{"id":20947,"date":"2026-10-09T12:11:01","date_gmt":"2026-10-09T12:11:01","guid":{"rendered":"https:\/\/news.theck1.no\/?p=20947"},"modified":"2026-10-09T12:11:01","modified_gmt":"2026-10-09T12:11:01","slug":"this-tool-turns-scientific-articles-into-agents-that-answer-questions-and-collaborate","status":"publish","type":"post","link":"https:\/\/news.theck1.no\/?p=20947","title":{"rendered":"This Tool Turns Scientific Articles Into Agents That Answer Questions and Collaborate"},"content":{"rendered":"

AI-rewritten:<\/strong> This is a summary of an article from Singularity Hub, rewritten by AI (Qwen, running locally) to make it easier to read. The facts come from the original article – read it for the full story.<\/p>\n

Singularity Hub • Shelly Fan • October 8, 2026<\/strong><\/div>\n\n

James Zou of Stanford University introduced Paper2Agent, a system designed to transform static scientific papers into interactive AI agents. Unlike standard chatbots that summarize text, these agents are trained on a paper’s full content, including figures and data, to reproduce its experimental results in a virtual environment. This process allows the AI to act as a "virtual author" capable of answering complex questions, applying methods from one study to new datasets, and collaborating with agents from other disciplines.<\/p>\n

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The project aims to address challenges like the overwhelming volume of scientific publishing and low reproducibility rates. While only 39 percent of psychology studies in a 2015 project yielded consistent results, Paper2Agent attempts to verify claims by running experiments. The system scans papers to capture details like experimental setups and stores them in a digital vault called an MCP server. This enables scientists to link their own large language models to the agent for deeper analysis rather than simple summarization.<\/p>\n

In testing, the tool successfully converted 76 percent of 110 papers into working agents, with failures often indicating missing code or data. One agent based on AlphaGenome answered genetics questions with near-perfect accuracy and was significantly faster than other AI scientists. Furthermore, multiple agents collaborated to identify new genetic variants linked to conditions like psoriasis and cholesterol issues, demonstrating how cross-disciplinary research could be automated without human matchmaking.<\/p>\n

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Source:<\/strong> Singularity Hub • Shelly Fan • October 8, 2026<\/p>\n

Read the original article at Singularity Hub →<\/a><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

AI-rewritten: This is a summary of an article from Singularity Hub, rewritten by AI (Qwen, running locally) to make it easier to read. The facts come from the original article – read it for the full story. Singularity Hub • Shelly Fan • October 8, 2026 James Zou of Stanford University introduced Paper2Agent, a system<\/p>\n

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