# Research

Deep Founder Ai is a platform that uses artificial intelligence for research and generating complete business plans. Its main advantages are saving time, providing reliable research results, and helping users confidently start their startups.

AI Answer Generator is an online tool powered by advanced AI models that can instantly generate accurate answers related to the input question. Users just need to enter their question to get immediate and relevant answers without registration or technical skills. This tool is applicable for knowledge lookup, fact verification, learning research, brainstorming, writing translation and language learning, productivity, Q&A, as well as general curiosity and inspiration.

Ai2 PaperFinder is an online tool dedicated to helping researchers quickly find the literature they need. The platform brings together over 8 million full-text papers and 108 million abstracts, with powerful literature retrieval and synthesis capabilities. Through simple keyword searches, users can quickly access relevant research, greatly improving research efficiency. The product is born from an urgent need for research literature and is priced as free, suitable for all types of researchers.

NextGenAI is an alliance initiated by OpenAI, bringing together 15 leading research institutions committed to leveraging AI to accelerate research breakthroughs and transform education. The alliance receives $50 million in research funding, computing resources, and API access from OpenAI, supporting students, educators, and researchers in expanding the frontiers of knowledge. By uniting institutions across the US and internationally, NextGenAI aims to drive progress faster than any single institution could, cultivating the next generation of leaders who will shape the future of AI.

Deep Review by SciSpace is an in-depth literature review tool for researchers and scholars. It uses artificial intelligence technology to help users quickly complete systematic literature reviews, ensuring that no important papers are missed. The tool supports multiple functions, such as literature search, in-depth analysis, and data extraction, aiming to improve research efficiency. It is positioned as an intelligent assistant for researchers, and the price may need to be further confirmed through the official website.

This product is a web-based AI research tool designed to help users conduct in-depth topic research quickly and efficiently. By integrating multiple search engines, web crawler technology, and large language models, it enables iterative in-depth research and displays the research process in an intuitive tree structure. The tool supports multilingual search and features real-time feedback, search visualization, and report export functions, greatly improving research efficiency. It is suitable for users who need to collect and analyze large amounts of information; students, researchers, and professionals alike can benefit from it. Currently, this product is offered for free, providing high cost-effectiveness and practical value.

SWE-RL is a reinforcement learning-based large language model reasoning technique proposed by Facebook Research, aiming to leverage open-source software evolution data to improve model performance in software engineering tasks. This technology optimizes the model's reasoning capabilities through a rule-driven reward mechanism, enabling it to better understand and generate high-quality code. The main advantages of SWE-RL lie in its innovative reinforcement learning approach and effective utilization of open-source data, opening up new possibilities in the field of software engineering. The technology is currently in the research phase and does not yet have a defined commercial pricing, but it shows significant potential in improving development efficiency and code quality.

Magma-8B is a foundational multi-modal AI model developed by Microsoft, specifically designed for researching multi-modal AI agents. It integrates text and image inputs to generate text outputs and possesses visual planning and agent capabilities. The model utilizes Meta LLaMA-3 as its language model backbone and incorporates a CLIP-ConvNeXt-XXLarge vision encoder. It can learn spatiotemporal relationships from unlabeled video data, exhibiting strong generalization capabilities and multi-task adaptability. Magma-8B excels in multi-modal tasks, particularly in spatial understanding and reasoning. It provides a powerful tool for multi-modal AI research, advancing the study of complex interactions in virtual and real-world environments.

Open Multi-Agent Canvas is an open-source multi-agent chat interface built using Next.js, LangGraph, and CopilotKit. It allows users to manage multiple agents within a dynamic conversation, primarily for travel planning and research. Leveraging advanced technologies, it provides users with an efficient and flexible multi-agent interaction experience. Its open-source nature allows developers to customize and extend it to meet diverse needs, offering high flexibility and scalability.

AlphaMaze is a project focused on enhancing the visual reasoning abilities of Large Language Models (LLMs). It trains models through maze tasks described in text format, enabling them to understand and plan in spatial structures. This method avoids complex image processing and directly assesses the model's spatial understanding through text descriptions. Its main advantage is the ability to reveal how the model thinks about spatial problems, rather than simply whether it can solve them. The model is based on open-source frameworks and aims to promote research and development of language models in the field of visual reasoning.

SWE-Lancer, launched by OpenAI, is a benchmark designed to assess the performance of cutting-edge language models in real-world freelance software engineering tasks. This benchmark encompasses a range of independent engineering tasks, from $50 bug fixes to $32,000 feature implementations, as well as managerial tasks such as selecting between technical implementation options. By mapping model performance to monetary value, SWE-Lancer offers a new perspective on researching the economic impact of AI model development and promoting the advancement of related research.

DeepResearch123 is an AI research resource navigation platform aimed at providing a wealth of resources, documentation, and practical case studies for researchers, developers, and enthusiasts. The platform covers the latest research outcomes in various fields, including machine learning, deep learning, and artificial intelligence, helping users quickly understand and master relevant knowledge. Its main advantages are the abundance of resources and clear categorization, making it easy for users to find and learn. The platform is open to all individuals interested in AI research, benefiting both beginners and professionals. Currently, the platform is free to use, allowing users to access all features without charge.

NEAR AI is dedicated to creating a future where users own their data and AI. It empowers users to control their data through open standards and protocols, rather than being governed by a select few companies. NEAR AI envisions a democratized AI landscape that enables users to genuinely own and control their AI through open models and protocols. Currently in its early stages, it has already demonstrated tremendous potential and possibilities for future development.

Aidy is an online research platform, supported by Y Combinator, that focuses on in-depth, ongoing, and customized research in policy, politics, and markets. By tracking high-quality online resources, it provides users with the latest intelligence and analysis to help them and their organizations excel in relevant fields. A key advantage of Aidy is its ability to ensure users do not miss key information such as actions, citations, or documents, and it allows for precise control over the depth and breadth of research based on user needs. The platform is suitable for teams of all sizes, from individual consulting firms to large corporate organizations, providing a competitive advantage. Specific pricing is not clearly stated on the platform, but it is positioned to deliver in-depth research services for professional users.

SakanaAI/asal is a research project that utilizes foundational models (FMs) to automate the search for Artificial Life (ALife). This project combines the latest AI technologies, particularly visual language foundational models, to discover simulations capable of producing target phenomena, generating temporally open novelty, and illuminating the entire fascinating and diverse landscape of simulations. It spans various ALife bases, including Boids, Particle Life, Game of Life, Lenia, and neural cellular automata, showcasing the potential of technological means to accelerate artificial life research.

QVQ-72B-Preview is an experimental research model developed by the Qwen team, focusing on enhancing visual reasoning capabilities. The model demonstrates strong abilities in multidisciplinary understanding and reasoning, achieving significant advances especially in mathematical reasoning tasks. Although advancements have been made in visual reasoning, it does not completely replace the capabilities of Qwen2-VL-72B, and may gradually lose focus on image content in multi-step visual reasoning, leading to hallucinations. Furthermore, QVQ does not show significantly better performance in basic recognition tasks compared to Qwen2-VL-72B.

Research Rabbit is an AI-based research assistant that can automatically delve into any user-defined topic. It utilizes a large language model (LLM) to generate search queries based on the user’s topic, retrieve web search results, and summarize those results using the LLM. Next, it reflects on the summary with the LLM, identifies knowledge gaps, and generates new search queries to fill those gaps. This process is repeated until the user-defined cycle count is met, ultimately providing a final Markdown summary that includes all utilized sources. The product is fully configured to operate with a local LLM (via Ollama).

Project Astra is a research prototype project by Google DeepMind aimed at exploring the capabilities of future general-purpose AI assistants. It assists users in exploring the world through natural interactions such as voice and video. Project Astra represents the forefront of AI technology application in daily life, emphasizing the importance of artificial intelligence in providing personalized help, enhancing efficiency, and fostering innovation. As a research prototype, Project Astra is currently available only to a limited number of trust testers, and specific details regarding background and pricing are not explicitly provided on the page.

MAmmoTH-VL is a large-scale multimodal reasoning platform that significantly enhances the performance of multimodal large language models (MLLMs) on various multimodal tasks through instruction tuning techniques. The platform has created a dataset consisting of 12 million instruction-response pairs using open models, covering a wide range of reasoning-intensive tasks and providing detailed and accurate reasoning steps. MAmmoTH-VL has achieved state-of-the-art performance on benchmarks such as MathVerse, MMMU-Pro, and MuirBench, showcasing its importance in education and research.

The Willow quantum chip is the latest generation of quantum chips developed by Google’s Quantum AI team, achieving significant breakthroughs in quantum error correction and performance. This chip dramatically reduces errors associated with increasing qubit counts, addressing a key challenge that has persisted in the quantum computing field for nearly 30 years. Furthermore, Willow accomplished a standard benchmark computation in less than five minutes, which would take today’s fastest supercomputers 10^25 years—far exceeding the age of the universe. This achievement marks an important step toward building commercially viable large-scale quantum computers, which have the potential to revolutionize fields such as medicine, energy, and artificial intelligence.

OLMo-2-1124-7B-SFT is an English text generation model released by the Allen Institute for AI (AI2), serving as a supervised fine-tuned version of the OLMo 2 7B model, specifically optimized for the Tülu 3 dataset. The Tülu 3 dataset aims to achieve top performance across diverse tasks, including chat, math problem solving, GSM8K, IFEval, and more. Key advantages of this model include its robust text generation capability, versatile task handling, and the availability of open-source code and training details, making it a powerful tool for research and education.

Open Notebook is a powerful open-source note-taking and research platform that integrates artificial intelligence, designed for researchers, students, and professionals. It aims to enhance their learning and capabilities while providing complete control over workflow, models, and data usage and exposure. This product represents a new type of privacy-preserving learning tool that helps users organize notes, generate podcasts, and deeply understand learning content with AI technology, all while ensuring the protection of users' data privacy. Open Notebook is an open-source project that encourages community involvement and contributions to create a personalized intelligent partner for individual development.

The DOLMino dataset mix for OLMo2 stage 2 annealing training is a compilation of various high-quality data sources, designed for the second phase of training the OLMo2 model. This dataset encompasses diverse types of data such as web pages, STEM papers, and encyclopedic entries, aimed at enhancing model performance in text generation tasks. Its significance lies in providing rich training resources for the development of smarter and more accurate NLP models.

QwQ (Qwen with Questions) is an experimental research model developed by the Qwen team, aimed at enhancing artificial intelligence's reasoning abilities. It embodies a philosophical spirit, approaching every question with genuine curiosity and skepticism, seeking deeper truths through self-questioning and reflection. QwQ excels in mathematics and programming, particularly in addressing complex problems. Although it is still learning and evolving, it has already demonstrated significant potential for deep reasoning in technological domains.

SoraVids is an archive library for the Sora video generation model based on the Hugging Face platform. It includes 87 videos and 83 corresponding prompts that were publicly displayed before OpenAI revoked its API keys. All videos are in the MIME type video/mp4 with a frame rate of 30 FPS. The foundation of SoraVids is built on OpenAI's video generation technology, which allows users to create video content from text prompts. The significance of this archive lies in preserving videos generated before the API key revocation, thus providing valuable resources for research and education.

Nano Search is a tool designed to simulate human thinking processes, offering users a groundbreaking search experience through a slow thinking model supported by expert collaboration (CoE). By analyzing user search needs, it showcases existing text, images, and video content and allows for rewriting and creation of search results. This helps users gain a deeper understanding and exploration of information. The primary advantage of Nano Search lies in its ability to transform search results into creative resources, enabling video production and fundamentally redefining the concept and form of search engines.

Llama-3.1-Tulu-3-8B-DPO is a member of the Tülu3 model family focused on instruction adherence. It provides fully open-source data, code, and guidelines intended to serve as a comprehensive resource for modern post-training techniques. This model is designed for a wide range of tasks beyond conversation, such as MATH, GSM8K, and IFEval, achieving state-of-the-art performance. Key advantages include open-source data and code, support for multiple tasks, and excellent performance. The model was developed by the Allen AI Institute and adheres to the Llama 3.1 community licensing agreement for research and educational use.

Inquir is a powerful tool designed to create personalized search engines tailored to your data. It unlocks features such as custom search solutions, data aggregation, AI-driven Retrieval-Augmented Generation (RAG) systems, and context-aware search capabilities. Take the first step towards enhancing user experience by launching your engine or scheduling a demonstration.

FrontierMath is a mathematical benchmarking platform designed to test the limits of artificial intelligence in solving complex mathematical problems. Created by over 60 mathematicians, it spans the full spectrum of modern mathematics, from algebraic geometry to Zermelo-Fraenkel set theory. Each problem on FrontierMath requires expert mathematicians to invest hours of work, and even state-of-the-art AI systems like GPT-4 and Gemini can solve less than 2% of the problems. This platform provides a genuine assessment environment, with all problems being novel and unpublished, eliminating the common data contamination issues found in existing benchmarks.

CoI-Agent is an intelligent agent based on large language models (LLM), designed to revolutionize the development of new ideas in research through a Chain of Ideas approach. This model integrates and analyzes vast amounts of data, offering researchers innovative concepts and directions for their studies. Its significance lies in its ability to accelerate the research process, enhance research efficiency, and assist researchers in uncovering new patterns and relationships within complex datasets. Developed by the DAMO-NLP-SG team, CoI-Agent is an open-source project available for free use.