Best AI tools for< Enhance Mathematical Reasoning >

DeepSeek R1 is a revolutionary open-source AI model for advanced reasoning that outperforms leading AI models in mathematics, coding, and general reasoning tasks. It utilizes a sophisticated MoE architecture with 37B active/671B total parameters and 128K context length, incorporating advanced reinforcement learning techniques. DeepSeek R1 offers multiple variants and distilled models optimized for complex problem-solving, multilingual understanding, and production-grade code generation. It provides cost-effective pricing compared to competitors like OpenAI o1, making it an attractive choice for developers and enterprises.

AI MathGPT is an AI-powered math tutoring tool designed to assist students and parents with math homework. It offers advanced reasoning, step-by-step solutions, and clear explanations for challenging math problems. The tool aims to boost math performance, provide 24/7 support, and enhance math learning experiences for users of all levels.

Math Sniper is an AI-powered application designed to provide precise math solutions, exam preparation assistance, and exploration of mathematical concepts. The app offers step-by-step solutions to math challenges at all levels, connects users with math tutors for personalized help, and covers a wide range of subjects beyond mathematics, such as biology, chemistry, physics, history, economics, and language tasks. With features like Snap & Ask for instant answers, step-by-step explanations, and a user-friendly interface, Math Sniper aims to enhance users' understanding of complex concepts and facilitate learning in various disciplines.

neoSVG is an AI-powered application that allows users to generate scalable vector graphics (SVGs) from simple text prompts. The platform utilizes state-of-the-art AI technology and Bezier curve paths with mathematical rules to create unique and high-resolution vector outputs. Users can create a variety of visuals for web design, mobile apps, print media, AR/VR applications, UI/UX designs, logos, and more. neoSVG aims to provide efficient SVG generation by leveraging powerful servers and continuous research to enhance its AI capabilities.

Qwen is an AI tool that focuses on developing and releasing various language models, including dense models, coding models, mathematical models, and vision language models. The Qwen family offers open-source models with different parameter ranges to cater to various user needs, such as production use, mobile applications, coding assistance, mathematical problem-solving, and visual understanding of images and videos. Qwen aims to enhance intelligence and provide smarter and more knowledgeable models for developers and users.

yesnoerror is an autonomous AI agent developed by DeSci initiative that scans scientific papers to uncover errors, inconsistencies, and flawed methods that human reviewers may have missed. The tool utilizes blockchain technology and AI to audit science at scale, aiming to enhance scientific integrity through automated error detection. By analyzing papers from renowned repositories like arXiv, bioRxiv, and medRxiv, yesnoerror helps researchers identify and correct critical issues in research, such as mathematical errors and data discrepancies.

AI Times Tables is an AI multiplication learning helper that offers free printable multiplication charts and worksheets. It provides a convenient way for students to practice multiplication skills and for teachers or parents to assess mastery of fundamental concepts. The application is designed to enhance math learning journeys for children from Kindergarten to 4th Grade, with customizable use recommendations to suit individual learning paces.

Worksheep is an AI-powered tool that allows users to create math worksheets instantly. By leveraging artificial intelligence technology, users can generate customized math exercises efficiently. The platform streamlines the process of worksheet creation, saving time for educators and parents. With a user-friendly interface, Worksheep simplifies the task of producing math problems for various educational purposes.

EducUp Studio is an online platform designed to help educators create engaging and interactive learning experiences for their students. The platform offers a variety of tools and resources that allow teachers to easily design and customize educational content, such as quizzes, presentations, and interactive lessons. EducUp Studio aims to enhance the teaching and learning process by providing a user-friendly interface and a range of features to support educators in creating dynamic and effective educational materials.

AI Subtraction Learning Helper is an AI tool designed to assist students in learning subtraction through printable subtraction tables, charts, and worksheets. The application provides free resources in various formats, including PDF and JPG, to enhance math learning for children from Kindergarten to 4th Grade. It offers colored subtraction charts, number decomposition solutions, and subtraction games to make learning engaging and effective. Parents and teachers can use the tool to customize practice sessions and track students' progress in mastering fundamental subtraction concepts.

MathSolver.top is an AI math solver and personalized math tutor application that offers a free platform for solving math problems with high accuracy. Users can upload math questions and receive step-by-step answers instantly. The application includes features like Homework Helper Mode, Tutor/Learner Mode, and the ability to scan/upload/copy math problems for quick solutions. It provides personalized progressive learning through AI-generated study sets and smart recommendations for daily tasks. MathSolver aims to enhance math learning by offering a user-friendly interface and efficient problem-solving capabilities.

Kardinal is an AI-powered Last-Mile Delivery Optimization Platform dedicated to providing carrier and shipper clients with tailored logistics intelligence solutions. The platform offers route optimization, delivery slot planning, transport procurement optimization, territory sectorization, parcel picking & sorting, and PUDO location services. Kardinal utilizes AI algorithms and machine learning to continuously optimize operations, adapt to real-time conditions, and enhance future optimizations. The platform helps businesses address critical questions from strategic to operational levels, enabling them to optimize continuously and in real-time while modeling reality without compromise.

Studeo is an AI-powered educational platform that offers personalized learning experiences to students in the field of mathematics. Students can interact with their AI avatar to learn formulas, practice exercises, play games, and take tests. The platform is designed to help students improve their grades by providing feedback, tips, and a dialogue feature with the AI avatar. Studeo covers various academic levels and subjects, making it a comprehensive tool for students seeking to enhance their math skills.

Mileto is a platform designed to help students and learners in the fields of Science, Technology, Engineering, and Mathematics (STEM) by providing detailed solutions to their problems. Users can simply snap a picture of their STEM problem, and Mileto will generate a comprehensive solution. The platform aims to simplify the learning process and enhance understanding of complex STEM concepts through visual aids and step-by-step explanations.

Photo Enhance AI is an AI-powered online tool that allows users to enhance and edit their photos with advanced artificial intelligence technology. The tool offers a range of features such as upscaling images, restoring old photos, removing backgrounds, replacing backgrounds, enhancing nighttime photos, removing scratches, and objects from photos. Users can easily transform their photos with just one click, achieving professional-level results effortlessly. With a user-friendly interface and affordable pricing options, Photo Enhance AI is a convenient solution for anyone looking to improve the quality of their images.

Utopia Enhance is an AI-powered music intelligence tool that enhances the value of music by generating over 300 metadata tags through advanced audio and lyric analysis. It aims to supercharge the discoverability and searchability of songs, providing users with valuable insights and data to optimize their music experience.

Chrome Web Store is a platform where users can discover and install extensions and themes to enhance their browsing experience on the Google Chrome browser. It offers a wide range of extensions and themes for various purposes, such as productivity, entertainment, and customization. Users can find popular extensions, editor's picks, and top categories to personalize their browsing experience. The platform also features user reviews and ratings to help users make informed decisions when choosing extensions and themes.

Scribbr.com is an online platform that offers academic proofreading and editing services for students and academics. The website helps users improve the quality of their academic papers by providing professional editing and feedback. Scribbr.com aims to assist users in enhancing their writing skills and academic performance by offering personalized feedback and suggestions. The platform is designed to support users in achieving their academic goals by providing high-quality editing services and educational resources.

You.com is an AI tool designed for workplace productivity enhancement. It offers a deep research agent named ARI that can think, read, and analyze up to 400 sources to publish polished reports in under 5 minutes. The platform also provides features for marketing, sales, engineering, product data analysis, finance, and research, with industry-leading accuracy. Users can create custom agents and benefit from AI-powered solutions to solve complex challenges and transform ideas into visuals.

The Visual Studio Marketplace is a platform where users can find and publish extensions for Visual Studio family of products. It offers a wide range of extensions to enhance the functionality and features of Visual Studio, Visual Studio Code, Azure DevOps, and more. Users can customize their development environment with themes, tools, and integrations to improve productivity and efficiency.

MathPile is a generative AI tool designed for math, offering a diverse and high-quality math-centric corpus comprising about 9.5 billion tokens. It draws from various sources such as textbooks, arXiv, Wikipedia, ProofWiki, StackExchange, and web pages, catering to different educational levels and math competitions. The corpus is meticulously processed to ensure data quality, with extensive documentation and data contamination detection. MathPile aims to enhance mathematical reasoning abilities of language models.

The repository 'Awesome-LLM-Preference-Learning' is the official repository of a survey paper titled 'Towards a Unified View of Preference Learning for Large Language Models: A Survey'. It contains a curated list of papers related to preference learning for Large Language Models (LLMs). The repository covers various aspects of preference learning, including on-policy and off-policy methods, feedback mechanisms, reward models, algorithms, evaluation techniques, and more. The papers included in the repository explore different approaches to aligning LLMs with human preferences, improving mathematical reasoning in LLMs, enhancing code generation, and optimizing language model performance.

AReaL (Ant Reasoning RL) is an open-source reinforcement learning system developed at the RL Lab, Ant Research. It is designed for training Large Reasoning Models (LRMs) in a fully open and inclusive manner. AReaL provides reproducible experiments for 1.5B and 7B LRMs, showcasing its scalability and performance across diverse computational budgets. The system follows an iterative training process to enhance model performance, with a focus on mathematical reasoning tasks. AReaL is equipped to adapt to different computational resource settings, enabling users to easily configure and launch training trials. Future plans include support for advanced models, optimizations for distributed training, and exploring research topics to enhance LRMs' reasoning capabilities.

PURE (Process-sUpervised Reinforcement lEarning) is a framework that trains a Process Reward Model (PRM) on a dataset and fine-tunes a language model to achieve state-of-the-art mathematical reasoning capabilities. It uses a novel credit assignment method to calculate return and supports multiple reward types. The final model outperforms existing methods with minimal RL data or compute resources, achieving high accuracy on various benchmarks. The tool addresses reward hacking issues and aims to enhance long-range decision-making and reasoning tasks using large language models.

This repository focuses on exploring tools that enhance the performance of language models for various tasks. It provides a structured list of literature relevant to tool-augmented language models, covering topics such as tool basics, tool use paradigm, scenarios, advanced methods, and evaluation. The repository includes papers, preprints, and books that discuss the use of tools in conjunction with language models for tasks like reasoning, question answering, mathematical calculations, accessing knowledge, interacting with the world, and handling non-textual modalities.

This repository is dedicated to enhancing Language Model (LLM) reasoning with reinforcement learning (RL). It includes a collection of the latest papers, slides, and materials related to RL-based LLM reasoning, aiming to facilitate quick learning and understanding in this field. Starring this repository allows users to stay updated and engaged with the forefront of RL-based LLM reasoning.

KAG is a logical reasoning and Q&A framework based on the OpenSPG engine and large language models. It is used to build logical reasoning and Q&A solutions for vertical domain knowledge bases. KAG supports logical reasoning, multi-hop fact Q&A, and integrates knowledge and chunk mutual indexing structure, conceptual semantic reasoning, schema-constrained knowledge construction, and logical form-guided hybrid reasoning and retrieval. The framework includes kg-builder for knowledge representation and kg-solver for logical symbol-guided hybrid solving and reasoning engine. KAG aims to enhance LLM service framework in professional domains by integrating logical and factual characteristics of KGs.

SuperPrompt is an open-source project designed to help users understand AI agents. The project includes a prompt with theoretical, mathematical, and binary instructions for users to follow. It aims to serve as a universal catalyst for infinite conceptual evolution, focusing on metamorphic abstract reasoning and self-transcending objectives. The prompt encourages users to explore fundamental truths, create order from cognitive chaos, and prepare for paradigm shifts in understanding. It provides guidelines for analyzing multidimensional states, synthesizing emergent patterns, and integrating new paradigms.

STILL is an open-source project exploring slow-thinking reasoning systems, focusing on o1-like reasoning systems. The project has released technical reports on enhancing LLM reasoning with reward-guided tree search algorithms and implementing slow-thinking reasoning systems using an imitate, explore, and self-improve framework. The project aims to replicate the capabilities of industry-level reasoning systems by fine-tuning reasoning models with long-form thought data and iteratively refining training datasets.

Prompt4ReasoningPapers is a repository dedicated to reasoning with language model prompting. It provides a comprehensive survey of cutting-edge research on reasoning abilities with language models. The repository includes papers, methods, analysis, resources, and tools related to reasoning tasks. It aims to support various real-world applications such as medical diagnosis, negotiation, etc.

OpenManus-RL is an open-source initiative focused on enhancing reasoning and decision-making capabilities of large language models (LLMs) through advanced reinforcement learning (RL)-based agent tuning. The project explores novel algorithmic structures, diverse reasoning paradigms, sophisticated reward strategies, and extensive benchmark environments. It aims to push the boundaries of agent reasoning and tool integration by integrating insights from leading RL tuning frameworks and continuously updating progress in a dynamic, live-streaming fashion.

The Awesome-Neuro-Symbolic-Learning-with-LLM repository is a curated collection of papers and resources focusing on improving reasoning and planning capabilities of Large Language Models (LLMs) and Multi-Modal Large Language Models (MLLMs) through neuro-symbolic learning. It covers a wide range of topics such as neuro-symbolic visual reasoning, program synthesis, logical reasoning, mathematical reasoning, code generation, visual reasoning, geometric reasoning, classical planning, game AI planning, robotic planning, AI agent planning, and more. The repository provides a comprehensive overview of tutorials, workshops, talks, surveys, papers, datasets, and benchmarks related to neuro-symbolic learning with LLMs and MLLMs.

Awesome-Attention-Heads is a platform providing the latest research on Attention Heads, focusing on enhancing understanding of Transformer structure for model interpretability. It explores attention mechanisms for behavior, inference, and analysis, alongside feed-forward networks for knowledge storage. The repository aims to support researchers studying LLM interpretability and hallucination by offering cutting-edge information on Attention Head Mining.

The 'llms-tools' repository is a comprehensive collection of AI tools, open-source projects, and research related to Large Language Models (LLMs) and Chatbots. It covers a wide range of topics such as AI in various domains, open-source models, chats & assistants, visual language models, evaluation tools, libraries, devices, income models, text-to-image, computer vision, audio & speech, code & math, games, robotics, typography, bio & med, military, climate, finance, and presentation. The repository provides valuable resources for researchers, developers, and enthusiasts interested in exploring the capabilities of LLMs and related technologies.

The `Call-for-Reviewers` repository aims to collect the latest 'call for reviewers' links from various top CS/ML/AI conferences/journals. It provides an opportunity for individuals in the computer/ machine learning/ artificial intelligence fields to gain review experience for applying for NIW/H1B/EB1 or enhancing their CV. The repository helps users stay updated with the latest research trends and engage with the academic community.

OpenCompass is a one-stop platform for large model evaluation, aiming to provide a fair, open, and reproducible benchmark for large model evaluation. Its main features include: * Comprehensive support for models and datasets: Pre-support for 20+ HuggingFace and API models, a model evaluation scheme of 70+ datasets with about 400,000 questions, comprehensively evaluating the capabilities of the models in five dimensions. * Efficient distributed evaluation: One line command to implement task division and distributed evaluation, completing the full evaluation of billion-scale models in just a few hours. * Diversified evaluation paradigms: Support for zero-shot, few-shot, and chain-of-thought evaluations, combined with standard or dialogue-type prompt templates, to easily stimulate the maximum performance of various models. * Modular design with high extensibility: Want to add new models or datasets, customize an advanced task division strategy, or even support a new cluster management system? Everything about OpenCompass can be easily expanded! * Experiment management and reporting mechanism: Use config files to fully record each experiment, and support real-time reporting of results.

This repository contains a collection of papers and resources related to Large Language Models (LLMs), including their applications in various domains such as text generation, translation, question answering, and dialogue systems. The repository also includes discussions on the ethical and societal implications of LLMs. **Description** This repository is a collection of papers and resources related to Large Language Models (LLMs). LLMs are a type of artificial intelligence (AI) that can understand and generate human-like text. They have a wide range of applications, including text generation, translation, question answering, and dialogue systems. **For Jobs** - **Content Writer** - **Copywriter** - **Editor** - **Journalist** - **Marketer** **AI Keywords** - **Large Language Models** - **Natural Language Processing** - **Machine Learning** - **Artificial Intelligence** - **Deep Learning** **For Tasks** - **Generate text** - **Translate text** - **Answer questions** - **Engage in dialogue** - **Summarize text**

LLM Datasets is a repository containing high-quality datasets, tools, and concepts for LLM fine-tuning. It provides datasets with characteristics like accuracy, diversity, and complexity to train large language models for various tasks. The repository includes datasets for general-purpose, math & logic, code, conversation & role-play, and agent & function calling domains. It also offers guidance on creating high-quality datasets through data deduplication, data quality assessment, data exploration, and data generation techniques.

Awesome-LLM is a curated list of resources related to large language models, focusing on papers, projects, frameworks, tools, tutorials, courses, opinions, and other useful resources in the field. It covers trending LLM projects, milestone papers, other papers, open LLM projects, LLM training frameworks, LLM evaluation frameworks, tools for deploying LLM, prompting libraries & tools, tutorials, courses, books, and opinions. The repository provides a comprehensive overview of the latest advancements and resources in the field of large language models.

LLM4Opt is a collection of references and papers focusing on applying Large Language Models (LLMs) for diverse optimization tasks. The repository includes research papers, tutorials, workshops, competitions, and related collections related to LLMs in optimization. It covers a wide range of topics such as algorithm search, code generation, machine learning, science, industry, and more. The goal is to provide a comprehensive resource for researchers and practitioners interested in leveraging LLMs for optimization tasks.

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