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Machine-learned amplitude simplification

The amplitudes can be calculated with established methods, but their resulting expressions can have hundreds of terms.

Progress records 1

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1 record · How we rate progress

Neural networks produce compact scalar and graviton scattering expressions

Cheung, Dersy and Schwartz report a transformer-based simplifier that reduces long spinor-helicity expressions and produces new compact five-point scalar and graviton amplitude expressions, alongside recovering the known five-gluon Parke–Taylor expression.

Read original source Original amplitude-simplification paper v2arxiv.org

Original report: 2024-08-08

Understanding the problem

Read the background

The amplitudes can be calculated with established methods, but their resulting expressions can have hundreds of terms. Rediscovering Parke–Taylor is a reproduction; the new compact representations and simplification method are the advance.

Why it matters

A reusable symbolic-calculation method with new compact amplitude representations; the contribution concerns expression structure rather than a new physical law.

What counts as progress

A sourced analytical result, substantive numerical advance, or useful calculation method with its physical model, approximation, baseline, and AI contribution stated.

What to watch for

  • Compare the same physical model, kinematic region, and approximation.
  • The pipeline validates reductions numerically at sample momenta; that is not a formal proof of every identity. Generalization and success rates depend on the tested expression families. AIProofWatch has not run the models or audited the identities.
  • Track independent checks and subsequent corrections separately from importance.

Background review: 2026-10-13. Selected sources:

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