Find papers helps you discover relevant research using an LLM-powered search experience that mirrors the multi-step reasoning process of expert researchers. It reformulates queries, follows citations, and explains why each paper is relevant, making it easier to find exactly what you're looking for—even when keywords fail.

Summarize literature turns complex research questions into structured, comprehensive summaries—every claim backed by a clickable citation and often an inline excerpt. It scans millions of abstracts and full-text papers, clusters evidence, and distills findings into clear sections that highlight key results, disagreements, and open questions.

Analyze data turns natural language questions into structured, reproducible analyses. It explores your dataset, generates hypotheses, runs statistical tests, and explains the results—making data-driven discovery faster, clearer, and accessible across disciplines. [information updated on 2025.09.06]

Consensus uses language models (LLMs) and purpose-built search technology (Vector search) to surface relevant papers. The current source material used comes from the Semantic Scholar, OpenAlex and Consensus’s own crawl of the scholarly web. Each search goes through three main steps: step 1 - combining semantic search (AI embeddings) with keyword search (BM25); step 2 - re-ranking the top 1,500 articles based on publication recency, citation count, and journal impact and reputation; step 3 - re-ranking the top 20 articles, with the same three main research quality signals, but using a larger, more powerful AI model. Find more details about the tool. [information updated on 2025.06.17]

Elicit searches across over 126 million academic papers from the Semantic Scholar corpus across all academic disciplines, including preprints (from arXiv, bioRxiv, and other repositories). It also includes non-open-access papers. Elicit answer will be based on the full text of the paper if it is available via open access, otherwise Elicit will answer based on just the abstract. Find more details about the tool. [information updated on 2025.03.07]

The platform has four agents: Crow – concise search: produces a succinct answer citing scientific data sources, good for API calls and specific questions (built with the in house open-source AI tool PaperQA2); Falcon – deep search: produces a long report with many sources, good for literature reviews and evaluating hypotheses; Owl – precedent search: formerly known as HasAnyone, good for understanding if anyone has ever done something in science; and in an experimental mode, Phoenix – chemistry tasks: a new iteration of ChemCrow, Phoenix uses cheminformatics tools to do chemistry. Good for planning synthesis and designing new molecules. Find more details about the tool. [information updated on 2025.07.18]

ORKG Ask employs a sophisticated pipeline to ensure you receive the most relevant information from the vast CORE dataset of research articles. The system uses an embedding model to encode your query and searches for semantically similar research articles using a vector store with an Approximate Nearest Neighbor (ANN) algorithm. ORKG Ask leverages the following advanced technologies to deliver its robust search and extraction capabilities: Qdrant vector store, Nomic embedding model and Mistral Instruct 7B v0.2 large language model. Find more details about the tool. [information updated on 2025.03.07]

Perplexity uses cutting-edge language models like GPT-4 Omni and Claude 3 to understand the queries context and nuances. It searches the internet in real time, gathering insights from diverse sources. In addition to the standard search mode, Perplexity also have a 'Deep Research' mode (in-depth report on complex topics). Find more details about the tool. [information updated on 2025.06.17]

SciSpace sources research articles and related data from multiple platforms including Semantic Scholar, OpenAlex, Google Scholar, and Microsoft Academic Graph. It uses GPT 3.5 for basic plans and GPT 4o for other plans. Scispace's search engine uses a combination of semantic and text-based methods, focusing on titles and abstracts. Citation analysis is not used in the ranking of selected articles. Find more details about the tool. [information updated on 2025.02.27]

Undermind covers all fields of science using the Semantic Scholar database. It employs a blend of lexical or keyword search and embedding-based vector or semantic search. Instead of a quick search, Undermind does a more thorough search in several steps that can take up to 8 minutes. It uses a search algorithm that, after finding relevant content, adapts to conduct successive keyword and citation searches. Additionally, it directly employs large language models like GPT-4 to classify papers into three levels of relevancy. Find more details about the tool. [information updated on 2024.12.11]