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@lanyon_ai

@lanyon_ai on X

2 captures, most recent first. Transcribed by hand from screenshots — see the timeline for what that means.

@lanyon_ai

— web clipping, 230 words — published 2026-08-31

Post by @lanyon_ai on X

Formally verified magnetohydrodynamics, with provably hyperbolicity-preserving magnetic divergence error correction. Simulating plasma vortices and supersonic plasma flows in arbitrary geometries using shock-capturing schemes for the equations of ideal MHD, all implemented and verified autonomously with Lanyon. ~32,000 lines of C, ~50,000 lines of Lean proof, ~150 correctness theorems, all generated in ~7 minutes. The ideal MHD equations couple the equations of hydrodynamics with Maxwell’s equations for electromagnetism to describe the interactions of perfectly conducting fluids with magnetic fields. As an elegant reduced-order description, the ideal MHD equations occur ubiquitously throughout heliophysics, geophysics, and astrophysical plasma physics, as well as in modeling the equilibrium and stability of nuclear fusion reactors. Solving these equations involves unique numerical and algorithmic challenges, including issues surrounding the preservation of divergence constraints for the magnetic field, as well as complex thermodynamic stability and wave-speed restrictions. Lanyon one-shotted an end-to-end formally verified solver for the ideal MHD equations, including a provably hyperbolicity-preserving divergence correction scheme based on the Dedner et al. approach, with fully executable mathematical, physical, and numerical correctness proofs. Then it ran a variety of complex numerical and physical validation simulations, and autonomously analyzed the results. To the best of our knowledge, this is the first ever fully formally verified divergence correction scheme for the MHD equations. GitHub link and technical deep dive below.👇 --- Technical deep dive by @minmodammar: https://lanyon.ai/research/ideal-mhd/… Corresponding GitHub repository: https://github.com/lanyonai/idealMHD…

@lanyon_ai

— saved image

Our second official benchmarking post is out! The Euler equations may *seem* easy to solve using finite volume methods, but all frontier models (including GPT-5.6 Sol, Fable 5, and Kimi K3) consistently introduce both subtle and unsubtle errors, including numerical oscillations, thermodynamic inconsistencies, and incorrect orders of accuracy. That is, if the code even works at all. Mathematical misformalizations abound, and token costs can easily hit tens of dollars per attempt.

Only Lanyon's neurosymbolic architecture is consistently able to produce robust solvers with end-to-end proofs of correctness, and it does so with costs that are >100x lower. Post below 👇
Note from Claude Sonnet 5

Tweet from Lanyon AI (@lanyon_ai) promoting a benchmarking post comparing frontier LLMs against their neurosymbolic architecture on Euler-equation finite-volume solver generation. Below the text are two density-contour plots of a 2-D Riemann problem (t=0.8, 800x800 grid) comparing a flawed solution against a 2nd-order minmod wave-propagation (Rusanov) solution, each with a colorbar.

ai benchmarkingneurosymbolic ainumerical methodsllm evaluation