FutureHouse
FutureHouse is a nonprofit AI research lab focused on automating scientific discovery in biology and other complex sciences. FutureHouse features superintelligent AI agents designed to assist scientists in accelerating research processes. It is optimized for retrieving and summarizing information from scientific literature, achieving state-of-the-art performance on benchmarks like RAG-QA Arena's science benchmark. It employs an agentic approach, allowing for iterative query expansion, LLM re-ranking, contextual summarization, and document citation traversal to enhance retrieval accuracy. FutureHouse also offers a framework for training language agents on challenging scientific tasks, enabling agents to perform tasks such as protein engineering, literature summarization, and molecular cloning. Their LAB-Bench benchmark evaluates language models on biology research tasks, including information extraction, database retrieval, etc.
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moara
Moara is an AI-powered research management platform that helps academics, scientists, and librarians streamline their literature review process. Designed around librarian-led best practices, it supports both narrative and systematic reviews through guided workflows and automation. Researchers can easily import papers from Zotero, citation files, or PDFs, accessing over 200 million publications to build a structured library. AI automatically extracts metadata, tags papers, and assists with triage, annotation, and evidence synthesis. Collaboration tools allow teams to assign papers, track progress, and comment in real time, ensuring efficiency and consistency. With Moara, researchers can conduct comprehensive, organized, and reproducible reviews faster than ever before.
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Sciscoper
Sciscoper is an AI powered research assistant that is used to streamline and accelerate the literature review process for STEM researchers, academics, and R&D teams. Researchers often deal with hundreds or thousands of scientific papers scattered across different sources, making it difficult to extract meaningful insights efficiently.
Sciscoper solves this by using AI and natural language processing to automatically:
Summarize scientific papers and research findings.
Extract key insights, concepts, and relationships across documents.
Generate literature reviews with citations in multiple reference styles.
Organize and index papers into a structured, searchable knowledge base for easy discovery.
This allows users to focus less on manual reading and note-taking, and more on analyzing results, identifying research gaps, and producing new scientific knowledge.
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Connected Papers
Connected Papers is a visual tool designed to assist researchers and applied scientists in discovering and exploring academic papers pertinent to their field of work. By inputting a "seed paper," users can generate a graph that displays related papers based on a similarity metric derived from co-citation and bibliographic coupling analyses. This approach allows for the identification of relevant literature, even when direct citations are absent. The resulting graph provides a visual overview of the research landscape, highlighting seminal works and potential areas for further exploration. Connected Papers aims to streamline the literature review process, making it more efficient and comprehensive for researchers.
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