Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
Exact (symbolic, no sampling) Weingarten calculus for networks of Haar-random SO(m) matrices, via two structurally independent computation engines (a "cut and glue" symbolic surgery engine, and a "direct index sum" engine using union-find and bucket elimination). This is the reusable computational core extracted from a research project studying the Weingarten calculus underlying Nissim's SO(3) deconfinement mechanism and its generalizations (see the related technical notes below). It computes exact expectation values of products of traces of Haar-random SO(m) matrices whenever every edge in the network occurs exactly 2, 4, or m times -- the three cases with a known closed-form Weingarten identity. Implemented and tested for SO(m), m >= 3 (numerically exercised for m = 3, 5, 7); SU(N) is not implemented. Ships with a known-answer test suite (classical Haar moments, two real regression cases from development, and a cross-check that both engines agree). MIT licensed. Deliberately does not include the tube-specific geometry that originally used this engine -- this is the generic calculus layer, independent of any one application. See the included README.md for full documentation, scope, and a worked example.
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Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 9, 2026
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
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