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Ksenia Se

Kseniase

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commented on their article 3 days ago

🦸🏻#14: What Is MCP, and Why Is Everyone – Suddenly!– Talking About It?

replied to their post 5 days ago

13 Awesome MCP Servers MCP changed how agents connect with tools. After writing the most read explanation of MCP on Hugging Face (https://huggingface.co/blog/Kseniase/mcp), we chose this 13 awesome MCP servers that you can work with: 1. Agentset MCP -> https://github.com/agentset-ai/mcp-server For efficient and quick building of intelligent, doc-based apps using open-source Agentset platform for RAG 2. GitHub MCP Server -> https://github.com/github/github-mcp-server Integrates GitHub APIs into your workflow, allowing to build AI tools and apps that interact with GitHub's ecosystem 3. arXiv MCP -> https://github.com/andybrandt/mcp-simple-arxiv Allows working with research papers on arXiv through effective search and access to their metadata, abstracts, and links 4. MCP Run Python -> https://github.com/pydantic/pydantic-ai/tree/main/mcp-run-python Enables to run Python code in a sandbox via Pyodide in Deno, so it can be isolated from the rest of the operating system 5. Safe Local Python Executor -> https://github.com/maxim-saplin/mcp_safe_local_python_executor A lightweight tool for running LLM-generated Python code locally, using Hugging Face’s LocalPythonExecutor (from smolagents framework) and exposing it via MCP for AI assistant integration 6. Cursor MCP Installer -> https://github.com/matthewdcage/cursor-mcp-installer Allows to automatically add MCP servers to Cursor for development convenience 7. Basic Memory -> https://memory.basicmachines.co/docs/introduction This knowledge management system connects to LLMs and lets you build a persistent semantic graph from AI conversations with AI agents Read further in the comments 👇 If you like it, also subscribe to the Turing Post: https://www.turingpost.com/subscribe

posted an update 5 days ago

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commented on 🦸🏻#14: What Is MCP, and Why Is Everyone – Suddenly!– Talking About It? 3 days ago

Thank you! Fixed (don't know how that happened, his name is correct in other places in the article)

replied to their post 5 days ago

Filesystem MCP Server -> https://github.com/modelcontextprotocol/servers/tree/HEAD/src/filesystem
Read, write, search files, plus create, delete, list and move directories specified via args.
Notion MCP Server -> https://github.com/makenotion/notion-mcp-server
Enable models to interact with your Notion workspace to automate tasks such as searching, reading, creating, and updating pages and databases
Markdownify MCP Server -> https://github.com/zcaceres/markdownify-mcp
Converts various file types (PDFs, images, audio) and web pages to Markdown format
Fetch MCP Server -> https://github.com/modelcontextprotocol/servers/tree/main/src/fetch
Allows LLMs to retrieve and process content from web pages, converting HTML to markdown
Mobile Next - MCP server for Mobile Development and Automation -> https://github.com/mobile-next/mobile-mcp
Enables Agents and LLMs to interact with iOS/Android apps using accessibility snapshots or taps from screenshots
MCP installer -> https://github.com/anaisbetts/mcp-installer
This one is quite hilarious - "MCP for MCP". It allows you to ask your model (Claude, for example) to install MCP servers hosted in npm or PyPi for you.

posted an update 5 days ago

Post

1765

13 Awesome MCP Servers

MCP changed how agents connect with tools.

After writing the most read explanation of MCP on Hugging Face (https://huggingface.co/blog/Kseniase/mcp), we chose this 13 awesome MCP servers that you can work with:

1. Agentset MCP -> https://github.com/agentset-ai/mcp-server
For efficient and quick building of intelligent, doc-based apps using open-source Agentset platform for RAG

2. GitHub MCP Server -> https://github.com/github/github-mcp-server
Integrates GitHub APIs into your workflow, allowing to build AI tools and apps that interact with GitHub's ecosystem

3. arXiv MCP -> https://github.com/andybrandt/mcp-simple-arxiv
Allows working with research papers on arXiv through effective search and access to their metadata, abstracts, and links

4. MCP Run Python -> https://github.com/pydantic/pydantic-ai/tree/main/mcp-run-python
Enables to run Python code in a sandbox via Pyodide in Deno, so it can be isolated from the rest of the operating system

5. Safe Local Python Executor -> https://github.com/maxim-saplin/mcp_safe_local_python_executor
A lightweight tool for running LLM-generated Python code locally, using Hugging Face’s LocalPythonExecutor (from smolagents framework) and exposing it via MCP for AI assistant integration

6. Cursor MCP Installer -> https://github.com/matthewdcage/cursor-mcp-installer
Allows to automatically add MCP servers to Cursor for development convenience

7. Basic Memory -> https://memory.basicmachines.co/docs/introduction
This knowledge management system connects to LLMs and lets you build a persistent semantic graph from AI conversations with AI agents

Read further in the comments 👇

If you like it, also subscribe to the Turing Post: https://www.turingpost.com/subscribe

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replied to their post 12 days ago

T-JEPA -> https://huggingface.co/papers/2410.05016
This one is for tabular (structured) data. By masking one subset of a table’s features and predicting their latent representation from another subset, it learns rich, label-agnostic embeddings
ACT-JEPA -> https://huggingface.co/papers/2501.14622
Merges imitation and self-supervised learning to learn policy embeddings without heavy expert data. It predicts chunked actions and abstract observations in latent space, filtering noise, modeling dynamics, and cutting compounding errors
Brain-JEPA -> https://huggingface.co/papers/2409.19407
Applies JEPA in brain dynamics foundation model for demographic, disease, and trait prediction.
3D-JEPA -> https://huggingface.co/papers/2409.15803
JEPA for 3D representation learning. It samples one rich context block and several target blocks, then predicts each target’s embedding from the context
Point-JEPA -> https://huggingface.co/papers/2404.16432
Brings joint-embedding predictive learning to point clouds. A lightweight sequencer orders patch embeddings. It lets the model choose context and target patches and reuse distance calculations for speed

posted an update 12 days ago

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4228

12 Types of JEPA

JEPA, or Joint Embedding Predictive Architecture, is an approach to building AI models introduced by Yann LeCun. It differs from transformers by predicting the representation of a missing or future part of the input, rather than the next token or pixel. This encourages conceptual understanding, not just low-level pattern matching. So JEPA allows teaching AI to reason abstractly.

Here are 12 types of JEPA you should know about:

1. I-JEPA -> Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture (2301.08243)
A non-generative, self-supervised learning framework designed for processing images. It works by masking parts of the images and then trying to predict those masked parts

2. MC-JEPA -> MC-JEPA: A Joint-Embedding Predictive Architecture for Self-Supervised Learning of Motion and Content Features (2307.12698)
Simultaneously interprets video data - dynamic elements (motion) and static details (content) - using a shared encoder

3. V-JEPA -> Revisiting Feature Prediction for Learning Visual Representations from Video (2404.08471)
Presents vision models trained by predicting future video features, without pretrained image encoders, text, negative sampling, or reconstruction

4. UI-JEPA -> UI-JEPA: Towards Active Perception of User Intent through Onscreen User Activity (2409.04081)
Masks unlabeled UI sequences to learn abstract embeddings, then adds a fine-tuned LLM decoder for intent prediction.

5. Audio-based JEPA (A-JEPA) -> A-JEPA: Joint-Embedding Predictive Architecture Can Listen (2311.15830)
Masks spectrogram patches with a curriculum, encodes them, and predicts hidden representations.

6. S-JEPA -> S-JEPA: towards seamless cross-dataset transfer through dynamic spatial attention (2403.11772)
Signal-JEPA is used in EEG analysis. It adds a spatial block-masking scheme and three lightweight downstream classifiers

7. TI-JEPA -> TI-JEPA: An Innovative Energy-based Joint Embedding Strategy for Text-Image Multimodal Systems (2503.06380)
Text-Image JEPA uses self-supervised, energy-based pre-training to map text and images into a shared embedding space, improving cross-modal transfer to downstream tasks

Find more types below 👇

Also, explore the basics of JEPA in our article: https://www.turingpost.com/p/jepa

If you liked it, subscribe to the Turing Post: https://www.turingpost.com/subscribe

1 reply

replied to their post 19 days ago

Coursera courses:

AI Agents: From Prompts to Multi-Agent Systems -> https://www.coursera.org/learn/ai-agents-from-prompts-to-multi-agent-systems
Covers generative AI basics, building agentic workflows and the creation and orchestration of MAS. Includes 5 modules and is good for beginners.
Mastering Multi-Agent Development with AutoGen -> https://www.coursera.org/learn/packt-mastering-multi-agent-development-with-autogen-zyalb
Build MAS with AutoGen’s tools, optimizing their communications with special chat patterns. Intermediate level, includes 9 modules.
Practical Multi AI Agents and Advanced Use Cases with crewAI https://www.coursera.org/projects/practical-multi-ai-agents-and-advanced-use-cases-with-crewai

Explore collaborative agents for complex workflows, that integrate external tools and use different models. You will build MAS to automate planning, scoring, and large content creation tasks. It's good for beginner level

posted an update 19 days ago

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3135

7 Free resources to master Multi-Agent Systems (MAS)

Collective intelligence is the future of AI. Sometimes, a single agent isn't enough — a team of simpler, specialized agents working together to solve a task can be a much better option. Building Multi-Agent Systems (MAS) isn’t easy, that's why today we’re offering you a list of sources that may help you master MAS:

1. CrewAI tutorials -> https://docs.crewai.com/introduction#ready-to-start-building%3F
At the end of the page you'll find a guide on how to build a crew of agents that research and analyze a topic, and create a report. Also, there are useful guides on how to build a single CrewAI agent and a workflow

2. Building with CAMEL multi-agent framework -> https://github.com/camel-ai/camel
Offers guides, cookbooks and other useful information to build even million agent societies, explore and work with MAS

3. Lang Chain multi-agent tutorial -> https://langchain-ai.github.io/langgraph/agents/multi-agent/
Explains how to make agents communicate via handoffs pattern on the example of 2 multi-agent architectures - supervisor and swarm

4. "Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations" by Yoav Shoham and Kevin Leyton-Brown -> https://www.masfoundations.org/download.html
This book explains learning between agents, how multiple agents solve shared problems and communicate with focus on theory, practical examples and algorithms, diving into the game theory and logical approaches

Also, check out The Turing Post article about MAS -> https://www.turingpost.com/p/mas
Our article can be a good starting guide for you to explore what MAS is, its components, architectures, types, top recent developments and current trends

More resources in the comments 👇

If you liked it, also subscribe to the Turing Post: https://www.turingpost.com/subscribe

2 replies

replied to their post 26 days ago

Reinforcement Learning from Human Feedback (RLHF) -> https://huggingface.co/papers/2203.02155
The classic approach that combines supervised fine-tuning with RL on a reward model trained from human preference data.
Check out other RL+F approaches here: https://www.turingpost.com/p/rl-f
Monte Carlo Tree Search (MCTS) -> https://huggingface.co/papers/2305.10601
This planning algorithm builds a search tree by simulating many reasoning paths from the current state, balancing exploration and exploitation.
AMPO (Active Multi-Preference Optimization) -> https://huggingface.co/papers/2502.18293
Combines on-policy generation, contrastive learning, and smart selection of training examples. From many possible responses, it picks a small, diverse set with both high- and low-quality answers and unique styles
SPIN (Self-Play Fine-Tuning) -> https://huggingface.co/papers/2401.01335
Uses self-play, where the model learns by comparing its own generated responses to earlier outputs and human examples
SPPO (Self-Play Preference Optimization) -> https://huggingface.co/papers/2405.00675
Aligns LMs by framing training as a two-player game where the model learns to improve against itself through preference comparisons, aiming to reach a Nash equilibrium
RSPO (Regularized Self-Play Policy Optimization) -> https://huggingface.co/papers/2503.00030
Lets models learn through self-play, with an extra regularization term added to keep training stable. It achieves best results with a linear combination of forward and reverse KL divergence regularization

posted an update 26 days ago

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4978

11 Alignment and Optimization Algorithms for LLMs

When we need to align models' behavior with the desired objectives, we rely on specialized algorithms that support helpfulness, accuracy, reasoning, safety, and alignment with user preferences. Much of a model’s usefulness comes from post-training optimization methods.

Here are the main optimization algorithms (both classic and new) in one place:

1. PPO (Proximal Policy Optimization) -> Proximal Policy Optimization Algorithms (1707.06347)
Clips the probability ratio to prevent the new policy from diverging too far from the old one. It helps keep everything stable

2. DPO (Direct Preference Optimization) -> Direct Preference Optimization: Your Language Model is Secretly a Reward Model (2305.18290)
It's a non RL method, where an LM is an implicit reward model. It uses a simple loss to boost the preferred answer’s probability over the less preferred one

3. GRPO (Group Relative Policy Optimization) -> DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models (2402.03300)
An RL method that compares a group of model outputs for the same input and updates the policy based on relative rankings. It doesn't need a separate critic model
It's latest application is Flow-GRPO which adds online RL into flow matching models -> Flow-GRPO: Training Flow Matching Models via Online RL (2505.05470)

4. DAPO (Decoupled Clip and Dynamic sAmpling Policy Optimization) -> DAPO: An Open-Source LLM Reinforcement Learning System at Scale (2503.14476)
Decouples the clipping bounds for flexibility, introducing 4 key techniques: clip-higher (to maintain exploration), dynamic sampling (to ensure gradient updates), token-level loss (to balance learning across long outputs), and overlong reward shaping (to handle long, truncated answers)

5. Supervised Fine-Tuning (SFT) -> Training language models to follow instructions with human feedback (2203.02155)
Often the first post-pretraining step. A model is fine-tuned on a dataset of high-quality human-written input-output pairs to directly teach desired behaviors

More in the comments 👇

If you liked it, also subscribe to the Turing Post: https://www.turingpost.com/subscribe

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upvoted an article 30 days ago

Article

🦸🏻#17: What is A2A and why is it – still! – underappreciated?

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30 days ago

• 13

published an article 30 days ago

Article

🦸🏻#17: What is A2A and why is it – still! – underappreciated?

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30 days ago

• 13

upvoted a paper about 1 month ago

Real-World Gaps in AI Governance Research

Paper • 2505.00174 • Published Apr 30 • 11

replied to their post about 1 month ago

CoT-RAG -> https://huggingface.co/papers/2504.13534
Adds 3 new designs to CoT approach: 1) Knowledge Graph-driven CoT Generation to guide reasoning chains, 2) Learnable Knowledge Case-aware RAG for combining RAG with knowledge graphs to provide relevant sub-cases, and 3) Logic-based pseudo-program prompting execution.
Unsupervised Visual CoT (UV-CoT) -> https://huggingface.co/papers/2504.18397
Performs preference comparisons between model-generated bounding boxes. It generates and ranks model responses to visual regions, using this feedback to guide training to improve image-level reasoning.
CoTAL -> https://huggingface.co/papers/2504.02323
Combines CoT with active learning, using curriculum-aligned assessments, human-in-the-loop prompt design, and teacher/student feedback to improve automated grading. It boosts GPT-4’s accuracy by up to 24.5%.
Deconstructing Long CoT (DLCoT) -> https://huggingface.co/papers/2503.16385
Enhances distillation data by segmenting data, simplifying solutions, and optimizing of intermediate error states, improving model performance and token efficiency.

posted an update about 1 month ago

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10 new Chain-of-Thoughts (CoT) methods

CoT has long been one of the hottest techniques in AI thanks to its effectiveness and compelling core idea: encouraging models to solve complex problems through explicit intermediate reasoning steps. But usually researchers modify original CoT approach, finding tips that further improve LLMs' reasoning. That's what we're going to talk about today.

Here's a list of 10 latest enhanced CoT approaches:

1. Chain-of-Defensive-Thought -> Chain-of-Defensive-Thought: Structured Reasoning Elicits Robustness in Large Language Models against Reference Corruption (2504.20769)
Provides a few structured, defensive reasoning exemplars to improve the robustness of LLMs

2. Hybrid-CoT -> AdaR1: From Long-CoT to Hybrid-CoT via Bi-Level Adaptive Reasoning Optimization (2504.21659)
Proposes using Adaptive Hybrid Reasoning Model (AdaR1) that combines Long- and Short-CoT, and applying bi-level preference training to select effective reasoning styles

3. Semantic-level and token-level CoT -> T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT (2505.00703)
Introduces T2I-R1 text-to-image gen model, that uses semantic-level CoT for prompt planning and token-level CoT for pixel-level generation, while BiCoT-GRPO coordinates them both

4. Speculative CoT (SCoT) -> Efficient Reasoning for LLMs through Speculative Chain-of-Thought (2504.19095)
SCoT drafts multiple reasoning paths with a lightweight draft, selects the best, and uses the target model for correction - all this to reduce latency by 48–66%

5. Collaborative CoT (Co-CoT) -> Co-CoT: A Prompt-Based Framework for Collaborative Chain-of-Thought Reasoning (2504.17091)
Breaks reasoning into blocks that users can inspect, modify and re-run, promoting active engagement. An adaptation mechanism aligns outputs with diverse cognitive styles and user goals

6. XS-CoT -> Enhancing Non-Core Language Instruction-Following in Speech LLMs via Semi-Implicit Cross-Lingual CoT Reasoning (2504.20835)
It's a cross-lingual framework that integrates speech-to-text translation into reasoning, using a semi-implicit CoT approach to compress intermediate tokens. This improves non-core language responses by up to 45%

Read further in the comments 👇

If you liked this, also subscribe to the Turing Post -> https://www.turingpost.com/subscribe

1 reply

commented on 🦸🏻#14: What Is MCP, and Why Is Everyone – Suddenly!– Talking About It? about 1 month ago

great idea

commented on 🦸🏻#14: What Is MCP, and Why Is Everyone – Suddenly!– Talking About It? about 1 month ago

A2A overview coming soon!

commented on 🦸🏻#14: What Is MCP, and Why Is Everyone – Suddenly!– Talking About It? about 1 month ago

It seems to work for me, check https://www.youtube.com/watch?v=kQmXtrmQ5Zg

commented on 🦸🏻#14: What Is MCP, and Why Is Everyone – Suddenly!– Talking About It? about 1 month ago

So far I think it's mostly for integrating tools. A2A is about agents and their communication. I will post a detailed overview of A2A here on Hugging face soon

upvoted 2 articles about 1 month ago

Article

Tiny Agents: a MCP-powered agent in 50 lines of code

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Apr 25

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Article

What is MoE 2.0? Update Your Knowledge about Mixture-of-experts

and 1 other •

Apr 27

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