The FERMIACC: Agents for Particle Theory
Prateek Agrawal, Nathaniel Craig, Amalia Madden, I\~nigo Valenzuela Lombera
TL;DR
The paper introduces FERMIACC, an autonomous reasoning system using OpenAI agents to generate and validate theoretical hypotheses in high energy physics data analysis.
Contribution
It presents a novel scaffolded reasoning model that enables autonomous hypothesis generation and validation in particle physics.
Findings
Successfully generated plausible physics hypotheses
Validated theories quantitatively at scale
Demonstrated effectiveness of autonomous reasoning in physics
Abstract
We present the FERMIACC, a scaffolded reasoning model built on OpenAI agents designed to autonomously generate and quantitatively validate theory hypotheses for high energy physics data at scale.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMulti-Agent Systems and Negotiation · AI-based Problem Solving and Planning · Computational Physics and Python Applications