex techniques.\n"
}
> Direct answer found in tool result. Presenting directly.

LLM Response: Beginner Steps to Learn Guitar:

* **Start with the basics:**  Familiarize yourself with the parts of the guitar, learn the names of the open strings, and practice basic fretting and strumming techniques.  You can begin by plucking the low E string with your thumb.
* **Learn basic chords:**  Focus on essential chords like G, C, D, E, and A.
* **Practice scales:** Use your thumb or a pick to practice scales.
* **Simple songs:**  Once comfortable with basic chords and strumming, try learning simple songs that use single note riffs and open chords.
* **Power chords:**  Progress to learning power chords, starting with the low E string.  Experiment with different frets and the A string.
* **Consistent practice:**  Play guitar as frequently as possible, even if it's just for a short time each day.  This will help you improve quickly.
* **Formal lessons/courses:** Consider structured lessons (online or in-person) for a more guided approach.  Many beginner courses cover tuning, chord boxes, pick holding, and strumming patterns.
* **Beginner-friendly resources:** Look for resources specifically designed for beginners, such as Andy Guitar's 10-day starter course or Lauren Bateman's beginner guitar system.  These often include easy songs and exercises.


It's important to avoid rushing the learning process.  Focus on building a solid foundation by mastering the fundamentals before moving on to more complex techniques.


Your Request (type '(end)' on a new line to submit, or '/new', 'exit', 'quit'):
I need a detailed technical report on the architecture of Google's new Gemma models.
(end)

> Thinking (Step 1/3: Choosing a tool)...
> LLM decided to call 'conduct_deep_research' with arguments: {'query': "I need a detailed technical report on the architecture of Google's new Gemma models."}

Executing tool on MCP server...

--- ✅ Tool Execution Complete ---
{
  "status": "success",
  "report": "# Technical Report: Architecture of Google's Gemma Models\n\n## Introduction\n\nThe Gemma family of models represents Google's foray into lightweight, open-source large language models (LLMs).  Developed using the same research and technology underlying the Gemini models, Gemma aims to provide performance and versatility while prioritizing efficient inference and responsible AI development. This report synthesizes available technical details on the Gemma architecture, training, and performance, focusing on the information currently accessible.\n\n## Architecture\n\nGemma models follow a decoder-only transformer architecture, a common design choice in modern LLMs. This architecture relies heavily on the self-attention mechanism, eliminating the recurrent networks that previously dominated sequence transduction models.  Self-attention allows the model to weigh the importance of different parts of the input sequence when generating output, enabling parallel processing and capturing long-range dependencies within the text.  Like the original Transformer model, Gemma utilizes stacked self-attention and point-wise, fully connected layers within its decoder.\n\nSpecific architectural details are available for Gemma 2B and 7B.  The 7B model has been described as having 9.24 billion parameters, while the 2B model is correspondingly smaller.  Further analysis of Gemma 7B reveals insights into the internal workings of its attention heads.  Some heads exhibit specialized behavior, such as the \"apostrophe head,\" which focuses attention on tokens following an apostrophe.  Other heads, particularly in the first and last layers, demonstrate diagonal attention patterns, effectively acting as residual connections.  This suggests a complex interplay of specialized and general-purpose attention mechanisms within the model.\n\n## Training and Infrastructure\n\nGemma models are trained primarily on English-language data, encompassing a diverse corpus of web documents, code samples, and mathematical content.  This broad training dataset aims to equip Gemma with capabilities for various text generation tasks, including question answering, summarization, and reasoning.  Gemma 2 9B was trained using the TPUv5p hardware platform, leveraging its high parallelism and memory efficiency for large-scale machine learning. The software stack involves JAX for training.  Gemma 7B was trained on a dataset comprising approximately 6 trillion tokens.\n\n## Performance and Efficiency\n\nPerformance analysis of Gemma models has focused on training and inference efficiency on Google Cloud accelerators.  Studies have shown high model flops utilization, particularly for Gemma 2B and 7B on TPU v5e-256 and v5p-128 hardware.  The MaxText reference implementation has been instrumental in achieving high-performance training.  Further optimizations are expected through community contributions and ongoing development.\n\n## Comparison with Other LLMs\n\nWhile direct comparisons with other LLMs like PaLM 2, LaMDA, and GPT models are limited in the available context, the architectural similarities with the Gemini family are noteworthy.  Gemma inherits advancements in scalable AI training infrastructure and safety practices from Gemini.  Furthermore, the decoder-only transformer architecture aligns Gemma with prevalent trends in contemporary LLMs, suggesting a convergence towards this design for text generation tasks.\n\n## Conclusion\n\nGemma represents a significant step towards accessible and efficient LLMs.  The decoder-only transformer architecture, coupled with training on a massive and diverse dataset, positions Gemma for a wide range of text generation tasks.  While specific details on certain aspects of the architecture and comparative performance are still emerging, the available information highlights Google's focus on efficiency, responsible development, and community engagement in advancing LLM technology.  Further research and analysis will be crucial for a more comprehensive understanding of Gemma's capabilities and its place within the broader LLM landscape.\n\n\n## Sources\n1. [[PDF] Attention is All you Need - NIPS](https://papers.neurips.cc/paper/7181-attention-is-all-you-need.pdf) - Date not available\n2. [GitHub - jadore801120/attention-is-all-you-need-pytorch](https://github.com/jadore801120/attention-is-all-you-need-pytorch) - Date not available\n3. [yanqi1811/attention-is-all-you-need - GitHub](https://github.com/yanqi1811/attention-is-all-you-need) - Date not available\n4. [Attention Is All You Need | PPT - SlideShare](https://www.slideshare.net/slideshow/attention-is-all-you-need-232995827/232995827) - Date not available\n5. [[PDF] arXiv:1706.03762v7 [cs.CL] 2 Aug 2023](http://arxiv.org/pdf/1706.03762) - Date not available\n6. [NeurIPS 2024 Thursday 12/12](https://neurips.cc/virtual/2024/day/12/12) - Date not available\n7. [How cuDNN 9 supports scaled dot product attention - LinkedIn](https://www.linkedin.com/posts/techsachinkumar_generativeai-activity-7201336623282749441-kmO7) - Date not available\n8. [A Deep Learning Model Based on Bidirectional Temporal ... - MDPI](https://www.mdpi.com/2076-3417/15/6/2984) - Date not available\n9. [ICLR 2025 Saturday 04/26](https://iclr.cc/virtual/2025/day/4/26) - Date not available\n10. [machine-learning-articles/easy-text-summarization-with ... - GitHub](https://github.com/christianversloot/machine-learning-articles/blob/main/easy-text-summarization-with-huggingface-transformers-and-machine-learning.md) - Date not available\n11. [Encoder-Decoder Seq2Seq Models, Clearly Explained!! - Medium](https://medium.com/analytics-vidhya/encoder-decoder-seq2seq-models-clearly-explained-c34186fbf49b) - Date not available\n12. [How to Develop an Encoder-Decoder Model for Sequence-to ...](https://www.machinelearningmastery.com/develop-encoder-decoder-model-sequence-sequence-prediction-keras/) - Date not available\n13. [Text Summarization from scratch using Encoder-Decoder network ...](https://medium.com/towards-data-science/text-summarization-from-scratch-using-encoder-decoder-network-with-attention-in-keras-5fa80d12710e) - Date not available\n14. [The Transformer Model - MachineLearningMastery.com](https://machinelearningmastery.com/the-transformer-model/) - Date not available\n15. [Understanding Transformer Performance with Associative ...](https://arxiv.org/html/2405.08707v1) - Date not available\n16. [Comprehensive Analysis of Transparency and Accessibility ...](https://arxiv.org/html/2502.18505v1) - Date not available\n17. [Comprehensive Analysis of Transparency and Accessibility ...](https://arxiv.org/pdf/2502.18505) - Date not available\n18. [TRAINING LANGUAGE MODELS WITH PAUSE TOKENS](https://openreview.net/pdf?id=ph04CRkPdC) - Date not available\n19. [Moshi: a speech-text foundation model for real-time dialogue](https://arxiv.org/html/2410.00037v1) - Date not available\n20. [Gemma 2 9B | Open Laboratory](https://openlaboratory.ai/models/gemma-2-9b) - Date not available\n21. [In-depth analysis of Gemma model variants: technological ... - AI\u4e91\u539f\u751f](https://aicloud.mygptmeta.com/en/archives/1365/) - Date not available\n22. [The Dispatch Report: The Dispatch Demo - ggerganov/llama.cpp](https://thedispatch.ai/reports/380/) - Date not available\n23. [[PDF] Operationalizing Generative AI on Vertex AI - Google Services](https://services.google.com/fh/files/misc/operationalizing_generative_ai_on_vertex_ai.pdf) - Date not available\n24. [[PDF] Orange Pi CM5 Base Base board + Core board User Manual](https://orangepi.net/wp-content/uploads/2025/01/OrangePi_CM5_Base_RK3588S_user-manual_v1.3.pdf) - Date not available\n25. [google/gemma-7b - Hugging Face](https://huggingface.co/google/gemma-7b) - Date not available\n26. [google/gemma-2b-it-pytorch - Hugging Face](https://huggingface.co/google/gemma-2b-it-pytorch) - Date not available\n27. [anish2105/Google-Gemma - GitHub](https://github.com/anish2105/Google-Gemma) - Date not available\n28. [MedGemma model card | Health AI Developer Foundations](https://developers.google.com/health-ai-developer-foundations/medgemma/model-card) - Date not available\n29. [Use Gemma open models | Generative AI on Vertex AI - Google Cloud](https://cloud.google.com/vertex-ai/generative-ai/docs/open-models/use-gemma) - Date not available\n30. [Round and Round We Go! What makes Rotary Positional ...](https://arxiv.org/html/2410.06205v1) - Date not available\n31. [Unifying Attention Heads and Task Vectors via Hidden ...](https://arxiv.org/html/2505.18752v1) - Date not available\n32. [ROUND AND ROUND WE GO! L W HAT MAKES ROTARY ...](https://openreview.net/pdf?id=GtvuNrk58a) - Date not available\n33. [Company: deepseek-ai | AINews](https://news.smol.ai/tags/deepseek-ai/) - Date not available\n34. [Performance deep dive of Gemma on Google Cloud](https://cloud.google.com/blog/products/ai-machine-learning/performance-deepdive-of-gemma-on-google-cloud) - Date not available\n35. [Alpaca-Powered Gemma 2B: PEFT with LoRA - Kaggle](https://www.kaggle.com/code/umerhaddii/alpaca-powered-gemma-2b-peft-with-lora) - Date not available\n36. [Alpaca-Powered Gemma2-2B: PEFT with LoRA - Kaggle](https://www.kaggle.com/code/umerhaddii/alpaca-powered-gemma2-2b-peft-with-lora) - Date not available\n37. [a Case Study on the Subject-verb Agreement Task - arXiv](https://arxiv.org/html/2410.06496v1) - Date not available\n38. [Gemma 7B - Open Laboratory](https://openlaboratory.ai/models/gemma-7b) - Date not available\n39. [Gemma - Google - Kaggle](https://www.kaggle.com/models/google/gemma/frameworks/pyTorch) - Date not available\n40. [How to set up a large language model (Google Gemma) while ...](https://community.boomi.com/s/article/How-to-set-up-a-large-language-model-Google-Gemma-while-keeping-control-of-your-data) - Date not available\n41. [Introducing PaliGemma 2 mix: A vision-language model for multiple ...](https://developers.googleblog.com/en/introducing-paligemma-2-mix/) - Date not available\n42. [Mixture of A Million Experts](https://arxiv.org/html/2407.04153v1) - Date not available\n43. [Mixture of A Million Experts](https://arxiv.org/pdf/2407.04153) - Date not available\n44. [Dense Backpropagation Improves Training for Sparse ...](https://arxiv.org/html/2504.12463v1) - Date not available\n45. [Unified Scaling Laws for Routed Language Models](https://arxiv.org/pdf/2202.01169) - Date not available\n46. [Monet: Mixture of Monosemantic Experts for Transformers](https://arxiv.org/html/2412.04139v1) - Date not available\n47. [[PDF] Open Mixture-of-Experts Language Models - OLMoE - Kyle Lo](https://kyleclo.com/assets/pdf/olmoe-open-mixture-of-experts-language-models.pdf) - Date not available\n48. [Monet: Mixture of Monosemantic Experts for Transformers Explained](https://www.lesswrong.com/posts/2obqKZiCXCicFCaSs/monet-mixture-of-monosemantic-experts-for-transformers) - Date not available\n49. [Stealing User Prompts from Mixture-of-Experts Models - OpenReview](https://openreview.net/forum?id=1RNSYEEpwi) - Date not available\n50. [Mixture of Diverse Size Experts - arXiv](https://arxiv.org/html/2409.12210v1) - Date not available\n51. [Noam Shazeer - CatalyzeX](https://www.catalyzex.com/author/Noam%20Shazeer) - Date not available\n52. [moe \u00b7 GitHub Topics](https://github.com/topics/moe) - Date not available\n53. [mixture-of-experts \u00b7 GitHub Topics](https://github.com/topics/mixture-of-experts) - Date not available\n54. [[PDF] Dense Backpropagation Improves Training for Sparse Mixture-of ...](https://arxiv.org/pdf/2504.12463) - Date not available\n55. [Pangu Pro MoE: Mixture of Grouped Experts for Efficient ...](https://arxiv.org/html/2505.21411v1) - Date not available\n56. [Mixture of Experts for Implicit Neural Representations](https://arxiv.org/html/2410.21643v1) - Date not available\n57. [MoETuner: Optimized Mixture of Expert Serving with ...](https://arxiv.org/html/2502.06643v1) - Date not available\n58. [Noam Shazeer's research works | Google Inc. and other ...](https://www.researchgate.net/scientific-contributions/Noam-Shazeer-2059717778) - Date not available\n59. [google-gemini/gemma-cookbook](https://github.com/google-gemini/gemma-cookbook/blob/main/Gemma/Using_Gemma_with_LLM_Comparator.ipynb) - Date not available\n60. [scaling-book/training.md at main](https://github.com/jax-ml/scaling-book/blob/main/training.md) - Date not available\n61. [gpustack/gpustack/assets/model-catalog.yaml at main](https://github.com/gpustack/gpustack/blob/main/gpustack/assets/model-catalog.yaml) - Date not available\n62. [EnnengYang/Awesome-Model-Merging-Methods-Theories ...](https://github.com/EnnengYang/Awesome-Model-Merging-Methods-Theories-Applications) - Date not available\n63. [Awesome-LLM: a curated list of Large Language Model](https://github.com/Hannibal046/Awesome-LLM) - Date not available\n"
}
> Direct answer found in tool result. Presenting directly.

LLM Response: # Technical Report: Architecture of Google's Gemma Models

## Introduction

The Gemma family of models represents Google's foray into lightweight, open-source large language models (LLMs).  Developed using the same research and technology underlying the Gemini models, Gemma aims to provide performance and versatility while prioritizing efficient inference and responsible AI development. This report synthesizes available technical details on the Gemma architecture, training, and performance, focusing on the information currently accessible.

## Architecture

Gemma models follow a decoder-only transformer architecture, a common design choice in modern LLMs. This architecture relies heavily on the self-attention mechanism, eliminating the recurrent networks that previously dominated sequence transduction models.  Self-attention allows the model to weigh the importance of different parts of the input sequence when generating output, enabling parallel processing and capturing long-range dependencies within the text.  Like the original Transformer model, Gemma utilizes stacked self-attention and point-wise, fully connected layers within its decoder.

Specific architectural details are available for Gemma 2B and 7B.  The 7B model has been described as having 9.24 billion parameters, while the 2B model is correspondingly smaller.  Further analysis of Gemma 7B reveals insights into the internal workings of its attention heads.  Some heads exhibit specialized behavior, such as the "apostrophe head," which focuses attention on tokens following an apostrophe.  Other heads, particularly in the first and last layers, demonstrate diagonal attention patterns, effectively acting as residual connections.  This suggests a complex interplay of specialized and general-purpose attention mechanisms within the model.

## Training and Infrastructure

Gemma models are trained primarily on English-language data, encompassing a diverse corpus of web documents, code samples, and mathematical content.  This broad training dataset aims to equip Gemma with capabilities for various text generation tasks, including question answering, summarization, and reasoning.  Gemma 2 9B was trained using the TPUv5p hardware platform, leveraging its high parallelism and memory efficiency for large-scale machine learning. The software stack involves JAX for training.  Gemma 7B was trained on a dataset comprising approximately 6 trillion tokens.

## Performance and Efficiency

Performance analysis of Gemma models has focused on training and inference efficiency on Google Cloud accelerators.  Studies have shown high model flops utilization, particularly for Gemma 2B and 7B on TPU v5e-256 and v5p-128 hardware.  The MaxText reference implementation has been instrumental in achieving high-performance training.  Further optimizations are expected through community contributions and ongoing development.

## Comparison with Other LLMs

While direct comparisons with other LLMs like PaLM 2, LaMDA, and GPT models are limited in the available context, the architectural similarities with the Gemini family are noteworthy.  Gemma inherits advancements in scalable AI training infrastructure and safety practices from Gemini.  Furthermore, the decoder-only transformer architecture aligns Gemma with prevalent trends in contemporary LLMs, suggesting a convergence towards this design for text generation tasks.

## Conclusion

Gemma represents a significant step towards accessible and efficient LLMs.  The decoder-only transformer architecture, coupled with training on a massive and diverse dataset, positions Gemma for a wide range of text generation tasks.  While specific details on certain aspects of the architecture and comparative performance are still emerging, the available information highlights Google's focus on efficiency, responsible development, and community engagement in advancing LLM technology.  Further research and analysis will be crucial for a more comprehensive understanding of Gemma's capabilities and its place within the broader LLM landscape.


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11. [Encoder-Decoder Seq2Seq Models, Clearly Explained!! - Medium](https://medium.com/analytics-vidhya/encoder-decoder-seq2seq-models-clearly-explained-c34186fbf49b) - Date not available
12. [How to Develop an Encoder-Decoder Model for Sequence-to ...](https://www.machinelearningmastery.com/develop-encoder-decoder-model-sequence-sequence-prediction-keras/) - Date not available
13. [Text Summarization from scratch using Encoder-Decoder network ...](https://medium.com/towards-data-science/text-summarization-from-scratch-using-encoder-decoder-network-with-attention-in-keras-5fa80d12710e) - Date not available
14. [The Transformer Model - MachineLearningMastery.com](https://machinelearningmastery.com/the-transformer-model/) - Date not available
15. [Understanding Transformer Performance with Associative ...](https://arxiv.org/html/2405.08707v1) - Date not available
16. [Comprehensive Analysis of Transparency and Accessibility ...](https://arxiv.org/html/2502.18505v1) - Date not available
17. [Comprehensive Analysis of Transparency and Accessibility ...](https://arxiv.org/pdf/2502.18505) - Date not available
18. [TRAINING LANGUAGE MODELS WITH PAUSE TOKENS](https://openreview.net/pdf?id=ph04CRkPdC) - Date not available
19. [Moshi: a speech-text foundation model for real-time dialogue](https://arxiv.org/html/2410.00037v1) - Date not available
20. [Gemma 2 9B | Open Laboratory](https://openlaboratory.ai/models/gemma-2-9b) - Date not available
21. [In-depth analysis of Gemma model variants: technological ... - AI云原生](https://aicloud.mygptmeta.com/en/archives/1365/) - Date not available
22. [The Dispatch Report: The Dispatch Demo - ggerganov/llama.cpp](https://thedispatch.ai/reports/380/) - Date not available
23. [[PDF] Operationalizing Generative AI on Vertex AI - Google Services](https://services.google.com/fh/files/misc/operationalizing_generative_ai_on_vertex_ai.pdf) - Date not available
24. [[PDF] Orange Pi CM5 Base Base board + Core board User Manual](https://orangepi.net/wp-content/uploads/2025/01/OrangePi_CM5_Base_RK3588S_user-manual_v1.3.pdf) - Date not available
25. [google/gemma-7b - Hugging Face](https://huggingface.co/google/gemma-7b) - Date not available
26. [google/gemma-2b-it-pytorch - Hugging Face](https://huggingface.co/google/gemma-2b-it-pytorch) - Date not available
27. [anish2105/Google-Gemma - GitHub](https://github.com/anish2105/Google-Gemma) - Date not available
28. [MedGemma model card | Health AI Developer Foundations](https://developers.google.com/health-ai-developer-foundations/medgemma/model-card) - Date not available
29. [Use Gemma open models | Generative AI on Vertex AI - Google Cloud](https://cloud.google.com/vertex-ai/generative-ai/docs/open-models/use-gemma) - Date not available
30. [Round and Round We Go! What makes Rotary Positional ...](https://arxiv.org/html/2410.06205v1) - Date not available
31. [Unifying Attention Heads and Task Vectors via Hidden ...](https://arxiv.org/html/2505.18752v1) - Date not available
32. [ROUND AND ROUND WE GO! L W HAT MAKES ROTARY ...](https://openreview.net/pdf?id=GtvuNrk58a) - Date not available
33. [Company: deepseek-ai | AINews](https://news.smol.ai/tags/deepseek-ai/) - Date not available
34. [Performance deep dive of Gemma on Google Cloud](https://cloud.google.com/blog/products/ai-machine-learning/performance-deepdive-of-gemma-on-google-cloud) - Date not available
35. [Alpaca-Powered Gemma 2B: PEFT with LoRA - Kaggle](https://www.kaggle.com/code/umerhaddii/alpaca-powered-gemma-2b-peft-with-lora) - Date not available
36. [Alpaca-Powered Gemma2-2B: PEFT with LoRA - Kaggle](https://www.kaggle.com/code/umerhaddii/alpaca-powered-gemma2-2b-peft-with-lora) - Date not available
37. [a Case Study on the Subject-verb Agreement Task - arXiv](https://arxiv.org/html/2410.06496v1) - Date not available
38. [Gemma 7B - Open Laboratory](https://openlaboratory.ai/models/gemma-7b) - Date not available
39. [Gemma - Google - Kaggle](https://www.kaggle.com/models/google/gemma/frameworks/pyTorch) - Date not available
40. [How to set up a large language model (Google Gemma) while ...](https://community.boomi.com/s/article/How-to-set-up-a-large-language-model-Google-Gemma-while-keeping-control-of-your-data) - Date not available
41. [Introducing PaliGemma 2 mix: A vision-language model for multiple ...](https://developers.googleblog.com/en/introducing-paligemma-2-mix/) - Date not available
42. [Mixture of A Million Experts](https://arxiv.org/html/2407.04153v1) - Date not available
43. [Mixture of A Million Experts](https://arxiv.org/pdf/2407.04153) - Date not available
44. [Dense Backpropagation Improves Training for Sparse ...](https://arxiv.org/html/2504.12463v1) - Date not available
45. [Unified Scaling Laws for Routed Language Models](https://arxiv.org/pdf/2202.01169) - Date not available
46. [Monet: Mixture of Monosemantic Experts for Transformers](https://arxiv.org/html/2412.04139v1) - Date not available
47. [[PDF] Open Mixture-of-Experts Language Models - OLMoE - Kyle Lo](https://kyleclo.com/assets/pdf/olmoe-open-mixture-of-experts-language-models.pdf) - Date not available      
48. [Monet: Mixture of Monosemantic Experts for Transformers Explained](https://www.lesswrong.com/posts/2obqKZiCXCicFCaSs/monet-mixture-of-monosemantic-experts-for-transformers) - Date not available
49. [Stealing User Prompts from Mixture-of-Experts Models - OpenReview](https://openreview.net/forum?id=1RNSYEEpwi) - Date not available
50. [Mixture of Diverse Size Experts - arXiv](https://arxiv.org/html/2409.12210v1) - Date not available
51. [Noam Shazeer - CatalyzeX](https://www.catalyzex.com/author/Noam%20Shazeer) - Date not available
52. [moe · GitHub Topics](https://github.com/topics/moe) - Date not available
53. [mixture-of-experts · GitHub Topics](https://github.com/topics/mixture-of-experts) - Date not available
54. [[PDF] Dense Backpropagation Improves Training for Sparse Mixture-of ...](https://arxiv.org/pdf/2504.12463) - Date not available
55. [Pangu Pro MoE: Mixture of Grouped Experts for Efficient ...](https://arxiv.org/html/2505.21411v1) - Date not available
56. [Mixture of Experts for Implicit Neural Representations](https://arxiv.org/html/2410.21643v1) - Date not available
57. [MoETuner: Optimized Mixture of Expert Serving with ...](https://arxiv.org/html/2502.06643v1) - Date not available
58. [Noam Shazeer's research works | Google Inc. and other ...](https://www.researchgate.net/scientific-contributions/Noam-Shazeer-2059717778) - Date not available
59. [google-gemini/gemma-cookbook](https://github.com/google-gemini/gemma-cookbook/blob/main/Gemma/Using_Gemma_with_LLM_Comparator.ipynb) - Date not available
60. [scaling-book/training.md at main](https://github.com/jax-ml/scaling-book/blob/main/training.md) - Date not available
61. [gpustack/gpustack/assets/model-catalog.yaml at main](https://github.com/gpustack/gpustack/blob/main/gpustack/assets/model-catalog.yaml) - Date not available
62. [EnnengYang/Awesome-Model-Merging-Methods-Theories ...](https://github.com/EnnengYang/Awesome-Model-Merging-Methods-Theories-Applications) - Date not available
63. [Awesome-LLM: a curated list of Large Language Model](https://github.com/Hannibal046/Awesome-LLM) - Date not available


Your Request (type '(end)' on a new line to submit, or '/new', 'exit', 'quit'):
Summarize this into 5 bullet points for me:
She had not, and as the officer suspected, somebody else had applied to Arizona community colleges in her name to scam the government into paying out financial aid money.

When she checked her student loan servicer account, Brady saw the scammers had not stopped there. A loan for over US$9,000 had been paid out in her name — but to another person —
 for coursework at a California college.

"I just can’t imagine how many people this is happening to that have no idea," Brady said.

The rise of artificial intelligence and the popularity of online classes have led to an explosion of financial aid fraud. Fake college enrollments have been surging as crime rings deploy "ghost students" — chatbots that join online classrooms and stay just long enough to collect a financial aid check.

In some cases, professors discover almost no one in their class is real. Students get locked out of the classes they need to graduate as bots push courses over their enrollment limits. And victims of identity theft who discover loans fraudulently taken out in their names must go through months of calling colleges, the Federal Student Aid office and loan servicers to try to get the debt erased.

On Friday, the U.S. Education Department introduced a temporary rule requiring students to show colleges a government-issued ID to prove their identity. It will apply only to first-time applicants for federal student aid for the summer term, affecting some 125,000 borrowers. The agency said it is developing more advanced screening for the fall.

"The rate of fraud through stolen identities has reached a level that imperils the federal student aid program," the department said in its guidance to colleges.

Public colleges have lost millions of dollars to fraud

An Associated Press analysis of fraud reports obtained through a public records request shows California colleges in 2024 reported 1.2 million fraudulent applications, which resulted in 223,000 suspected fake enrollments. Other states are affected by the same problem, but with 116 community colleges, California is a particularly large target.

Criminals stole at least $11.1 million in federal, state and local financial aid from California community colleges last year that could not be recovered, according to the reports.

Colleges typically receive a portion of the loans intended for tuition, with the balance going directly to students for other expenses. Community colleges are targeted in part because their lower tuition means larger percentages of grants and loans go to borrowers.

Scammers frequently use AI chatbots to carry out the fraud, targeting courses that are online and allow students to watch lectures and complete coursework on their own time.

In January, Wayne Chaw started getting emails about a class he never signed up for at De Anza Community College, where he had taken coding classes a decade earlier. Identity thieves had obtained his Social Security number and collected $1,395 in financial aid in his name.

The energy management class required students to submit a homework assignment to prove they were real. But someone wrote submissions impersonating Chaw, likely using a chatbot.

"This person is typing as me, saying my first and last name. ... It’s very freaky when I saw that," said Chaw.

The fraud involved a grant, not loans, so Chaw himself did not lose money. He called the Social Security Administration to report the identity theft, but after five hours on hold, he never got through to a person.

As the Trump administration moves to dismantle the Education Department, federal cuts may make it harder to catch criminals and help victims of identity theft. In March, the Trump administration fired more than 300 people from the Federal Student Aid office, and the department’s Office of Inspector General, which investigates fraud, has lost more than 20% of its staff through attrition and retirements since October.

"I’m just nervous that I’m going to be stuck with this," Brady said. "The agency is going to be so broken down and disintegrated that I won’t be able to do anything, and I’m just going to be stuck with those $9,000" in loans.

Criminal cases around the country offer a glimpse of the schemes’ pervasiveness.

In the past year, investigators indicted a man accused of leading a Texas fraud ring that used stolen identities to pursue $1.5 million in student aid. Another person in Texas pleaded guilty to using the names of prison inmates to apply for over $650,000 in student aid at colleges across the South and Southwest. And a person in New York recently pleaded guilty to a $450,000 student aid scam that lasted a decade.

Identify fraud victims who never attended college are hit with student debt

Brittnee Nelson of Shreveport, Louisiana, was bringing her daughter to day care two years ago when she received a notification that her credit score had dropped 27 points.       

Loans had been taken out in her name for colleges in California and Louisiana, she discovered. She canceled one before it was paid out, but it was too late to stop a loan of over $5,000 for Delgado Community College in New Orleans.

Nelson runs her own housecleaning business and did not go to college. She already was signed up for identity theft protection and carefully monitored her credit. Still, her debt almost went into collections before the loan was put in forbearance. She recently got the loans taken off her record after two years of effort.

"It’s like if someone came into your house and robbed you," she said.

The federal government’s efforts to verify borrowers’ identity could help, she said.

"If they can make these hurdles a little bit harder and have these verifications more provable, I think that’s really, really, really going to protect people in the long run," she said.

Delgado spokesperson Barbara Waiters said responsibility for approving loans ultimately lies with federal agencies.

"This is an unfortunate and serious matter, but it is not the direct or indirect result of Delgado’s internal processes," Waiters said.

In San Francisco, the loans taken out in Brady’s name are in a grace period, but still on the books. That has not been her only challenge. A few months ago, she was laid off from her job and decided to sign up for a class at City College San Francisco to help her career. But all the classes were full.

After a few weeks, Brady finally was able to sign up for a class. The professor apologized for the delay in spots opening up: The college has been struggling with fraudulent applications.

Heather Brady was napping at home in San Francisco on a Sunday afternoon when the officer knocked on her door to ask an unusual question: Had she applied to Arizona Western College?
(end)

> Thinking (Step 1/3: Choosing a tool)...
> LLM decided to call 'summarize_text' with arguments: {'text_to_summarize': 'She had not, and as the officer suspected, somebody else had applied to Arizona community colleges in her name to scam the government into paying out financial aid money.\n\nWhen she checked her student loan servicer account, Brady saw the scammers had not stopped there. A loan for over US$9,000 had been paid out in her name — but to another person — for coursework at a California college.\n\n"I just can’t imagine how many people this is happening to that have no idea," Brady said.\n\nThe rise of artificial intelligence and the popularity of online classes have led to an explosion of financial aid fraud. Fake college enrollments have been surging as crime rings deploy "ghost students" — chatbots that join online classrooms and stay just long enough to collect a financial aid check.\n\nIn some cases, professors discover almost no one in their class is real. Students get locked out of the classes they need to graduate as bots push courses over their enrollment limits. And victims of identity theft who discover loans fraudulently taken out in their names must go through months of calling colleges, the Federal Student Aid office and loan servicers to try to get the debt erased.\n\nOn Friday, the U.S. Education Department introduced a temporary rule requiring students to show colleges a government-issued ID to prove their identity. It will apply only to first-time applicants for federal student aid for the summer term, affecting some 125,000 borrowers. The agency said it is developing more advanced screening for the fall.\n\n"The rate of fraud through stolen identities has reached a level that imperils the federal student aid program," the department said in its guidance to colleges.\n\nPublic colleges have lost millions of dollars to fraud\n\nAn Associated Press analysis of fraud reports obtained through a public records request shows California colleges in 2024 reported 1.2 million fraudulent applications, which resulted in 223,000 suspected fake enrollments. Other states are affected by the same problem, but with 116 community colleges, California is a particularly large target.\n\nCriminals stole at least $11.1 million in federal, state and local financial aid from California community colleges last year that could not be recovered, according to the reports.\n\nColleges typically receive a portion of the loans intended for tuition, with the balance going directly to students for other expenses. Community colleges are targeted in part because their lower tuition means larger percentages of grants and loans go to borrowers.\n\nScammers frequently use AI chatbots to carry out the fraud, targeting courses that are online and allow students to watch lectures and complete coursework on their own time.\n\nIn January, Wayne Chaw started getting emails about a class he never signed up for at De Anza Community College, where he had taken coding classes a decade earlier. Identity thieves had obtained his Social Security number and collected $1,395 in financial aid in his name.\n\nThe energy management class required students to submit a homework assignment to prove they were real. But someone wrote submissions impersonating Chaw, likely using a chatbot.\n\n"This person is typing as me, saying my first and last name. ... It’s very freaky when I saw that," said Chaw.\n\nThe fraud involved a grant, not loans, so Chaw himself did not lose money. He called the Social Security Administration to report the identity theft, but after five hours on hold, he never got through to a person.\n\nAs the Trump administration moves to dismantle the Education Department, federal cuts may make it harder to catch criminals and help victims of identity theft. In March, the Trump administration fired more than 300 people from the Federal Student Aid office, and the department’s Office of Inspector General, which investigates fraud, has lost more than 20% of its staff through attrition and retirements since October.\n\n"I’m just nervous that I’m going to be stuck with this," Brady said. "The agency is going to be so broken down and disintegrated that I won’t be able to do anything, and I’m just going to be stuck with those $9,000" in loans.\n\nCriminal cases around the country offer a glimpse of the schemes’ pervasiveness.\n\nIn the past year, investigators indicted a man accused of leading a Texas fraud ring that used stolen identities to pursue $1.5 million in student aid. Another person in Texas pleaded guilty to using the names of prison inmates to apply for over $650,000 in student aid at colleges across the South and Southwest. And a person in New York recently pleaded guilty to a $450,000 student aid scam that lasted a decade.\n\nIdentify fraud victims who never attended college are hit with student debt\n\nBrittnee Nelson of Shreveport, Louisiana, was bringing her daughter to day care two years ago when she received a notification that her credit score had dropped 27 points.\n\nLoans had been taken out in her name for colleges in California and Louisiana, she discovered. She canceled one before it was paid out, but it was too late to stop a loan of over $5,000 for Delgado Community College in New Orleans.\n\nNelson runs her own housecleaning business and did not go to college. She already was signed up for identity theft protection and carefully monitored her credit. Still, her debt almost went into collections before the loan was put in forbearance. She recently got the loans taken off her record after two years of effort.\n\n"It’s like if someone came into your house and robbed you," she said.\n\nThe federal government’s efforts to verify borrowers’ identity could help, she said.\n\n"If they can make these hurdles a little bit harder and have these verifications more provable, I think that’s really, really, really going to protect people in the long run," she said.\n\nDelgado spokesperson Barbara Waiters said responsibility for approving loans ultimately lies with federal agencies.\n\n"This is an unfortunate and serious matter, but it is not the direct or indirect result of Delgado’s internal processes," Waiters said.\n\nIn San Francisco, the loans taken out in Brady’s name are in a grace period, but still on the books. That has not been her only challenge. A few months ago, she was laid off from her job and decided to sign up for a class at City College San Francisco to help her career. But all the classes were full.\n\nAfter a few weeks, Brady finally was able to sign up for a class. The professor apologized for the delay in spots opening up: The college has been struggling with fraudulent applications.\n\nHeather Brady was napping at home in San Francisco on a Sunday afternoon when the officer knocked on her door to ask an unusual question: Had she applied to Arizona Western College?', 'format': 'bullet points'}

Executing tool on MCP server...

--- ✅ Tool Execution Complete ---
{
  "status": "success",
  "summary": "* A woman named Heather Brady was the victim of identity theft, with someone taking out student loans in her name at colleges in Arizona and California.\n* This type of financial aid fraud is increasing, fueled by AI and online classes.  \"Ghost students\" (chatbots) are used to enroll in classes and obtain financial aid.\n* The fraud has various consequences:  Professors finding classes full of bots, legitimate students locked out of courses, and victims facing lengthy processes to clear fraudulent debt.\n* California community colleges reported 1.2 million fraudulent applications and $11.1 million in unrecoverable losses in 2024.\n* Community colleges are often targeted due to lower tuition costs, resulting in higher proportions of financial aid going directly to students.\n* AI chatbots are frequently used by scammers, particularly in online, self-paced courses.\n* Wayne Chaw's identity was also stolen, with someone using a chatbot to complete assignments in his name.\n* The U.S. Education Department introduced a temporary rule requiring government-issued ID for first-time aid applicants.\n* Budget cuts and staff reductions in the Federal Student Aid office and the Office of Inspector General may hinder fraud investigation and victim support.\n* Brittnee Nelson, who never attended college, had loans fraudulently taken out in her name and faced a two-year struggle to clear the debt.\n* Several criminal cases across the country highlight the widespread nature of these schemes.\n*  Brady's attempts to enroll in a class at City College of San Francisco were hampered by fraudulent enrollments filling up classes.\n"
}
> Direct answer found in tool result. Presenting directly.

LLM Response: * A woman named Heather Brady was the victim of identity theft, with someone taking out student loans in her name at colleges in Arizona and California.
* This type of financial aid fraud is increasing, fueled by AI and online classes.  "Ghost students" (chatbots) are used to enroll in classes and obtain financial aid.
* The fraud has various consequences:  Professors finding classes full of bots, legitimate students locked out of courses, and victims facing lengthy processes to clear fraudulent debt.
* California community colleges reported 1.2 million fraudulent applications and $11.1 million in unrecoverable losses in 2024.
* Community colleges are often targeted due to lower tuition costs, resulting in higher proportions of financial aid going directly to students.
* AI chatbots are frequently used by scammers, particularly in online, self-paced courses.
* Wayne Chaw's identity was also stolen, with someone using a chatbot to complete assignments in his name.
* The U.S. Education Department introduced a temporary rule requiring government-issued ID for first-time aid applicants.
* Budget cuts and staff reductions in the Federal Student Aid office and the Office of Inspector General may hinder fraud investigation and victim support.
* Brittnee Nelson, who never attended college, had loans fraudulently taken out in her name and faced a two-year struggle to clear the debt.
* Several criminal cases across the country highlight the widespread nature of these schemes.
*  Brady's attempts to enroll in a class at City College of San Francisco were hampered by fraudulent enrollments filling up classes.


Your Request (type '(end)' on a new line to submit, or '/new', 'exit', 'quit'):
Can you explain this Python code to me like I'm a project manager?
def get_user_data(user_id):
    # Fetches user data from a database
    db = connect_to_database()
    user_record = db.query("SELECT * FROM users WHERE id = ?", user_id)
    return user_record
(end)

> Thinking (Step 1/3: Choosing a tool)...
> LLM decided to call 'explain_code' with arguments: {'code_snippet': 'def get_user_data(user_id):\n    # Fetches user data from a database\n    db = connect_to_database()\n    user_record = db.query("SELECT * FROM users WHERE id = ?", user_id)\n    return user_record', 'programming_language': 'Python'}

Executing tool on MCP server...

--- ✅ Tool Execution Complete ---
{
  "status": "success",
  "explanation": "## \ud83c\udfaf Purpose\n\nThis code retrieves a specific user's information from a database.\n\n## \ud83d\uddc4\ufe0f Analogies\n\nThink of this code as a librarian fetching a specific book from a library.  The `user_id` is like the book's unique identification number (ISBN), and the database is like the library's collection of books. The code searches the library (database) using the ISBN (`user_id`) and retrieves the corresponding book (user record).\n\n## \ud83d\udce5 Inputs & \ud83d\udce4 Outputs\n\n- **Inputs:** \n    - `user_id`: A unique identifier for the user whose data needs to be retrieved.  This is like the ISBN of the book we want to find.\n- **Outputs:**\n    - `user_record`:  The complete set of information stored about the specified user in the database. This is like the book itself, containing all the information about it.\n\n## \u26a0\ufe0f Potential Risks & Questions\n\n1. **Data Security:** Is this code accessing sensitive user data? Are there appropriate security measures in place to protect this data from unauthorized access or breaches?  How is the database connection secured?\n2. **Error Handling:** What happens if the provided `user_id` doesn't exist in the database?  Does the code handle this gracefully, or will it crash? How are errors logged and reported?\n3. **Database Performance:**  How efficient is this database query? If we have a large number of users, will this code perform quickly enough, or could it become a bottleneck?  Are there any indexes on the `id` column in the `users` table to speed up the query?\n"
}
> Direct answer found in tool result. Presenting directly.

LLM Response: ## 🎯 Purpose

This code retrieves a specific user's information from a database.

## 🗄️ Analogies

Think of this code as a librarian fetching a specific book from a library.  The `user_id` is like the book's unique identification number (ISBN), and the database is like the library's collection of books. The code searches the library (database) using the ISBN (`user_id`) and retrieves the corresponding book (user record).

## 📥 Inputs & 📤 Outputs

- **Inputs:**
    - `user_id`: A unique identifier for the user whose data needs to be retrieved.  This is like the ISBN of the book we want to find.
- **Outputs:**
    - `user_record`:  The complete set of information stored about the specified user in the database. This is like the book itself, containing all the information about it.     

## ⚠️ Potential Risks & Questions

1. **Data Security:** Is this code accessing sensitive user data? Are there appropriate security measures in place to protect this data from unauthorized access or breaches?  How is the database connection secured?
2. **Error Handling:** What happens if the provided `user_id` doesn't exist in the database?  Does the code handle this gracefully, or will it crash? How are errors logged and reported?
3. **Database Performance:**  How efficient is this database query? If we have a large number of users, will this code perform quickly enough, or could it become a bottleneck?  Are there any indexes on the `id` column in the `users` table to speed up the query?      


Your Request (type '(end)' on a new line to submit, or '/new', 'exit', 'quit'):
I need to understand the purpose of this JavaScript code for a report. Please explain it.

const processItems = async (items) => {
  const results = await Promise.all(
    items.map(item =>
      fetch(`https://api.example.com/items/${item.id}`)
        .then(res => res.json())
    )
  );
  return results.filter(r => r.isActive);
};
(end)

> Thinking (Step 1/3: Choosing a tool)...
> LLM decided to call 'explain_code' with arguments: {'code_snippet': 'const processItems = async (items) => {\n  const results = await Promise.all(\n    items.map(item =>\n      fetch(`https://api.example.com/items/${item.id}`)\n        .then(res => res.json())\n    )\n  );\n  return results.filter(r => r.isActive);\n};', 'programming_language': 'javascript'}

Executing tool on MCP server...

--- ✅ Tool Execution Complete ---
{
  "status": "success",
  "explanation": "## \ud83c\udfaf Purpose\n\nThis code retrieves the details of multiple items from a remote server and returns only the active ones.\n\n## \ud83d\uddfa\ufe0f Analogies\n\nThink of this code as a restaurant kitchen receiving multiple orders (items) at once.  Each order slip has an item ID. The kitchen staff (the code) then checks each order against the menu database (the API) to get the full details of each dish. Finally, they only prepare and serve (return) the dishes that are currently available (active).\n\n## \ud83d\udce5 Inputs & \ud83d\udce4 Outputs\n\n- **Inputs:** \n    - An array of `items`. Each `item` in the array must have an `id` property (e.g., `{ id: 1 }, { id: 2 }`).  This `id` is used to fetch details about each item.\n- **Outputs:** \n    - An array of `results`. Each `result` contains the full details of an active item retrieved from the server.  Only items marked as `isActive` (presumably a boolean property within the item details) are included in the output.\n\n## \u26a0\ufe0f Potential Risks & Questions\n\n1. **Error Handling:** How does this code handle network errors or cases where the server returns an error?  If the connection to `api.example.com` fails or one of the items isn't found, what happens?  Does it gracefully handle these situations or could it crash the application?\n2. **Performance:** What happens if the `items` array is very large?  Fetching data for each item individually could be slow. Are there any rate limits on the API that we need to consider?  Could this lead to performance bottlenecks?\n3. **Data Security:**  Is the data retrieved from `api.example.com` sensitive?  If so, are there appropriate security measures in place to protect this data during transit and after it's received?  Does the API require authentication?\n"
}
> Direct answer found in tool result. Presenting directly.

LLM Response: ## 🎯 Purpose

This code retrieves the details of multiple items from a remote server and returns only the active ones.

## 🗺️ Analogies

Think of this code as a restaurant kitchen receiving multiple orders (items) at once.  Each order slip has an item ID. The kitchen staff (the code) then checks each order against the menu database (the API) to get the full details of each dish. Finally, they only prepare and serve (return) the dishes that are currently available (active).

## 📥 Inputs & 📤 Outputs

- **Inputs:**
    - An array of `items`. Each `item` in the array must have an `id` property (e.g., `{ id: 1 }, { id: 2 }`).  This `id` is used to fetch details about each item.
- **Outputs:**
    - An array of `results`. Each `result` contains the full details of an active item retrieved from the server.  Only items marked as `isActive` (presumably a boolean property within the item details) are included in the output.

## ⚠️ Potential Risks & Questions

1. **Error Handling:** How does this code handle network errors or cases where the server returns an error?  If the connection to `api.example.com` fails or one of the items isn't found, what happens?  Does it gracefully handle these situations or could it crash the application?
2. **Performance:** What happens if the `items` array is very large?  Fetching data for each item individually could be slow. Are there any rate limits on the API that we need to consider?  Could this lead to performance bottlenecks?
3. **Data Security:**  Is the data retrieved from `api.example.com` sensitive?  If so, are there appropriate security measures in place to protect this data during transit and after it's received?  Does the API require authentication?


Your Request (type '(end)' on a new line to submit, or '/new', 'exit', 'quit'):
Vẽ cho tôi một biểu đồ tròn thể hiện cơ cấu dân số theo độ tuổi. Dữ liệu là: {"labels": ["0-18", "19-35", "36-60", "60+"], "sizes": [25, 40, 25, 10]}. Tiêu đề là "Cơ cấu dân số".
(end)
(end)

> Thinking (Step 1/3: Choosing a tool)...
> LLM decided to call 'visualize_data' with arguments: {'chart_type': 'pie', 'data': {'labels': ['0-18', '19-35', '36-60', '60+'], 'sizes': [25, 40, 25, 10]}, 'title': 'Cơ cấu dân số'}


--- ✅ Tool Execution Complete ---
{
  "status": "success",
  "message": "Chart saved to generated_charts\\chart_211427.png",        
  "image_path": "generated_charts\\chart_211427.png"
}
> Direct answer found in tool result. Presenting directly.

LLM Response: Chart saved to generated_charts\chart_211427.png

PS F:\MCP_DeepResearch> python mcp_server.py
2025-06-14 11:38:03,924 - __main__ - INFO - Starting Multi-Tool Agent MCP Server...
INFO:     Started server process [63032]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
INFO:     127.0.0.1:62019 - "GET /sse HTTP/1.1" 200 OK
INFO:     127.0.0.1:62021 - "POST /messages/?session_id=f07cecae9eff4e6aa23bad6706e4a46a HTTP/1.1" 202 Accepted
INFO:     127.0.0.1:62021 - "POST /messages/?session_id=f07cecae9eff4e6aa23bad6706e4a46a HTTP/1.1" 202 Accepted
INFO:     127.0.0.1:62021 - "POST /messages/?session_id=f07cecae9eff4e6aa23bad6706e4a46a HTTP/1.1" 202 Accepted
2025-06-14 11:38:09,149 - mcp.server.lowlevel.server - INFO - Processing request of type ListToolsRequest
INFO:     127.0.0.1:62030 - "POST /messages/?session_id=f07cecae9eff4e6aa23bad6706e4a46a HTTP/1.1" 202 Accepted
2025-06-14 11:38:30,770 - mcp.server.lowlevel.server - INFO - Processing request of type CallToolRequest
2025-06-14 11:38:30,771 - __main__ - INFO - MCP Tool: ADVANCED search for 'current stock price of nvidia'
2025-06-14 11:38:30,771 - __main__ - INFO - Search attempt 1 with query: 'current stock price of nvidia'
2025-06-14 11:38:36,370 - __main__ - INFO - Found a definitive answer.
INFO:     127.0.0.1:62628 - "GET /sse HTTP/1.1" 200 OK
INFO:     127.0.0.1:62631 - "POST /messages/?session_id=fb39df496bda4879987efb1a366203c7 HTTP/1.1" 202 Accepted
INFO:     127.0.0.1:62631 - "POST /messages/?session_id=fb39df496bda4879987efb1a366203c7 HTTP/1.1" 202 Accepted
INFO:     127.0.0.1:62631 - "POST /messages/?session_id=fb39df496bda4879987efb1a366203c7 HTTP/1.1" 202 Accepted
2025-06-14 11:42:38,121 - mcp.server.lowlevel.server - INFO - Processing request of type ListToolsRequest
INFO:     127.0.0.1:62639 - "POST /messages/?session_id=fb39df496bda4879987efb1a366203c7 HTTP/1.1" 202 Accepted
2025-06-14 11:42:52,042 - mcp.server.lowlevel.server - INFO - Processing request of type CallToolRequest
2025-06-14 11:42:52,043 - __main__ - INFO - MCP Tool: ADVANCED search for 'what's the current stock price of Apple'
2025-06-14 11:42:52,044 - __main__ - INFO - Search attempt 1 with query: 'what's the current stock price of Apple'
2025-06-14 11:42:57,782 - __main__ - INFO - Found a definitive answer.
INFO:     127.0.0.1:62643 - "POST /messages/?session_id=fb39df496bda4879987efb1a366203c7 HTTP/1.1" 202 Accepted
2025-06-14 11:43:38,107 - mcp.server.lowlevel.server - INFO - Processing request of type CallToolRequest
2025-06-14 11:43:38,107 - __main__ - INFO - MCP Tool: MEDIUM search for 'What is a latent space in machine learning?'
2025-06-14 11:43:40,163 - __main__ - INFO - [Query Optimizer] For 'What is a latent space in machine learning?...' -> Generated Queries: ['latent space machine learning', 'machine learning latent space representation', 'what is latent space dimensionality reduction']
INFO:     127.0.0.1:62656 - "POST /messages/?session_id=fb39df496bda4879987efb1a366203c7 HTTP/1.1" 202 Accepted
2025-06-14 11:44:26,610 - mcp.server.lowlevel.server - INFO - Processing request of type CallToolRequest
2025-06-14 11:44:26,611 - __main__ - INFO - MCP Tool: MEDIUM search for 'Gợi ý cho tôi 3 nhà hàng món Việt ngon ở Quận 1, Sài Gòn cho một bữa tối gia đình.'
2025-06-14 11:44:28,192 - __main__ - INFO - [Query Optimizer] For 'Gợi ý cho tôi 3 nhà hàng món Việt ngon ở Quận 1, S...' -> Generated Queries: ['Nhà hàng món Việt ngon Quận 1 Sài Gòn gia đình', 'Bữa tối gia đình nhà hàng Việt Quận 1', 'Nhà hàng Việt ngon nhất Quận 1 Sài Gòn']
INFO:     127.0.0.1:62717 - "POST /messages/?session_id=fb39df496bda4879987efb1a366203c7 HTTP/1.1" 202 Accepted
2025-06-14 11:48:04,377 - mcp.server.lowlevel.server - INFO - Processing request of type CallToolRequest
2025-06-14 11:48:04,378 - __main__ - INFO - MCP Tool: MEDIUM search for 'Beginner steps to learn guitar'
2025-06-14 11:48:05,995 - __main__ - INFO - [Query Optimizer] For 'Beginner steps to learn guitar...' -> Generated Queries: ['learn guitar beginner steps', 'easy guitar lessons for beginners', 'beginner guitar tutorial first steps']
INFO:     127.0.0.1:62725 - "POST /messages/?session_id=fb39df496bda4879987efb1a366203c7 HTTP/1.1" 202 Accepted
2025-06-14 11:48:35,165 - mcp.server.lowlevel.server - INFO - Processing request of type CallToolRequest
2025-06-14 11:48:35,165 - __main__ - INFO - MCP Tool: Received request for DEEP research on 'I need a detailed technical report on the architecture of Google's new Gemma models.'
2025-06-14 11:48:35,170 - __main__ - INFO - [Progress Stream to Client] [Worker] Initializing agent swarm...
2025-06-14 11:48:35,172 - __main__ - INFO - [Progress Stream to Client] [Worker] Creating strategic research plan...
2025-06-14 11:48:46,302 - multi_agents.agents_logic - INFO - Generated research plan: {
  "core_concepts": [
    "Transformer-based language models: Understanding the fundamental architecture of transformer models, including attention mechanisms, self-attention, and encoder-decoder structures.",
    "Mixture-of-Experts (MoE) architecture:  A deep dive into how MoE models work, their advantages and disadvantages, and how they differ from standard transformer models.",    
    "Pathways system:  Understanding Google's Pathways system and how it facilitates training and serving large language models like Gemma."
  ],
  "key_questions": [
    "What is the specific architecture of the Gemma family of models? This includes details on the number of layers, hidden units, attention heads, and any unique architectural innovations.",
    "How does Gemma leverage the Mixture-of-Experts (MoE) architecture?  What is the routing algorithm used, and how many experts are employed in each model size?",
    "How does Gemma compare to other state-of-the-art language models, such as PaLM 2, LaMDA, and GPT models, in terms of architecture, performance, and efficiency?",
    "What are the training datasets and methods used for Gemma?  This includes data size, pre-processing steps, and optimization techniques.",
    "How does Google's Pathways system support the training and serving of Gemma models? What are the specific infrastructure and software components involved?"
  ],
  "information_requirements": [
    "Detailed diagrams and explanations of Gemma's architecture.",
    "Specific parameters and configurations of different Gemma model sizes (e.g., Gemma 2B, Gemma 7B).",
    "Benchmarking results comparing Gemma's performance on various NLP tasks.",
    "Information on the routing algorithm and expert selection within the MoE layers.",  
    "Details on the training data, including size, source, and pre-processing steps.",   
    "Information on the hardware and software infrastructure used for training and inference.",
    "Analysis of the computational cost and efficiency of Gemma compared to other models.",
    "Insights into any novel techniques or optimizations used in Gemma's architecture or training process."
  ],
  "research_priorities": [
    "1. Understanding the core architecture of Gemma models: Focus on obtaining detailed diagrams and descriptions of the model's structure.",
    "2. Investigating the MoE implementation:  Deep dive into the specifics of how MoE is integrated into Gemma.",
    "3. Comparative analysis with other LLMs:  Gather benchmark results and compare Gemma's performance and efficiency against other leading models.",
    "4. Training and infrastructure details:  Research the training datasets, methods, and the role of the Pathways system.",
    "5. Novel techniques and optimizations: Explore any unique aspects of Gemma's architecture or training process that contribute to its performance."
  ]
}
2025-06-14 11:48:46,306 - __main__ - INFO - [Progress Stream to Client] [Worker] Deep diving into: core_concepts - 'Transformer-based language models: Understanding the fundamental architecture of transformer models, including attention mechanisms, self-attention, and encoder-decoder structures.' (Attempt 1)
2025-06-14 11:48:50,146 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: '("Attention Is All You Need" (Ashish Vaswani OR Noam Shazeer OR Niki Parmar OR Jakob Uszkoreit OR Llion Jones OR Aidan N. Gomez OR Łukasz Kaiser OR Illia Polosukhin) filetype:pdf OR "transformer model" architecture technical details github documentation'     
2025-06-14 11:48:54,553 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: '("self-attention mechanism" OR "scaled dot-product attention") in-depth explanation academic papers (ICLR OR NeurIPS OR ACL) -marketing -tutorial -blog'
2025-06-14 11:49:00,729 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: 'encoder-decoder structure transformer model sequence-to-sequence learning (machine translation OR text summarization) implementation details code examples -towardsdatascience -medium'
2025-06-14 11:49:06,737 - __main__ - INFO - [Progress Stream to Client] [Worker] Deep diving into: key_questions - 'What is the specific architecture of the Gemma family of models? This includes details on the number of layers, hidden units, attention heads, and any unique architectural innovations.' (Attempt 1)
2025-06-14 11:49:09,158 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: 'Gemma architecture "number of layers" "hidden units" "attention heads" filetype:pdf OR filetype:docx OR site:arxiv.org'
2025-06-14 11:49:14,730 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: '"Gemma model" architecture technical specifications "official documentation" -marketing -tutorial'
2025-06-14 11:49:19,525 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: 'Gemma model github repository "model card" OR "architecture details" OR "implementation details"'
2025-06-14 11:49:24,431 - __main__ - INFO - [Progress Stream to Client] [Worker] Deep diving into: key_questions - 'What is the specific architecture of the Gemma family of models? This includes details on the number of layers, hidden units, attention heads, and any unique architectural innovations.' (Attempt 2)
2025-06-14 11:49:26,700 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: 'Gemma architecture layers hidden units attention heads filetype:pdf OR filetype:docx OR site:arxiv.org'
2025-06-14 11:49:33,301 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: '"Gemma model" github OR huggingface "architecture details" -marketing -tutorial' 
2025-06-14 11:49:38,854 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: '"Gemma" model technical specifications "official documentation" OR "white paper"'
2025-06-14 11:49:44,613 - __main__ - INFO - [Progress Stream to Client] [Worker] Deep diving into: key_questions - 'How does Gemma leverage the Mixture-of-Experts (MoE) architecture?  What is the routing algorithm used, and how many experts are employed in each model size?' (Attempt 1)
2025-06-14 11:49:47,143 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: 'Gemma MoE architecture routing algorithm expert count filetype:pdf OR filetype:docx OR site:arxiv.org'
2025-06-14 11:49:52,849 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: '"Mixture of Experts" AND "Gemma" AND "routing" AND ("number of experts" OR "expert count") -marketing -blog'
2025-06-14 11:49:58,072 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: 'Gemma model "sparsely gated mixture of experts" training details github OR arxiv.org'
2025-06-14 11:50:04,731 - __main__ - INFO - [Progress Stream to Client] [Worker] Deep diving into: key_questions - 'How does Gemma leverage the Mixture-of-Experts (MoE) architecture?  What is the routing algorithm used, and how many experts are employed in each model size?' (Attempt 2)
2025-06-14 11:50:07,491 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: 'Gemma MoE architecture routing algorithm expert count filetype:pdf OR site:arxiv.org'
2025-06-14 11:50:14,748 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: '"Mixture of Experts" AND Gemma AND "routing algorithm" AND "number of experts" -marketing -blog'
2025-06-14 11:50:19,268 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: 'Gemma model "expert capacity" "training scaling" "load balancing" site:github.com OR site:research.google.com'
2025-06-14 11:50:28,181 - __main__ - INFO - [Progress Stream to Client] [Worker] Found 63 unique sources. Generating final report...
2025-06-14 11:50:43,537 - __main__ - INFO - [Progress Stream to Client] [Worker] Task finished successfully.
2025-06-14 11:50:43,538 - __main__ - INFO - MCP Tool: Deep research task completed.      
INFO:     127.0.0.1:62811 - "POST /messages/?session_id=fb39df496bda4879987efb1a366203c7 HTTP/1.1" 202 Accepted
2025-06-14 11:52:36,649 - mcp.server.lowlevel.server - INFO - Processing request of type CallToolRequest
2025-06-14 11:52:36,650 - __main__ - INFO - MCP Tool: Received request to SUMMARIZE text.
INFO:     127.0.0.1:62833 - "POST /messages/?session_id=fb39df496bda4879987efb1a366203c7 HTTP/1.1" 202 Accepted
2025-06-14 11:53:28,570 - mcp.server.lowlevel.server - INFO - Processing request of type CallToolRequest
2025-06-14 11:53:28,572 - __main__ - INFO - MCP Tool: Received request to EXPLAIN Python code.
INFO:     127.0.0.1:62838 - "POST /messages/?session_id=fb39df496bda4879987efb1a366203c7 HTTP/1.1" 202 Accepted
2025-06-14 11:54:03,395 - mcp.server.lowlevel.server - INFO - Processing request of type CallToolRequest
2025-06-14 11:54:03,395 - __main__ - INFO - MCP Tool: Received request to EXPLAIN javascript code.
INFO:     127.0.0.1:62841 - "POST /messages/?session_id=fb39df496bda4879987efb1a366203c7 HTTP/1.1" 202 Accepted
2025-06-14 12:02:59,205 - mcp.server.lowlevel.server - INFO - Processing request of type CallToolRequest
2025-06-14 12:02:59,206 - __main__ - INFO - MCP Tool: VISUALIZE data as a pie chart.

Some more deep research attemps (This is before the suite-of-tools implementation, only deep research :) )

• V-JEPA2:

PS F:\MCP_DeepResearch> python mcp_server.py
2025-06-13 10:58:14,063 - __main__ - INFO - Starting DeepResearchAgent MCP Server...
INFO:     Started server process [41264]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
INFO:     127.0.0.1:64211 - "GET /sse HTTP/1.1" 200 OK
INFO:     127.0.0.1:64213 - "POST /messages/?session_id=4ddc7dd0f7d8478d8907a834bcae5b39 HTTP/1.1" 202 Accepted
INFO:     127.0.0.1:64230 - "POST /messages/?session_id=4ddc7dd0f7d8478d8907a834bcae5b39 HTTP/1.1" 202 Accepted
INFO:     127.0.0.1:64230 - "POST /messages/?session_id=4ddc7dd0f7d8478d8907a834bcae5b39 HTTP/1.1" 202 Accepted
2025-06-13 10:59:30,528 - mcp.server.lowlevel.server - INFO - Processing request of type CallToolRequest
2025-06-13 10:59:30,529 - __main__ - INFO - MCP Tool: Received request for deep research on 'Report on Meta's V-JEPA 2, focusing on the difference between conventional pre-training and their 'World Model' vision.'
2025-06-13 10:59:30,537 - __main__ - INFO - [Progress Stream to Client] [Worker] Initializing agent swarm...
2025-06-13 10:59:30,539 - __main__ - INFO - [Progress Stream to Client] [Worker] Creating strategic research plan...
2025-06-13 10:59:40,583 - multi_agents.agents_logic - INFO - Generated research plan: {
  "core_concepts": [
    "Self-Supervised Learning (SSL) for Computer Vision",
    "Conventional Pre-training Methods (e.g., contrastive learning, masked image modeling)",
    "World Models and their application in AI"
  ],
  "key_questions": [
    "How does V-JEPA 2's 'World Model' approach differ fundamentally from conventional SSL pre-training methods for computer vision?",
    "What are the specific architectural and training innovations introduced in V-JEPA 2 to implement this 'World Model' vision?",
    "What are the claimed advantages of using a 'World Model' for pre-training, specifically in terms of performance, generalization, and robustness?",
    "What evidence or experimental results are presented to support these claims of improved performance and capabilities?",
    "What are the limitations of V-JEPA 2 and its 'World Model' approach, and what are the potential future research directions?"
  ],
  "information_requirements": [
    "Detailed explanation of V-JEPA 2's architecture and training process.",
    "Comparison of V-JEPA 2's architecture with previous versions of V-JEPA and other relevant SSL methods (e.g., MAE, SimCLR, MoCo).",
    "Specific details about the 'World Model' implementation: How is it represented? How is it learned?",
    "Quantitative results demonstrating the performance of V-JEPA 2 on various computer vision benchmarks (e.g., ImageNet, COCO).",
    "Comparison of V-JEPA 2's performance with other state-of-the-art SSL methods.",
    "Analysis of V-JEPA 2's generalization ability to different datasets or tasks.",
    "Investigation of V-JEPA 2's robustness to adversarial attacks or noisy data.",
    "Discussion of the computational resources required for training V-JEPA 2.",
    "Identification of any limitations or drawbacks of the proposed approach.",
    "Exploration of potential future research directions based on V-JEPA 2's findings."
  ],
  "research_priorities": [
    "Understanding the 'World Model' concept and its theoretical underpinnings.",
    "Detailed analysis of V-JEPA 2's architecture and training methodology.",
    "Comparative study of V-JEPA 2 with conventional SSL methods.",
    "Evaluation of V-JEPA 2's performance and generalization capabilities.",
    "Investigation of limitations and future research directions."
  ]
}
2025-06-13 10:59:40,588 - __main__ - INFO - [Progress Stream to Client] [Worker] Deep diving into: core_concepts - 'Self-Supervised Learning (SSL) for Computer Vision' (Attempt 1)
2025-06-13 10:59:43,833 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: 'self-supervised learning computer vision "contrastive learning" OR "BYOL" OR "SimCLR" OR "MoCo"  github implementation details filetype:pdf OR filetype:md'
2025-06-13 10:59:50,009 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: 'self-supervised representation learning image classification "rotation prediction" OR "jigsaw puzzles" OR "in-instance discrimination" benchmark comparison arxiv'
2025-06-13 10:59:56,626 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: '"self-supervised learning for computer vision" survey OR review state-of-the-art recent advances limitations future directions  filetype:pdf publication:neurips OR publication:icml OR publication:cvpr'
2025-06-13 11:00:02,655 - __main__ - INFO - [Progress Stream to Client] [Worker] Deep diving into: core_concepts - 'Self-Supervised Learning (SSL) for Computer Vision' (Attempt 2)
2025-06-13 11:00:05,423 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: 'self-supervised learning computer vision "contrastive learning" OR "BYOL" OR "SimCLR" OR "MoCo" filetype:pdf OR site:arxiv.org'
2025-06-13 11:00:10,913 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: 'implementation details self-supervised learning frameworks computer vision "PyTorch" OR "TensorFlow" github OR documentation'
2025-06-13 11:00:15,451 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: '"A Comprehensive Study of Self-Supervised Learning" computer vision benchmark datasets "ImageNet" OR "CIFAR-10" academic papers peer-reviewed publications'
2025-06-13 11:00:20,459 - __main__ - INFO - [Progress Stream to Client] [Worker] Deep diving into: key_questions - 'How does V-JEPA 2's 'World Model' approach differ fundamentally from conventional SSL pre-training methods for computer vision?' (Attempt 1)
2025-06-13 11:00:23,474 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: 'V-JEPA 2 "world model" architecture vs. contrastive self-supervised learning (SSL) computer vision -marketing -blog'
2025-06-13 11:00:30,889 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: '"Predictive Coding" AND "V-JEPA" AND "Self-Supervised Learning" site:arxiv.org OR site:research.meta.com'
2025-06-13 11:00:37,004 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: 'Comparison of abstract predictive models with masked image modeling OR contrastive learning for visual representation learning "V-JEPA"'
2025-06-13 11:00:42,869 - __main__ - INFO - [Progress Stream to Client] [Worker] Research evaluation complete. All objectives met.
2025-06-13 11:00:42,870 - __main__ - INFO - [Progress Stream to Client] [Worker] Found 25 unique sources. Generating final report...
2025-06-13 11:01:02,072 - __main__ - INFO - MCP Tool: Research task completed. Returning final dictionary.
2025-06-13 11:01:02,073 - __main__ - INFO - [Progress Stream to Client] [Worker] Task finished successfully.
INFO:     Shutting down
INFO:     Waiting for application shutdown.
INFO:     Application shutdown complete.
INFO:     Finished server process [41264]
Traceback (most recent call last):
2025-06-13 11:00:42,869 - __main__ - INFO - [Progress Stream to Client] [Worker] Research evaluation complete. All objectives met.
2025-06-13 11:00:42,870 - __main__ - INFO - [Progress Stream to Client] [Worker] Found 25 unique sources. Generating final report...
2025-06-13 11:01:02,072 - __main__ - INFO - MCP Tool: Research task completed. Returning final dictionary.
2025-06-13 11:01:02,073 - __main__ - INFO - [Progress Stream to Client] [Worker] Task finished successfully.

PS F:\MCP_DeepResearch> python F:\MCP_DeepResearch\llm_tool_caller_client.py
--- Smart Research Client Initialized ---
An LLM will route your query to the appropriate tool on the server.
Type 'exit' or 'quit' to stop.

Your Request> A report on V-JEPA 2 from Meta, also focusing on the team's highlight of the difference between the conventional pre-training process and their vision of a "
World Model"

> Asking routing LLM to choose a tool...
> LLM decided to call 'conduct_deep_research' with arguments: {'query': "Report on Meta's V-JEPA 2, focusing on the difference between conventional pre-training and their 'World Model' vision."}

Executing tool on MCP server...
This may take several minutes. See the SERVER terminal for real-time progress.

--- ✅ RESEARCH COMPLETE ---
Status: success

--- 📝 FINAL REPORT ---
# Report on Meta's V-JEPA 2: World Models vs. Conventional Pre-training

This report examines Meta's V-JEPA 2, focusing on its "World Model" approach to self-supervised learning (SSL) and how it contrasts with conventional pre-training methods in computer vision. While the provided research context offers a broad overview of SSL techniques and mentions V-JEPA and its variants, specific details on V-JEPA 2's architecture, training process, and performance are limited. This report will therefore synthesize the available information to provide a high-level comparison between the "World Model" vision and established SSL methods.

## Conventional Self-Supervised Learning

The provided context highlights several dominant paradigms in conventional SSL for computer vision. These include:

* **Contrastive Learning:** Methods like MoCo, SimCLR, and PIRL learn representations by maximizing the similarity between different augmented views of the same image while minimizing similarity between views of different images. This approach relies heavily on carefully designed augmentations and often requires large batch sizes or memory banks for effective negative sampling.
* **Clustering:** Techniques such as DeepCluster, SeLA, and SwAV group images into clusters based on their learned representations. This provides a form of pseudo-supervision, allowing the network to learn by predicting cluster assignments.
* **Distillation:** Methods like BYOL and SimSiam leverage a student-teacher framework, where a student network learns to predict the output of a teacher network.  BYOL, notably, achieves this without negative samples, using a moving average of the student network's weights to update the teacher.
* **Predictive Methods:**  Beyond these, other SSL approaches involve predicting image transformations (e.g., rotation prediction) or solving jigsaw puzzles. These methods define a pretext task that encourages the network to learn useful representations.

These conventional methods have demonstrated considerable success in learning visual representations without labeled data. However, they often rely on handcrafted pretext tasks or complex training strategies like negative sampling.

## The "World Model" Vision

V-JEPA, and by extension V-JEPA 2, embodies a different philosophy.  It aims to learn a "World Model" through a joint-embedding predictive architecture (JEPA).  This approach involves predicting representations of masked portions of input data (images or videos) within a learned latent space.  This differs fundamentally from pixel-level reconstruction seen in generative models.  The "World Model" concept, as alluded to in the context, aligns with ideas from cognitive science and neuroscience, suggesting a more biologically plausible approach to learning.  Hierarchical JEPA further extends this by learning a hierarchy of representations, potentially capturing more complex relationships within the data.

V-JEPA's focus on predicting abstract representations, rather than pixel-level details, allows it to discard unpredictable information and focus on higher-level semantic understanding.  This is particularly relevant for video understanding, where temporal dynamics and complex interactions are crucial.  Masking in both space and time, as employed in V-JEPA, encourages the model to develop a deeper understanding of these dynamics.

## Comparing the Approaches

While direct comparison is difficult without specific details on V-JEPA 2, the core difference lies in the learning objective. Conventional SSL methods often rely on handcrafted pretext tasks or contrastive learning, which can be sensitive to hyperparameters and augmentation strategies.  V-JEPA's "World Model" approach, by contrast, focuses on learning internal representations that capture the underlying structure of the data.  This potentially leads to more robust and generalizable representations.

## Limitations and Future Directions

The available context does not provide sufficient information to discuss the specific limitations of V-JEPA 2 or its performance relative to other SSL methods. However, the general challenges of "World Model" approaches might include the difficulty of designing appropriate masking strategies and the computational cost of learning complex hierarchical representations. Future research directions could involve exploring different representation learning objectives, developing more efficient training algorithms, and evaluating the generalization capabilities of these models on a wider range of downstream tasks.


## Conclusion

V-JEPA 2 represents a shift from conventional SSL towards a more principled approach based on learning "World Models."  By predicting abstract representations rather than pixel-level details, V-JEPA aims to capture higher-level semantic understanding. While the provided context lacks specific details on V-JEPA 2, the core principles of its "World Model" approach suggest a promising direction for future research in self-supervised learning.  Further investigation into its architecture, training process, and performance will be crucial to fully assess its potential and compare it effectively with established SSL methods.


## Sources
1. [[PDF] Self-Supervised Learning in Vision](https://icml.cc/media/icml-2023/Slides/21552.pdf) - Date not available
2. [Self-Supervised-Learning-Papers-with-Code/README.md ... - GitHub](https://github.com/WangJingyao07/Self-Supervised-Learning-Papers-with-Code/blob/main/README.md) - Date not available
3. [[PDF] Bootstrap Your Own Latent A New Approach to Self-Supervised ...](https://misovalko.github.io/publications/grill2020bootstrap.pdf) - Date not available
4. [SSL-Backdoor/README.md at main - GitHub](https://github.com/UMBCvision/SSL-Backdoor/blob/main/README.md) - Date not available
5. [[PDF] arXiv:2112.12750v1 [cs.CV] 23 Dec 2021](https://arxiv.org/pdf/2112.12750) - Date not available
6. [Self-Supervised Learning for Image Segmentation - arXiv](https://arxiv.org/html/2505.13584v1) - Date not available
7. [Self-supervised learning for medical image classification - Nature](https://www.nature.com/articles/s41746-023-00811-0) - Date not available
8. [A review on discriminative self-supervised learning methods - arXiv](https://arxiv.org/html/2405.04969v1) - Date not available
9. [[PDF] Fine-Grained Self-Supervised Learning with Jigsaw Puzzles for ...](https://arxiv.org/pdf/2308.05770) - Date not available
10. [A Closer Look at Benchmarking Self-Supervised Pre-training ... - arXiv](https://arxiv.org/html/2407.12210v2) - Date not available
11. [A Survey of the Self Supervised Learning Mechanisms for ...](https://arxiv.org/html/2408.17059v1) - Date not available
12. [Self-Supervised Learning in Computer Vision](https://ancientmooner.github.io/doc/self-supervised-learning-cv-hanhu-BAAI.pdf) - Date not available
13. [Lecture 13: Self-Supervised Learning](https://cs231n.stanford.edu/slides/2023/lecture_13.pdf) - Date not available
14. [Lecture 12: Self-Supervised Learning](https://cs231n.stanford.edu/slides/2024/lecture_12.pdf) - Date not available
15. [PyTorch implementation of SimCLR: A Simple Framework ... - GitHub](https://github.com/sthalles/SimCLR) - Date not available
16. [byol - self-supervised learning for computer vision tasks. - GitHub](https://github.com/rafacelente/byol) - Date not available
17. [lightly-ai/lightly: A python library for self-supervised learning ... - GitHub](https://github.com/lightly-ai/lightly) - Date not available
18. [facebookresearch/dino: PyTorch code for Vision Transformers ...](https://github.com/facebookresearch/dino) - Date not available
19. [Vision AI Frameworks: TensorFlow vs PyTorch vs OpenCV - Ultralytics](https://www.ultralytics.com/blog/exploring-vision-ai-frameworks-tensorflow-pytorch-and-opencv) - Date not available
20. [On the genealogy of machine learning datasets: A critical history of ...](https://journals.sagepub.com/doi/full/10.1177/20539517211035955) - Date not available
21. [A Path Towards Autonomous Machines | PDF - SlideShare](https://www.slideshare.net/slideshow/a-path-towards-autonomous-machines/253779343) - Date not available
22. [V-JEPA 2: Self-Supervised Video Models Enable ...](https://arxiv.org/html/2506.09985v1) - Date not available
23. [Intuitive physics understanding emerges from self ...](https://arxiv.org/html/2502.11831v1) - Date not available
24. [V-JEPA: The next step toward advanced machine intelligence](https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/) - Date not available
25. [JEPA: LeCun's Path Towards More Human-Like AI - Medium](https://medium.com/@anil.jain.baba/jepa-lecuns-path-towards-more-human-like-ai-9535e48b3c65) - Date not available


Your Request> exit
Exiting client.
PS F:\MCP_DeepResearch>
25. [JEPA: LeCun's Path Towards More Human-Like AI - Medium](https://medium.com/@anil.jain.baba/jepa-lecuns-path-towards-more-human-like-ai-9535e48b3c65) - Date not available


Your Request> exit
Exiting client.
25. [JEPA: LeCun's Path Towards More Human-Like AI - Medium](https://medium.com/@anil.jain.baba/jepa-lecuns-path-towards-more-human-like-ai-9535e48b3c65) - Date not available


25. [JEPA: LeCun's Path Towards More Human-Like AI - Medium](https://medium.com/@anil.jain.baba/jepa-lecuns-path-towards-more-human-like-ai-9535e48b3c65) - Date not available


25. [JEPA: LeCun's Path Towards More Human-Like AI - Medium](https://medium.com/@anil.jain.baba/jepa-lecuns-path-towards-more-human-like-ai-9535e48b3c65) - Date not available

25. [JEPA: LeCun's Path Towards More Human-Like AI - Medium](https://medium.com/@anil.jain.ba25. [JEPA: LeCun's Path Towards Mor25. [JEPA: LeCun's Path Towards More Human-Like AI - Medium]25. [JEPA: LeCun's Path Towards More Human-Like AI - Medium](https://medium.com/@anil.jain.b25. [JEPA: LeCun's Path Towards More Human-Like 25. [JEPA: L25. 25. 25. [JEP25. [JEP25. 2525. [JEPA: LeCun's Path Towards More Human-Li25. [JEPA: LeCun's Path Towards More Human-Like AI - Medium](https://medium.com/@anil.jain.baba/jepa-lecuns-path-towards-more-human-like-ai-9535e48b3c65) - Date not available


Your Request> exit
Exiting client.

• RAG Techniques:

PS F:\MCP_DeepResearch> python mcp_server.py
2025-06-13 11:39:48,914 - __main__ - INFO - Starting DeepResearchAgent MCP Server...
INFO:     Started server process [4152]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
INFO:     127.0.0.1:49203 - "GET /sse HTTP/1.1" 200 OK
INFO:     127.0.0.1:49205 - "POST /messages/?session_id=ef324cb2572b4810a805d16cf8204529 HTTP/1.1" 202 Accepted
INFO:     127.0.0.1:49205 - "POST /messages/?session_id=ef324cb2572b4810a805d16cf8204529 HTTP/1.1" 202 Accepted
INFO:     127.0.0.1:49205 - "POST /messages/?session_id=ef324cb2572b4810a805d16cf8204529 HTTP/1.1" 202 Accepted
2025-06-13 11:39:55,080 - mcp.server.lowlevel.server - INFO - Processing request of type ListToolsRequest
INFO:     127.0.0.1:49270 - "GET /sse HTTP/1.1" 200 OK
INFO:     127.0.0.1:49272 - "POST /messages/?session_id=2a446a77550347988c13ed0e2457ba55 HTTP/1.1" 202 Accepted
INFO:     127.0.0.1:49272 - "POST /messages/?session_id=2a446a77550347988c13ed0e2457ba55 HTTP/1.1" 202 Accepted
INFO:     127.0.0.1:49272 - "POST /messages/?session_id=2a446a77550347988c13ed0e2457ba55 HTTP/1.1" 202 Accepted
2025-06-13 11:42:43,496 - mcp.server.lowlevel.server - INFO - Processing request of type ListToolsRequest
INFO:     127.0.0.1:49372 - "POST /messages/?session_id=2a446a77550347988c13ed0e2457ba55 HTTP/1.1" 202 Accepted
2025-06-13 11:45:16,703 - mcp.server.lowlevel.server - INFO - Processing request of type CallToolRequest
2025-06-13 11:45:16,704 - __main__ - INFO - MCP Tool: Received request for deep research on 'Report on RAG techniques used in the industry, highlighting unique features and improvements for solving pain points like the lack of interconnection between retrieved knowledge chunks.  Include examples like GraphRAG and its focus on building a long-lasting knowledge base.'
2025-06-13 11:45:16,708 - __main__ - INFO - [Progress Stream to Client] [Worker] Initializing agent swarm...
2025-06-13 11:45:16,711 - __main__ - INFO - [Progress Stream to Client] [Worker] Creating strategic research plan...
2025-06-13 11:45:26,998 - multi_agents.agents_logic - INFO - Generated research plan: {
  "core_concepts": [
    "Retrieval-Augmented Generation (RAG): Understanding the fundamental architecture, process flow, and core components (retriever, generator, prompt engineering).",
    "Knowledge Graphs:  Understanding their structure, benefits, and how they can be used to represent and connect knowledge.",
    "Challenges of Traditional RAG:  Familiarization with common issues like hallucination, context fragmentation, and limitations in handling complex reasoning."    
  ],
  "key_questions": [
    "What are the primary pain points of standard RAG implementations in real-world applications, particularly concerning the disconnection between retrieved knowledge chunks?",
    "How do advanced RAG techniques address these limitations? What novel approaches are being used to improve knowledge integration and coherence?",
    "What are the unique features and architectural improvements of GraphRAG and similar methods that leverage graph structures for knowledge representation?",       
    "How does GraphRAG's focus on building a long-lasting knowledge base contribute to solving the identified pain points? What are the practical implications and benefits?",
    "What are some other notable examples of industry-applied RAG techniques designed to enhance knowledge interconnection and reasoning capabilities, and how do they compare to GraphRAG?"
  ],
  "information_requirements": [
    "Detailed explanation of the limitations of standard RAG, supported by examples and case studies.",
    "Comprehensive overview of advanced RAG techniques, including their architectures, algorithms, and key innovations.",
    "In-depth analysis of GraphRAG, including its graph construction process, knowledge retrieval mechanisms, and generation strategies.",
    "Comparative analysis of GraphRAG with other relevant techniques, highlighting their strengths, weaknesses, and suitability for different applications.",
    "Empirical evidence and performance metrics demonstrating the effectiveness of GraphRAG and other advanced techniques in addressing the identified pain points.", 
    "Real-world examples and case studies showcasing the successful application of these techniques in various industries.",
    "Exploration of the future directions and potential research areas in RAG, particularly concerning knowledge graph integration and long-lasting knowledge bases." 
  ],
  "research_priorities": [
    "Understanding the core concepts of RAG and knowledge graphs.",
    "Identifying and analyzing the pain points of standard RAG implementations.",  
    "Deep dive into GraphRAG, its architecture, features, and benefits.",
    "Comparative analysis of GraphRAG with other advanced RAG techniques.",        
    "Exploration of real-world applications and case studies.",
    "Investigation of future research directions and potential improvements."      
  ]
}
2025-06-13 11:45:27,002 - __main__ - INFO - [Progress Stream to Client] [Worker] Deep diving into: core_concepts - 'Retrieval-Augmented Generation (RAG): Understanding the fundamental architecture, process flow, and core components (retriever, generator, prompt engineering).' (Attempt 1)
2025-06-13 11:45:30,368 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: 'Retrieval-Augmented Generation (RAG) architecture "neural retriever" OR "dense retriever"  filetype:pdf OR site:arxiv.org -"marketing" -"blog"'
2025-06-13 11:45:36,317 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: '"RAG" AND ("prompt engineering for retrieval" OR "prompt design for RAG") AND ("few-shot learning" OR "zero-shot learning")  site:aclweb.org OR site:neurips.cc'
2025-06-13 11:45:39,871 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching forarching for: 'implementation details of Retrieval-Augmented Generation (RAG)  "evaluation metrics" OR "open-source" github -"marketing" -"product demo"'
2025-06-13 11:45:46,360 - __main__ - INFO - [Progress Stream to Client] [Worker] Deep diving into: key_questions - 'What are the primary pain points of standard RAG implementations in real-world applications, particularly concerning the disconnection between retrieved knowledge chunks?' (Attempt 1)
2025-06-13 11:45:49,943 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: '("Retrieval Augmented Generation" OR RAG) "knowledge discontinuity" OR "context fragmentation" OR "disconnected knowledge chunks" filetype:pdf OR site:arxiv.org OR site:aclweb.org'     
: '("RAG limitations" OR "challenges of RAG") "context window" OR "coherence" OR "consistency"  "real-world applications" -marketing -vendor site:github.com OR site:medium.com/tagged/rag OR site:towardsdatascience.com/tagged/rag'       
2025-06-13 11:46:00,069 - __main__ - INFO - [Progress Stream to Client] [Worker] Searching for: '"Evaluating RAG" "knowledge graph integration" OR "graph-based retrieval" OR "semantic search"  "disconnected context" academic publications'
2025-06-13 11:46:06,765 - __main__ - INFO - [Progress Stream to Client] [Worker] Research evaluation complete. All objectives met.
2025-06-13 11:46:06,767 - __main__ - INFO - [Progress Stream to Client] [Worker] Found 22 unique sources. Generating final report...
2025-06-13 11:46:23,839 - __main__ - INFO - MCP Tool: Research task completed. Returning final dictionary.
2025-06-13 11:46:23,840 - __main__ - INFO - [Progress Stream to Client] [Worker] Task finished successfully.
INFO:     127.0.0.1:49546 - "POST /messages/?session_id=2a446a77550347988c13ed0e2457ba55 HTTP/1.1" 202 Accepted

PS F:\MCP_DeepResearch> python F:\MCP_DeepResearch\llm_tool_caller_client.py
--- Dynamic MCP Client Initialized ---
--> Discovering available tools from the server...
--> Success! Found 1 tools: ['conduct_deep_research']
An LLM will now route your query to the appropriate discovered tool.
Type 'exit' or 'quit' to stop.

Your Request> a report on RAG techniques used in the industry. The report should hi
ghlight the unique features and improvement of that technique for solving pain poin
ts of RAG. Eg: GraphRag is created to solve the lack of interconnection between retrieved chunks of knowledge and is an effort to build a long-lasting knowledge base 


> Asking routing LLM to choose a tool...
> LLM decided to call 'conduct_deep_research' with arguments: {'query': 'Report on RAG techniques used in the industry, highlighting unique features and improvements for solving pain points like the lack of interconnection between retrieved knowledge chunks.  Include examples like GraphRAG and its focus on building a long-lasting knowledge base.'}

Executing tool on MCP server...
This may take several minutes. See the SERVER terminal for real-time progress.

--- ✅ RESEARCH COMPLETE ---
Status: success

--- 📝 FINAL REPORT ---
# Report on RAG Techniques in the Industry

## Introduction

Retrieval-Augmented Generation (RAG) has become a crucial technique for enhancing the capabilities of Large Language Models (LLMs) in knowledge-intensive tasks.  By combining the generative power of LLMs with the ability to access and process external knowledge sources, RAG systems aim to produce more informed, factual, and contextually relevant outputs. This report examines the core components and workflow of RAG, analyzes the limitations of standard implementations, and explores advanced techniques, particularly GraphRAG and similar methods, that address these limitations by improving knowledge integration and coherence.

## Standard RAG Architecture and Workflow

RAG systems typically consist of a retriever and a generator.  Given a user query, the retriever searches a knowledge base (often indexed text data) for relevant information. This retrieved information is then used to augment the input to the generator, which produces the final output.  Retrievers can be based on sparse lexical methods like BM25, which excels at identifying lexical similarity, especially in specialized domains, or dense neural embeddings, which capture semantic relationships. The generator, often a pre-trained seq2seq model like BART, conditions its output on both the original query and the retrieved information.

## Challenges and Pain Points of Standard RAG

Standard RAG implementations face several challenges, particularly concerning the disconnection between retrieved knowledge chunks.  One key issue is context fragmentation, where retrieved passages are treated independently, leading to a lack of coherence and potentially inaccurate or contradictory information in the generated output.  This is especially problematic in multi-hop reasoning tasks, which require integrating information from multiple sources.  Another limitation is the constrained expressive capacity of retrieval models, particularly dense retrievers using bi-encoder architectures, which can struggle with complex queries.  Furthermore, the reliance on top-k retrieval can introduce a trade-off between recall and irrelevant context: a small k might miss crucial information, while a large k can overwhelm the generator with noise.

## Advanced RAG Techniques and Knowledge Integration

Advanced RAG techniques aim to address these limitations by improving knowledge integration and coherence.  Query rewriting methods, inspired by techniques like ITRG and HyDE, iteratively refine the user query to enhance semantic understanding and improve retrieval accuracy. Hybrid retrieval approaches combine the strengths of sparse and dense retrievers to expand the search scope and capture both lexical and semantic information.  However, integrating query rewriting with hybrid retrieval remains a challenge.

## GraphRAG and Knowledge Graph Integration

GraphRAG represents a significant advancement in RAG by leveraging knowledge graphs for knowledge representation and retrieval.  By structuring knowledge as a graph of interconnected entities and relationships, GraphRAG enables multi-hop reasoning and facilitates a more holistic understanding of the retrieved information.  This approach addresses the context fragmentation issue by explicitly modeling the relationships between knowledge chunks.  While the specific implementation details of GraphRAG's graph construction, retrieval mechanisms, and generation strategies are not fully detailed in the provided context, its focus on building a long-lasting knowledge base suggests a move towards more persistent and evolving knowledge integration within RAG systems.  This has the potential to improve the accuracy, consistency, and contextual relevance of generated outputs over time.

## Comparative Analysis and Future Directions

While a direct comparison of GraphRAG with other advanced techniques is limited by the available information, it is clear that GraphRAG's graph-based approach offers a unique solution to the problem of knowledge interconnection.  Other approaches, such as REFORM, address context fragmentation by conditioning retrieval embeddings on the entire input, promoting global context continuity.  However, REFORM's reliance on retrieval embeddings might still face limitations in handling complex relationships between knowledge pieces, a challenge that GraphRAG directly addresses.  Future research directions include exploring the optimal integration of knowledge graphs with different retrieval and generation strategies, developing robust evaluation metrics for graph-based RAG systems, and investigating the scalability and maintainability of long-lasting knowledge bases.


## Conclusion

RAG is a rapidly evolving field with significant potential for enhancing LLM applications.  While standard RAG implementations face challenges related to context fragmentation and limited reasoning capabilities, advanced techniques like GraphRAG offer promising solutions by leveraging knowledge graphs for improved knowledge integration and coherence.  The development of robust graph-based RAG systems, coupled with ongoing research in query rewriting, hybrid retrieval, and long-lasting knowledge bases, is paving the way for more powerful and contextually aware AI systems.


## Sources
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2. [Chain-of-Retrieval Augmented Generation](https://arxiv.org/html/2501.14342v1) - Date not available
3. [LevelRAG: Enhancing Retrieval-Augmented Generation ...](https://arxiv.org/html/2502.18139v1) - Date not available
4. [Retrieval-Augmented Generation for Knowledge-Intensive ...](https://arxiv.org/pdf/2005.11401) - Date not available
5. [Retrieval-Augmented Generation for AI-Generated Content](https://arxiv.org/html/2402.19473v1) - Date not available
6. [RAGFlow is an open-source RAG (Retrieval-Augmented ... - GitHub](https://github.com/infiniflow/ragflow) - Date not available
7. [vectara/open-rag-eval: Open source RAG evaluation package - GitHub](https://github.com/vectara/open-rag-eval) - Date not available
8. [NirDiamant/RAG_Techniques - GitHub](https://github.com/NirDiamant/RAG_Techniques) - Date not available
9. [MiniRAG: Towards Extremely Simple Retrieval-Augmented ...](https://arxiv.org/html/2501.06713v3) - Date not available
10. [Optimizing open-domain question answering with graph ...](https://arxiv.org/html/2503.02922v1) - Date not available
11. [Towards Extremely Simple Retrieval-Augmented Generation](https://arxiv.org/pdf/2501.06713?) - Date not available
12. [REFORMing Long-Context Processing in Transformers](https://arxiv.org/html/2506.01215v1) - Date not available
13. [ChatQA 2: Bridging the Gap to Proprietary LLMs in Long ...](https://arxiv.org/html/2407.14482v3) - Date not available
14. [detailed_report_how_to_improve...](https://github.com/LearningCircuit/local-deep-research/blob/main/examples/detaate not available
15. [awesome-rag/README.md at main](https://github.com/SJTU-DMTai/awesome-rag/blob/main/README.md) - Date not available
16. [README.md - Tongji-KGLLM/RAG-Survey](https://github.com/Tongji-KGLLM/RAG-Survey/blob/main/README.md) - Date not available
17. [README.md - Cache-Augmented Generation (CAG)](https://github.com/hhhuang/CAG/blob/main/README.md) - Date not available
15. [awesome-rag/README.md at main](https://github.com/SJTU-DMTai/awesome-rag/blob/main/README.md) - Date not available
16. [README.md - Tongji-KGLLM/RAG-Survey](https://github.com/Tongji-KGLLM/RAG-Survey/blob/main/README.md) - Date not available
17. [README.md - Cache-Augmented Generation (CAG)](https://github.com/hhhuang/CAG/blob/main/README.md) - Date not available
vailable
17. [README.md - Cache-Augmented Generation (CAG)](https://github.com/hhhuang/CAG/blob/main/README.md) - Date not available
18. [Evaluating RAG applications with Amazon Bedrock knowledge base ...](https://aws.amazon.com/blogs/machine-learning/evaluating-rag-applications-with-amazon-bedrock-knowledge-base-evaluation/) - Date not available
19. [[PDF] Knowledge Graph Integration and Self-Verification for ... - OpenReview](https://openreview.net/pdf?id=457wTt0ngj) - Date not available
20. [Evaluating Knowledge Graph Based Retrieval Augmented ... - arXiv](https://arxiv.org/abs/2504.05163) - Date not av/evaluating-rag-applications-with-amazon-bedrock-knowledge-base-evaluation/) - Date not available
19. [[PDF] Knowledge Graph Integration and Self-Verification for ... - OpenReview](https://openreview.net/pdf?id=457wTt0ngj) - Date not available
20. [Evaluating Knowledge Graph Based Retrieval Augmented ... - arXiv](https://arxiv.org/abs/2504.05163) - Date not av19. [[PDF] Knowledge Graph Integration and Self-Verification for ... - OpenReview](https://openreview.net/pdf?id=457wTt0ngj) - Date not available
20. [Evaluating Knowledge Graph Based Retrieval Augmented ... - arXiv](https://arxiv.org/abs/2504.05163) - Date not avt0ngj) - Date not available
20. [Evaluating Knowledge Graph Based Retrieval Augmented ... - arXiv](https://arxiv.org/abs/2504.05163) - Date not available
20. [Evaluating Knowledge Graph Based Retrieval Augmented ... - arXiv](https://arxiv.org/abs/2504.05163) - Date not available
ailable
21. [How to Implement Graph RAG Using Knowledge Graphs and Vector ...](https://medium.com/data-science/how-to-implemen- Date not available
22. [Revolutionizing RAG with Knowledge Graphs: The Future of ...](https://medium.com/@robertdennyson/revolutionizing-rag-with-knowledge-graphs-the-future-of-contextual-ai-b3addf5d9cc9) - Date not available


Your Request> quit