Dblp Veracity An Open Source Ai Fact Checking System

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Please note: Providing information about references and citations is only possible thanks to to the open metadata APIs provided by crossref.org and opencitations.net.

If citation data of your publications is not openly available yet, then please consider asking your publisher to release your citation data to the public. For more information please see the Initiative for Open Citations (I4OC). Please also note that there is no way of submitting missing references or citation data directly to dblp. Please also note that this feature is work in progress and that it is still far from being perfect. That is, in particular, JavaScript is requires in order to retrieve and display any references and citations for this record.

references and citations temporaily disabled To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q. A streamlined web application that analyzes claims to determine their truthfulness through evidence gathering and analysis, supporting efforts in misinformation detection.

AskVeracity is an agentic AI system that verifies factual claims through a combination of NLP techniques and large language models. The system gathers and analyzes evidence from multiple sources to provide transparent and explainable verdicts. AskVeracity is built with a modular architecture: Option 1: Using Streamlit secrets (recommended for local development) Push your code to the Hugging Face repository or upload files directly through the web interface Describe your interests as you like, be it keywords or sentence, you’ll get relevant papers every day via AI semantic matching.

IJCAI, ICML, CVPR, KDD... Access papers from top AI conferences, all in one tool, for free. Keep your research organized with our bookmarking feature. Build a well-structured knowledge hub at your fingertips. Got questions? Ask in ChatDOC with one single click.

Use AI to pull data, clarify terms, and verify facts with our precise word-level tracing feature. Please note: Providing information about references and citations is only possible thanks to to the open metadata APIs provided by crossref.org and opencitations.net. If citation data of your publications is not openly available yet, then please consider asking your publisher to release your citation data to the public. For more information please see the Initiative for Open Citations (I4OC). Please also note that there is no way of submitting missing references or citation data directly to dblp. Please also note that this feature is work in progress and that it is still far from being perfect.

That is, in particular, JavaScript is requires in order to retrieve and display any references and citations for this record. references and citations temporaily disabled To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser.

For more information see our F.A.Q. AI content detectors promise a simple answer to a messy problem: tell me if this text was written by a human or by artificial intelligence. Teachers want to protect academic honesty. Editors and SEO teams want to spot low quality AI-written content before it hits a blog post or web page. Founders want to keep AI writing tools from quietly taking over the writing process without anyone noticing. The problem is that most people have heard horror stories about false positives.

A student turns in an original essay and gets an “AI score” that says 98% likely AI-generated. A content creator writes a genuine article, then an aggressive AI text detector tells a client it is “probably AI.” That is not just annoying, it can be reputation damaging. So I decided to test what is actually working in 2026. I generated AI-written text with three major large language models:

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