A free AI-assisted scholarly search service from Ai2, combining paper discovery, citation connections, personal research organization and reading assistance on supported papers.
Semantic Scholar is an AI-driven academic search and discovery project within Ai2. It organizes scholarly records and uses machine learning to surface relationships and useful context. It is particularly helpful when you know a topic or a seed paper but need a manageable route into the surrounding literature.
Its role is discovery and reading assistance. A useful result may still be a preliminary study, an outdated method or a paper that answers a different question. Treat summaries and citation signals as ways to choose what to read, then evaluate the original source before incorporating its claims into your work.
Begin a literature review
Check a technical claim
Follow an active topic
Prepare a reading group
Official product image. Click to inspect the details.
Write a specific search question
Screen paper records
Open the original evidence
Save a structured reading note
Example prompt or task: Find papers addressing this specific research question. For each promising result, record its method, evaluation setting and limitation after checking the original paper; keep uncertain claims separate from verified findings.
This is an editorial research workflow, not a claim that the search service performs a complete systematic review or validates every scientific result.
Search and discovery are free. Finding a paper record does not remove a publisher paywall or guarantee an available PDF.
Semantic Scholar describes its search and discovery tools as free. Personal organization and notification features use an account, while the underlying scholarly records can be explored without treating account creation as a paid subscription.
Full-text availability depends on publishers, open-access copies and institutional access. API keys, rate limits and dataset terms are separate from ordinary browsing. This listing does not promise free access to every paper or unrestricted reuse of every indexed document.
Its scholarly search and discovery service is free. Publisher access can be separate.
Basic paper access does not require an account; saving, feeds and alerts use account features.
No. Some records lead to publisher or institutional access options, and some lack a full-text link.
No. Availability and domain coverage are limited.
No. The enhanced reader and individual features apply to supported papers.
No. Read the paper and the context of the citations.
Official APIs and datasets exist, with their own conditions and limits.
Explore platforms, inputs and outputs, licensing, and access requirements.
The service is accessed through a browser. Search results and paper records can lead to publisher pages, PDFs or metadata-only records, so the amount of evidence available differs by paper.
Semantic Reader is documented for supported papers, especially parts of the arXiv collection, with some features limited further by language or field. An account library can provide context for personalized features, but it does not make the coverage universal.
Search topics and saved libraries can reveal research interests. Review account, alert and sharing settings according to the sensitivity of your work, and distinguish a private reading workflow from publicly shared material.
AI-generated summaries and reading aids can contain errors. Verify important claims in the original text, retain attribution and respect the separate rights attached to full-text documents.
Reviewed October 3, 2026. Product facts come from the official sources below. Suggested projects, prompts and review methods are FindGoodAI editorial guidance, not measured performance results.
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