This question resolves YES if, at any point prior to December 31, 2037, all four of the following conditions are met. Otherwise, the question resolves NO.

Attribution & Origin

An artificial intelligence system (an autonomous model, algorithm, or AI agent) must generate, derive, or synthesize the core mathematical framework, field equations, or conceptual mechanism of a novel physics theory.

Publication & Attribution Requirement: The theory must be published in a peer-reviewed journal recognized by the Web of Science (e.g., Physical Review Letters, Physical Review X, Nature, or Science).

Compliance with Publishing Standards: Because major academic publishers prohibit listing non-human software as a formal paper author, the AI model does not need to be listed as a co-author. However, the main text, Methods section, or Formal Acknowledgments of the published paper must explicitly credit the AI system as the primary source or generator of the core theoretical mechanism.

Quantitative Novelty

The theory must make a quantitative prediction that differs significantly from the Standard Model of Particle Physics and/or standard $\Lambda\text{CDM}$ cosmological model. The prediction could fall into at one of these domains:

• A particle decay mode, cross-section, or branching ratio (e.g., specific proton decay channels like $p \to K^+\bar{\nu}$)

• A cosmological signal (e.g., stochastic gravitational wave background spectral index, CMB non-Gaussianity features)

• A fundamental parameter, coupling constant, or anomaly shift (e.g., resolving the Hubble tension or predicting an explicit electron Electric Dipole Moment)

• A topological defect or relic particle signature (e.g., magnetic monopole mass bounds, dark matter interaction thresholds)

I will evaluate other domains as they become relevant but this should give market participants a good idea of the class of predition I am thinking of.

Empirical Verification

An independent experimental collaboration (e.g., CERN/LHC, Hyper-Kamiokande, LEGEND, LISA, NANOGrav, JWST, or an equivalent peer-reviewed experimental physics facility) must publish experimental data confirming the AI's prediction. The confirmation must satisfy either of the following standard physics benchmarks:

• Positive Signal Discovery: A statistically significant discovery reaching at least the $5\sigma$ threshold ($p < 2.87 \times 10^{-7}$).

• Precision/Constraint Verification: A measured physical parameter or upper/lower bound that falls within the AI theory's specifically predicted confidence interval, in a region where standard physics predicted a different parameter range or a null outcome.

Post-dictions and Data Leakage: The prediction must be published before the experimental data confirming it is publicly released or presented at a conference. Theories derived by fitting existing, unreleased, or preliminary public datasets do not qualify.

Mathematical Re-wrapping: Reframing an existing theoretical framework (e.g., re-deriving known Standard Model predictions using new tensor notation or alternative vector spaces) without predicting a novel physical outcome does not qualify.

Pure Numerical Simulations: Standard computational simulations running known laws of physics (e.g., lattice QCD calculations) do not qualify. The AI must propose new physics.

Resolution Source & Edge-Case Handling

Primary Sources: Official press releases and peer-reviewed confirmation papers from the experimental physics collaboration, alongside primary literature (Nature, Science, APS Physics).

Disputes on Credit: If there is controversy regarding whether the human authors or the AI conceived the core idea, resolution will rely on the explicit methodology statement in the primary peer-reviewed paper. If the human authors state the AI was merely used for copyediting, literature formatting, or basic code execution, the market resolves NO.