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seawolf2357 
posted an update 2 days ago
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3989
📚 Papers Leaderboard - See the Latest AI Research Trends at a Glance! ✨

Hello, AI research community! Today I'm introducing a new tool for exploring research papers. Papers Leaderboard is an open-source dashboard that makes it easy to find and filter the latest AI research papers.

Heartsync/Papers-Leaderboard

🌟 Key Features

Date Filtering: View only papers published within a specific timeframe (from May 5, 2023 to present)
Title Search: Quickly find papers containing your keywords of interest
Abstract Search: Explore paper content more deeply by searching for keywords within abstracts
Automatic Updates: The database is updated with the latest papers every hour

💡 How to Use It?

Select a start date and end date
Enter keywords you want to find in titles or abstracts
Adjust the maximum number of search results for abstract searches
Results are displayed neatly in table format
ginipick 
posted an update 2 days ago
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3059
🤖 AI Academic Paper Generator: Your Research Partner 🎓

Hello, researchers! Today I'm introducing my AI Academic Paper Generation System. This application is built with Streamlit and provides AI agents to assist with every stage of the academic research process.

ginipick/AgentX-Papers

✨ Key Features

📚 Literature Research: AI reviews and summarizes relevant research
📝 Paper Outline: Generates a well-structured paper outline
✍️ Draft Writing: Creates a paper draft based on your research topic
🔗 Citation Generation: Automatically generates academic citations
🖋️ Editing & Polishing: Checks grammar, context, and logical flow
🌐 Multilingual Support: Interface available in English and Korean

🚀 How to Use

Enter basic information like research topic, paper title, and deadline
AI agents generate everything from literature review to final paper
Download your completed paper or consult with the chatbot for further assistance

💡 What Makes It Special
This tool integrates all stages of academic research. Going beyond simple text generation, it mimics the actual research process to produce higher quality papers.
Visualization features and social media sharing options will be added in the next update! 💪

#AIResearch #AcademicWriting #ResearchAssistant #ArtificialIntelligence
aiqtech 
posted an update 2 days ago
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3080
🌐 AI Token Visualization Tool with Perfect Multilingual Support

Hello! Today I'm introducing my Token Visualization Tool with comprehensive multilingual support. This web-based application allows you to see how various Large Language Models (LLMs) tokenize text.

aiqtech/LLM-Token-Visual

✨ Key Features

🤖 Multiple LLM Tokenizers: Support for Llama 4, Mistral, Gemma, Deepseek, QWQ, BERT, and more
🔄 Custom Model Support: Use any tokenizer available on HuggingFace
📊 Detailed Token Statistics: Analyze total tokens, unique tokens, compression ratio, and more
🌈 Visual Token Representation: Each token assigned a unique color for visual distinction
📂 File Analysis Support: Upload and analyze large files

🌏 Powerful Multilingual Support
The most significant advantage of this tool is its perfect support for all languages:

📝 Asian languages including Korean, Chinese, and Japanese fully supported
🔤 RTL (right-to-left) languages like Arabic and Hebrew supported
🈺 Special characters and emoji tokenization visualization
🧩 Compare tokenization differences between languages
💬 Mixed multilingual text processing analysis

🚀 How It Works

Select your desired tokenizer model (predefined or HuggingFace model ID)
Input multilingual text or upload a file for analysis
Click 'Analyze Text' to see the tokenized results
Visually understand how the model breaks down various languages with color-coded tokens

💡 Benefits of Multilingual Processing
Understanding multilingual text tokenization patterns helps you:

Optimize prompts that mix multiple languages
Compare token efficiency across languages (e.g., English vs. Korean vs. Chinese token usage)
Predict token usage for internationalization (i18n) applications
Optimize costs for multilingual AI services

🛠️ Technology Stack

Backend: Flask (Python)
Frontend: HTML, CSS, JavaScript (jQuery)
Tokenizers: 🤗 Transformers library
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Kseniase 
posted an update 1 day ago
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3702
11 new types of RAG

RAG is evolving fast, keeping pace with cutting-edge AI trends. Today it becomes more agentic and smarter at navigating complex structures like hypergraphs.

Here are 11 latest RAG types:

1. InstructRAG -> InstructRAG: Leveraging Retrieval-Augmented Generation on Instruction Graphs for LLM-Based Task Planning (2504.13032)
Combines RAG with a multi-agent framework, using a graph-based structure, an RL agent to expand task coverage, and a meta-learning agent for better generalization

2. CoRAG (Collaborative RAG) -> CoRAG: Collaborative Retrieval-Augmented Generation (2504.01883)
A collaborative framework that extends RAG to settings where clients train a shared model using a joint passage store

3. ReaRAG -> ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented Generation (2503.21729)
It uses a Thought-Action-Observation loop to decide at each step whether to retrieve information or finalize an answer, reducing unnecessary reasoning and errors

4. MCTS-RAG -> MCTS-RAG: Enhancing Retrieval-Augmented Generation with Monte Carlo Tree Search (2503.20757)
Combines RAG with Monte Carlo Tree Search (MCTS) to help small LMs handle complex, knowledge-heavy tasks

5. Typed-RAG - > Typed-RAG: Type-aware Multi-Aspect Decomposition for Non-Factoid Question Answering (2503.15879)
Improves answers on open-ended questions by identifying question types (a debate, personal experience, or comparison) and breaking it down into simpler parts

6. MADAM-RAG -> Retrieval-Augmented Generation with Conflicting Evidence (2504.13079)
A multi-agent system where models debate answers over multiple rounds and an aggregator filters noise and misinformation

7. HM-RAG -> HM-RAG: Hierarchical Multi-Agent Multimodal Retrieval Augmented Generation (2504.12330)
A hierarchical multi-agent RAG framework that uses 3 agents: one to split queries, one to retrieve across multiple data types (text, graphs and web), and one to merge and refine answers

8. CDF-RAG -> CDF-RAG: Causal Dynamic Feedback for Adaptive Retrieval-Augmented Generation (2504.12560)
Works with causal graphs and enables multi-hop causal reasoning, refining queries. It validates responses against causal pathways

To explore what is Causal AI, read our article: https://www.turingpost.com/p/causalai

Subscribe to the Turing Post: https://www.turingpost.com/subscribe

Read further 👇
  • 1 reply
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fantos 
posted an update about 19 hours ago
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1327
🎨 BadgeCraft: Create Beautiful Badges with Ease! ✨
Hello there! Today I'm introducing BadgeCraft, a simple app that lets you create stunning badges for your websites, GitHub READMEs, and documentation.

🌟 Key Features

🖌️ 14 diverse color options including vibrant neon colors
🔤 Custom text input for label and message
🖼️ Support for 2000+ logos via Simple Icons
🔗 Clickable link integration
👁️ Real-time preview
💻 Ready-to-use HTML code generation

📝 How to Use

Label - Enter the text to display on the left side of the badge (e.g., "Discord", "Version", "Status")
Message - Enter the text to display on the right side of the badge
Logo - Type the name of a logo provided by Simple Icons (e.g., "discord", "github")
Style - Choose the shape of your badge (flat, plastic, for-the-badge, etc.)
Color Settings - Select background color, label background color, and logo color
Link - Enter the URL that the badge will link to when clicked

✅ Use Cases

Add social media links to your GitHub project README
Display version information or download links on your website
Include tech stack badges in blog posts
Show status indicators in documentation (e.g., "in development", "stable")

💡 Tips

Click on any of the prepared examples to automatically fill in all settings
Copy the generated HTML code and paste directly into your website or blog
HTML works in GitHub READMEs, but if you prefer markdown, use the ![alt text](badge URL) format

👨‍💻 Tech Stack
This app was built using Gradio and leverages the shields.io API to generate badges. Its simple UI makes it accessible for everyone!

🔗 openfree/Badge

✨ Available under MIT License - feel free to use and modify.
  • 1 reply
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openfree 
posted an update 3 days ago
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4054
🧠 ThinkFlow: The Revolutionary Platform That Gives LLMs the Power to Think 🚀

Hello AI community! We're excited to introduce you to ThinkFlow, an innovative service that transforms how language models solve problems. 🎉
VIDraft/ThinkFlow-llama

✨ What is ThinkFlow?
ThinkFlow is a groundbreaking platform that automatically applies step-by-step reasoning capabilities to existing LLM models without any modifications. It makes complex problem-solving transparent, allowing you to witness the model's thought process in real-time.

🔍 Key Features

Reasoning Without Model Modifications: Add step-by-step reasoning while utilizing existing LLMs as they are ⚙️
Visualized Thinking Process: See exactly how the model analyzes and solves problems 👁️
Before & After Comparison: Compare standard responses with reasoning-enhanced outputs in real-time 📊
Improved Accuracy: Deliver more accurate solutions for complex math and logic problems 📈
Educational Value: Teach students systematic approaches to problem-solving 👨‍🏫
User-Friendly Interface: Intuitive and easy-to-use UI for seamless experience 🖥️

💡 What Problems Can It Solve?
ThinkFlow is particularly effective for various domains including:

Complex mathematical problems 🧮
Logic puzzles 🧩
Questions requiring multi-step reasoning 🤔
Scientific analysis challenges 🔬
Complex decision-making processes 📝

👨‍💻 Technical Details
ThinkFlow is built on the meta-llama/Llama-3.1-8B-Instruct model and uses carefully designed prompt chains to guide the model through step-by-step thinking. Each reasoning step builds upon the results of previous steps, culminating in a comprehensive final answer.

💬 Join Our Community!
If you have questions or suggestions about ThinkFlow, join our Discord community: https://discord.gg/openfreeai
Let's build better AI reasoning experiences together! 💪

#AI #LLM #ReasoningAI #ThinkFlow #HuggingFace #OpenSource #AIEducation
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openfree 
posted an update 1 day ago
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2106
📊 Papers Impact: Instant AI Grading for Your Research Papers! 🚀

🌟 Introduction
Hello, AI research community! 🎉
Introducing Papers Impact - the revolutionary AI tool that automatically grades and predicts the potential impact of research papers! 🧠💡

VIDraft/PapersImpact

✨ Key Feature: Instant Paper Grading
The core functionality is brilliantly simple: Just enter an arXiv paper ID or URL, and our AI instantly analyzes and grades the paper's potential academic impact! No need to read through the entire paper yourself - our system automatically evaluates the title and abstract to generate a normalized impact score between 0 and 1.
🎯 How It Works

Enter Paper ID or URL: Simply paste an arXiv ID (e.g., "2504.11651") or full URL
Automatic Fetching: The system retrieves the paper's title and abstract
AI Analysis: Our advanced LLaMA-based transformer model analyzes the content
Instant Grading: Receive an impact score and corresponding letter grade in seconds!

💡 Who Can Benefit?

🔬 Researchers: Pre-assess your paper before submission
📚 Students: Quickly gauge the quality of papers for literature reviews
🏫 Educators: Objectively evaluate student research
📊 Research Managers: Prioritize which papers to read in depth
🧩 Journal Editors: Get an AI second opinion on submissions

🚀 Technical Details
Our model is trained on an extensive dataset of published papers in CS.CV, CS.CL, and CS.AI fields, using NDCG optimization with Sigmoid activation and MSE loss. It's been rigorously cross-validated against historical citation data to ensure accurate impact predictions.
  • 2 replies
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hesamation 
posted an update 1 day ago
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1545
The best researchers from DeepSeek, OpenAI, Microsoft, and ByteDance explored RL and Reasoning in LLMs,

Here's some of their key findings:

1/ RL can further improve distilled models. These models are essentially SFT fine-tuned with the data generated by larger models, and the SFT+RL combo does not disappoint.

This is verified in the DeepSeek-R1 paper.

2/ both GRPO and PPO algorithms suffer from length bias; they encourage longer responses. This can be tackled by introducing explicit rewards based on the length of the answer.

3/Most reasoning research is focused on code and math. But training models on logic puzzles improves them for mathematical tasks too.

This shows the RL reasoning is generalized beyond the specific domain knowledge.

Previous research also shows RL can be a great generalizer.

4/The reasoning might not be only induced by RL; it might already be hidden in the base models due to the pre-training and CoT data they were trained on.

So while RL does wake up the reasoning beast, maybe it's not the only solution (e.g. other methods such as distillation)

5/ back to the length bias; reasoning models tend to generate longer responses for wrong answers. RL might be the culprit.

RL favours longer answers when the reward is negative, to dilute the penalty per individual token and lower the loss.

This might explain the "aha" moments!

6/ OpenAI's competitive programming paper showed an interesting finding:

o3 can learn its own test-time strategies (like writing an inefficient but correct solution to verify the answer of an optimized solution)

RL helps LLMs develop their own reasoning & verification methods.
The recent article by @rasbt helped me a lot in getting a broad view of the recent research on reasoning models.

He also lists more influential papers on this topic, It's a must-read if you're interested.

check it out 👇
https://magazine.sebastianraschka.com/p/the-state-of-llm-reasoning-model-training
Jaward 
posted an update 1 day ago
AdinaY 
posted an update about 19 hours ago
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