What question did this study set out to answer?

The aim is to develop PIR, a symbolic regression engine, to automate the discovery of physical laws from data.

July 9, 2026Open Access

PIR: Physics Intermediate Representation for Automated Discovery of Physical Laws

Key Points

The aim is to develop PIR, a symbolic regression engine, to automate the discovery of physical laws from data.
Utilized a monomial-basis log-linearization gate to identify power-law structures.
Employed pairwise structure decomposition and RANSAC for analysis.
Incorporated sparse regression techniques and an Occam complexity penalty.
PIR recovered 12 out of 44 equations exactly with zero wobble across seeds.
An additional 12 equations were recovered in functional form with transcendental constants as decimals.
Identified structural limits, including a transcendental wall with a 0/18 recovery rate.

Abstract

We present PIR (Physics Intermediate Representation), a classical symbolicregression engine for automated discovery of physical laws from data. PIR uses amonomial-basis log-linearization gate (F3) to detect power-law structure, combinedwith pairwise structure decomposition, RANSAC, sparse regression, and an Occamcomplexity penalty. On the Feynman Symbolic Regression Benchmark (Tier A, blindprotocol), PIR recovers 12/44 equations exactly (zero wobble across seeds, v3. 4). Asecondary 12/44 equations are recovered in correct functional form withtranscendental constants folded as decimals (FORMNUMERIC), reported separatelyand never summed into the primary figure. We characterize two hard structural limits: atranscendental wall (F2 class, 0/18 recovery, mechanism proven) and a log-spacefailure mode for sum-of-products laws (closed negative result). These negatives are asinformative as the positives: they define PIR's recovery envelope precisely and point toconcrete next steps.

Read Full Paperexternally

Ask AI

Helpful

Bookmark

View Full Paper