research tools

Hunyuan T1 is a very large-scale inference model launched by Tencent. It is based on reinforcement learning technology and significantly improves inference capabilities through extensive post-training. It performs outstandingly in long text processing and context capture, while optimizing the consumption of computing resources and having efficient reasoning capabilities. It is suitable for all kinds of reasoning tasks, especially in mathematics, logical reasoning and other fields. This product is based on deep learning and continuously optimized based on actual feedback. It is suitable for applications in scientific research, education and other fields.

SmolDocling-256M-preview is a language model with 256M parameters launched by ds4sd, focusing on the medical field. Its importance lies in providing effective tools for tasks such as medical text processing and medical knowledge extraction. In medical research and clinical practice, a large amount of text data needs to be analyzed and processed. This model can understand and process medical professional language. The main advantages include better performance in the medical field and the ability to handle a variety of medical-related text tasks, such as disease diagnosis assistance, medical literature abstracts, etc. The background of this model is that with the growth of medical data, there is an increasing need for technology to process medical text. It is positioned to provide language processing capability support for researchers, doctors, developers, etc. in the medical field. No price-related information is currently mentioned.

Project Aria is a project launched by Meta that focuses on first-person perspective research and aims to promote the development of augmented reality (AR) and artificial intelligence (AI) through innovative technologies. This project collects information from the user's perspective through devices such as Aria Gen 2 glasses to support machine perception and AR research. Its key strengths include innovative hardware design, rich open source datasets and challenges, and close collaboration with global research partners. The project comes amid Meta’s long-term investment in future AR technology and aims to drive industry progress through open research.

Elimination Game is an innovative benchmarking framework for evaluating the performance of large language models (LLMs) in complex social environments. It simulates a multi-player competition scenario similar to 'Werewolf' and tests the model's social reasoning, strategy selection and deception capabilities through public discussions, private communication and voting elimination mechanisms. This framework not only provides an important tool for studying the intelligence of AI in social games, but also provides developers with the opportunity to gain insights into the potential of models in real-life social scenarios. Its main advantages include multi-round interaction design, dynamic alliance and defection mechanisms, and detailed evaluation indicators that can comprehensively measure the social ability of AI.

Build Y is a cutting-edge technology platform developed by Necrozma Labs, designed to showcase and explore various innovative technologies. The platform covers the latest research results in fields ranging from artificial intelligence to biotechnology, from quantum computing to sustainable energy. Its main advantage is that it provides a centralized place for engineers and scientists to display and communicate, and promotes interdisciplinary technical cooperation and innovation. The background of the platform is to promote global scientific and technological progress and inspire more innovative thinking by sharing the latest research results and technological breakthroughs. At present, the specific price and positioning information of the platform are not clear, but its goal is to become a knowledge sharing center in the technology field.

DeepSeek Profile Data is a project focused on performance analysis of deep learning frameworks. It captures performance data for training and inference frameworks through PyTorch Profiler, helping researchers and developers better understand computation and communication overlapping strategies as well as underlying implementation details. This data is critical for optimizing large-scale distributed training and inference tasks, which can significantly improve system efficiency and performance. This project is an important contribution of the DeepSeek team in the field of deep learning infrastructure and aims to promote the community's exploration of efficient computing strategies.

Evo 2 is an AI basic model launched by NVIDIA, designed to analyze the genetic code of biomolecules through deep learning technology. Developed on the NVIDIA DGX Cloud platform, the model is capable of processing large-scale genomic data and provides a powerful tool for biomedical research. The main advantage of Evo 2 is its ability to process gene sequences of up to 1 million tokens, allowing for a more complete understanding of the complexity of the genome. The model has broad application prospects in the biomedical field, including disease diagnosis, drug development and gene editing. Evo 2 was developed with support from the Arc Institute and Stanford University with the goal of driving innovation and breakthroughs in biomedical research.

AlphaMaze is a project focused on improving the visual reasoning capabilities of large language models (LLM). It trains the model through maze tasks described in text form to enable it to understand and plan spatial structures. This method not only avoids complex image processing, but also directly evaluates the model's spatial understanding ability through text descriptions. Its main advantage is that it reveals how the model thinks about spatial problems, not just whether it can solve them. This model is based on an open source framework and aims to promote the research and development of language models in the field of visual reasoning.

This product is an open source project developed by Vectara to evaluate the hallucination rate of large language models (LLM) when summarizing short documents. It uses Vectara’s Hughes Hallucination Evaluation Model (HHEM-2.1) to calculate rankings by detecting hallucinations in the model output. This tool is of great significance for the research and development of more reliable LLM, and can help developers understand and improve the accuracy of the model.

The Anthropic Economic Index is a project focused on studying the impact of artificial intelligence on the labor market and economy. It provides a detailed picture of the practical applications of AI in the modern economy by analyzing large amounts of anonymized Claude.ai conversation data. The Index's first report, based on data from millions of conversations, reveals the use of AI in different professional tasks. Its main advantages are to provide empirical data to support policy development and to promote research collaboration through open data sets. The index is set against the backdrop of the profound impact of the rapid development of AI technology on the way we work, and aims to provide a scientific basis for responding to future changes in the labor market.

WeatherNext is the latest AI weather forecast technology developed by Google DeepMind and Google Research. It provides fast and accurate weather predictions through advanced AI models to help combat extreme weather events, improve the reliability of renewable energy, and enhance global food security. The technology is provided free of charge to scientists and forecasters to accelerate the research and application of global weather forecasting.

Open Thoughts is a project led by Bespoke Labs and the DataComp community to curate high-quality open source inference datasets for training advanced small models. The project brings together researchers and engineers from Stanford University, University of California, Berkeley, University of Washington and other universities and research institutions, and is committed to promoting the development of inference models through high-quality data sets. The background is that the application demand of current reasoning models in fields such as mathematics and code reasoning is growing, and high-quality data sets are the key to improving model performance. The project is currently free and open to researchers, developers, and professionals interested in inference models. The open source nature of its data sets and tools makes it an important resource for promoting artificial intelligence education and research.

Humanity's Last Exam is a multi-modal benchmark developed by a global collaboration of experts to measure the performance of large language models in academic settings. It contains 3,000 questions contributed by nearly 1,000 experts from more than 500 institutions in 50 countries, covering more than 100 disciplines. The test is intended to be the ultimate closed academic benchmark, pushing the boundaries of artificial intelligence technology by pushing the limits of models. Its main advantage is that it is highly difficult and can effectively evaluate the performance of models on complex academic problems.

Procyon AI Computer Vision Benchmark is a professional benchmarking tool developed by UL Solutions, designed to help users evaluate the performance of different AI inference engines on Windows PC or Apple Mac. The tool provides engineering teams with an independent, standardized assessment of the quality of their AI inference engine implementation and the performance of specialized hardware by performing a series of tests based on common machine vision tasks, leveraging a variety of advanced neural network models. The product supports a variety of mainstream AI inference engines, such as NVIDIA® TensorRT™, Intel® OpenVINO™, etc., and can compare the performance of floating point and integer optimization models. Its main advantages include ease of installation and operation, no need for complex configuration, and the ability to export detailed result files, etc. The product is positioned for professional users, such as hardware manufacturers, software developers and scientific researchers, to assist their research and development and optimization work in the field of AI.

METAGENE-1 is a basic metagenomic model developed by researchers at the University of Southern California, Prime Intellect, and the Nucleic Acid Observatory. The model has 7 billion parameters and was trained on 1.5 trillion base pairs of DNA and RNA sequences derived from human wastewater samples. The primary function of METAGENE-1 is to aid public health applications such as epidemic surveillance, pathogen detection and early detection of emerging health threats. Its advantage is that it can capture the complete distribution of genomic information in the human microbiome and has strong generalization capabilities.

FlagEval is a model evaluation platform that focuses on the evaluation of large language models and multi-modal models. It provides a fair and transparent environment that allows different models to be compared under the same standards, helps researchers and developers understand model performance, and promotes the development of artificial intelligence technology. The platform covers a variety of model types such as dialogue models and visual language models, supports the evaluation of open source and closed source models, and provides special evaluations such as K12 subject tests and financial quantitative trading evaluations.

ExploreToM is a framework developed by Facebook Research that aims to generate diverse and challenging theory-of-mind data at scale for enhanced training and evaluation of large language models (LLMs). The framework utilizes the A* search algorithm to generate complex story structures and novel, diverse, and plausible scenarios on a custom domain-specific language to test the limits of LLMs.

Procyon is a suite of performance testing benchmark tools developed by UL Solutions and designed for professional users in industry, enterprise, government, retail and media. Each benchmark in the Procyon suite provides a consistent and familiar experience and shares a common set of design and functionality. The flexible licensing model means users can choose the individual benchmarks that suit their needs. The Procyon Benchmark Suite will soon offer a series of benchmarks and performance tests aimed at professional users, each designed for a specific use case and using real applications wherever possible. UL Solutions works closely with industry partners to ensure each Procyon benchmark is accurate, relevant and unbiased.

FACTS Grounding is a comprehensive benchmark launched by Google DeepMind that aims to evaluate whether the responses generated by large language models (LLMs) are not only factually accurate with respect to the given input, but also detailed enough to provide users with satisfactory answers. This benchmark is critical to increasing the trust and accuracy of LLMs in real-world applications, helping to drive factual and fundamental progress across the industry.

Boltz-1 is the first truly open-source biomolecular structure prediction model developed by researchers at the Abdul Latif Jameel Health Machine Learning Clinic at the Massachusetts Institute of Technology (MIT), achieving AlphaFold3-level accuracy. The model is named after the Boltzmann distribution, a probability measure that describes the distribution of molecular structures. Boltz-1 was developed to encourage innovation beyond academia to support commercial use. It was developed by doctoral students Jeremy Wohlwend and Gabriele Corso and MIT Jameel Clinic researcher Saro Passaro, with guidance from MIT Electrical Engineering and Computer Science (EECS) professors Regina Barzilay and Tommi Jaakkola. The development of Boltz-1 faced challenges of scale and data processing, but ultimately succeeded in building the necessary computing power, providing a basis for standardizing structural biology research practices and hopefully accelerating the creation of life-changing drugs.

ProcessBench is a tool focused on identifying errors in mathematical reasoning. It identifies errors in the process by analyzing the steps of solving mathematical problems, which is of great significance to the field of education, especially mathematics education. This tool can help students and teachers identify and correct errors in mathematical problem solving and improve the accuracy and efficiency of problem solving. Based on deep learning technology, ProcessBench can process a large amount of mathematical problem data and provide technical support for mathematics education.

The RLVR-GSM-MATH-IF-Mixed-Constraints data set is a data set focused on mathematical problems. It contains various types of mathematical problems and corresponding solutions, and is used to train and verify reinforcement learning models. The importance of this data set lies in its ability to help develop smarter educational aids and improve students' ability to solve mathematical problems. Product background information shows that the data set was released by allenai on the Hugging Face platform, including two subsets: GSM8k and MATH, as well as IF Prompts with verifiable constraints, and is suitable for MIT License and ODC-BY license.

P-MMEval is a multilingual benchmark covering both basic and ability-specialized datasets. It extends existing benchmarks to ensure consistent language coverage across all datasets and provides parallel samples across multiple languages, supporting up to 10 languages ​​and covering 8 language families. P-MMEval facilitates comprehensive assessment of multilingual proficiency and conducts comparative analysis of cross-language transferability.

MAmmoTH-VL is a large-scale multi-modal reasoning platform that significantly improves the performance of multi-modal large language models (MLLMs) in multi-modal tasks through instruction tuning technology. The platform uses open models to create a dataset of 12 million command-response pairs, covering diverse, inference-intensive tasks with detailed and faithful justification. MAmmoTH-VL achieved state-of-the-art performance on benchmarks such as MathVerse, MMMU-Pro and MuirBench, demonstrating its importance in education and research.

Willow quantum chip is the latest generation of quantum chip developed by Google's quantum artificial intelligence team. It has made major breakthroughs in quantum error correction and performance. This chip can significantly reduce errors that occur as the number of qubits increases, achieving a key challenge that has been pursued in the field of quantum computing for nearly 30 years. Additionally, Willow completed a standard benchmark calculation in less than five minutes that would have taken today's fastest supercomputers 10^25 years, or well beyond the age of the universe. This achievement marks an important step towards building commercially significant large-scale quantum computers, which have the potential to revolutionize fields such as medicine, energy and artificial intelligence.

GraphCast is a deep learning model developed by Google DeepMind, focusing on global medium-term weather forecasting. This model uses advanced machine learning technology to predict weather changes and improve the accuracy and speed of forecasts. GraphCast models play an important role in scientific research, helping to better understand and predict weather patterns, and are of great value to many fields such as meteorology, agriculture, and aviation.

GenCast is a new high-resolution (0.25°) AI ensemble model developed by Google DeepMind that is more accurate than the European Center for Medium-Range Weather Forecasts (ECMWF)'s ENS system in predicting daily weather and extreme weather events, providing faster and more accurate forecasts up to 15 days in advance. This model is based on the diffusion model, which is a type of generative AI model that has recently made rapid progress in image, video and music generation. GenCast learns global weather patterns by analyzing historical weather data and can accurately generate complex probability distributions of future weather scenarios. The model's code, weights, and predictions will be publicly released to support the broader weather forecasting community.

Nous Research focuses on developing human-centered language models and simulators, working to align AI systems with real-world user experiences. Our main research areas include model architecture, data synthesis, fine-tuning, and inference. We prioritize the development of open source, human-compatible models that challenge traditional closed model approaches.

FrontierMath is a mathematical benchmarking platform designed to test the limits of artificial intelligence's ability to solve complex mathematical problems. It was co-created by more than 60 mathematicians and covers the full spectrum of modern mathematics from algebraic geometry to Zermelo-Fraenkel set theory. Each FrontierMath problem requires hours of work from expert mathematicians, and even the most advanced AI systems, such as GPT-4 and Gemini, can solve less than 2% of the problems. This platform provides a true evaluation environment where all questions are new and unpublished, eliminating the data contamination problem prevalent in existing benchmarks.

SimpleQA is a factual benchmark released by OpenAI that measures the ability of language models to answer short, fact-seeking questions. It helps evaluate and improve the accuracy and reliability of language models by providing datasets with high accuracy, diversity, challenge, and a good researcher experience. This benchmark is an important advance for training models that produce factually correct responses, helping to increase the model's trustworthiness and broaden its range of applications.

Brightband is a company dedicated to making weather and climate predictable through advanced earth system AI technology to help humans adapt to increasingly extreme weather changes. The platform encourages the global community to work together to improve the technical level of weather prediction through open source benchmark data sets, models and indicators. Brightband provides tools used by academia, government and companies to improve weather and climate-related decision-making to benefit people and the planet in the long term.

Google DeepMind is a leading artificial intelligence company owned by Google, focused on developing advanced machine learning algorithms and systems. DeepMind is known for its pioneering work in deep learning and reinforcement learning, with research spanning fields from gaming to healthcare. DeepMind's goal is to advance science and medicine by building intelligent systems to solve complex problems.

Chai-1 is a multimodal basic model for drug discovery that can predict the molecular structure of proteins, small molecules, DNA, RNA, covalent modifications, etc. It achieved a 77% success rate on the PoseBusters benchmark, which is comparable to AlphaFold3. Chai-1 operates without multiple sequence alignments, retains most of its performance, and is able to fold multimeric structures more accurately. In addition, Chai-1 can be combined with laboratory data to improve prediction performance. This model aims to transform biology from science to engineering and promote the application of AI in biological research.

Chai Discovery is a website focused on decoding life interactions. It may involve bioinformatics, genomics or related fields, aiming to reveal the complex interactions between living organisms through advanced technological means. The importance of this product or technology lies in the in-depth insights and data support it may provide for life sciences, medical research and related fields.

OpenBB is an online platform that uses artificial intelligence to streamline the investment research process. It allows users to customize analysis, quickly generate reports, and enhance investment decisions by integrating private data sets and large language models. The main advantages of the product include high efficiency, flexibility and user-friendly interface, especially suitable for use by financial professionals and investors.

The AI ​​Risk Repository is a comprehensive living database of more than 700 AI risks, categorized according to their causes and risk areas. It provides an easily accessible overview of AI risks and is a common reference framework for researchers, developers, businesses, evaluators, auditors, policymakers and regulators to help develop research, curriculum, audit and policy.

Trends.vc is an online platform that provides market research and trend analysis for entrepreneurs. It helps users save market research time and quickly understand the latest developments in AI, currency and other fields through free 5-minute reports. The platform brings together more than 62,564 like-minded founders to discuss and discover new market opportunities.

StudyRecon is a smart tool designed to simplify and assist literature review in the research process. It helps users quickly obtain comprehensive and accurate literature materials by providing a panoramic view of the academic landscape, query suggestions, cross-database searches, keyword visualization, paper abstracts and annotations, thereby improving the quality and efficiency of literature reviews.

The Thousand Brains Project was launched by Jeff Hawkins and Numenta to develop new artificial intelligence systems by understanding the working principles of the cerebral neocortex. This project is based on the Thousand Brains Theory of Intelligence and proposes a fundamentally different working principle of the brain from traditional AI systems. The goal of the project is to build an efficient and powerful intelligent system that can achieve the intelligence capabilities of humans. Numenta has opened up its research resources, including conference proceedings, open source code, and built a large community around its algorithms. The project is financially supported by the Gates Foundation and others, and researchers around the world are encouraged to participate or join this exciting project.

Models Table provides a list of over 300 large-scale language models used by all major AI labs, including Amazon Olympus, OpenAI GPT-5, OpenAI GPT-6, and more. This list demonstrates the development trends and diversity of large language models and is a valuable resource for AI researchers and developers.

Big Model House is a platform focusing on the artificial intelligence big model industry, providing information such as industry reports, technological innovation trends, expert reviews, awards and honors. It promotes the innovation and application of artificial intelligence technology by integrating industry resources, helping enterprises and individuals better understand and utilize large model technology.

The Fastest.ai is a website that provides reliable performance measurement data for evaluating the performance of popular models. It provides accurate performance data by measuring the model's response time, the number of tokens generated per second, and the total time from request to final token generation. The website is designed to help users choose the fastest AI model and provide performance comparisons of other models. It performs daily updates on model performance, and users can choose the appropriate model according to their needs.

The Aria Daily Activity Dataset is a re-release of the first pilot data set released by Project Aria, updated with new tools and location data to accelerate the development of machine perception and artificial intelligence technologies. The data set contains first-person video sequences in daily life scenes, and is equipped with rich sensor data, annotation data, and 3D point cloud data generated by the Aria machine perception service. Researchers can quickly get started with this dataset using specialized tools provided by Aria.

fforward.ai is an AI product that helps product managers analyze customer interviews and synthesize opportunities. It provides intelligent conversation analysis and machine learning technology to help product teams better understand customer needs and explore business opportunities. fforward.ai converts interview recordings into text and then extracts valuable information and insights through technologies such as natural language processing and sentiment analysis. Product managers can use these analysis results to discover and grasp the common needs of customers and provide guidance for product development and improvement.

UpCodes is a searchable database of U.S. building and construction codes. It brings together state and city building codes for easier navigation. It provides detailed building and construction codes to help professionals and ordinary users quickly find and understand relevant regulations. UpCodes also provides a range of features, including code update notifications, code comparison and advanced search. Users can choose different pricing plans according to their needs to get more features and services.

The KnowledgeGraph GPT project aims to use OpenAI's GPT-3 model to convert unstructured text data into structured knowledge graph representation. This product has powerful functions and advantages, is reasonably priced, and is positioned to meet users' needs for structured processing of text data.

Tuned In is an AI dynamic trend analysis tool that aggregates more than 400 trends from more than 50 trend reports and utilizes OpenAI’s GPT3 technology to synthesize topics. It provides key trends for 2023 to help users stay ahead and inspired.

Web3 Summary is a leading DeFi and NFT research platform, supporting upcoming DeFi researchers and NFT flippers. It includes features such as trading terminal, wallet research, Discord bot, mobile app, and more. Users can use it to conduct transaction research, wallet and contract scanning, obtain transaction Alpha, etc. Web3 Summary also provides Profit Taking, relative valuation, Chrome plug-in and other functions, suitable for DeFi and NFT traders. Please check the official website for pricing.

LAION is a non-profit organization dedicated to making machine learning resources available to the public, including data sets, tools, and models. We encourage open public education and greener use of resources through the reuse of existing datasets and models. We provide multiple datasets, models, and projects to support a wide range of AI research.