Newton (contributed to the Linux Foundation in September 2025, built on NVIDIA Warp and OpenUSD) exposes multiple solvers (Euler, Featherstone, XPBD, and others) behind one differentiable interface, so a simulation built on it inherits backprop-through-contact for free, the same way ferromotion's tape gives every lesson in this course a gradient. NVIDIA's own GTC 2026 benchmark reports Newton reaching 475× MuJoCo-XLA (MJX) on manipulation tasks and 252× on locomotion, on an RTX PRO 6000 Blackwell GPU. Read that number exactly as labeled: it is a vendor's own comparison, on its own new hardware, of raw simulation throughput, not an independently replicated figure, and not a claim about gradient accuracy or sim-to-real fidelity. What is not in question is the design choice underneath it: differentiable-by-construction physics, industrialized. Two papers ride the same idea on MJX directly, on real hardware. 'Achieving Precise and Reliable Locomotion with Differentiable Simulation-Based System Identification' (arXiv:2508.04696) folds identification into the RL training loop, fitting a legged robot's mass, inertia, and a neural friction correction from trajectories alone, no torque sensor, and reports a real-hardware 75% cut in rotational drift and a 46% gain in commanded-direction travel versus its RL baseline. 'Trajectory-based actuator identification via differentiable simulation' (arXiv:2604.10351) fits an effective actuator model straight from motion trajectories by backpropagating through MJX, closing the actuation gap without instrumenting the motor. Both are the capstone's Stage 1, identify a plant by differentiating a rollout against data, run on a real robot instead of a simulated point mass. OrbiSim (arXiv:2605.16395, a May 2026 preprint, part of the NeoWorld project) makes the architectural case explicit: a generative video model predicting the next frame pixel-by-pixel has no obligation to conserve momentum or respect contact, while a world model built as a differentiable physics engine (explicit state, explicit dynamics, a renderer bolted on after) inherits the physics for free and stays a model you can identify and control by gradient. That is an external paper's claim, reached independently of this course; the note is that it converges with, rather than originates, the structure-as-differentiation thesis this course has been building since Module 1. The Bench's own numbers below are a ferromotion nano-simulation, run on your device for this lesson, illustrative of the method, not a claim of scale or of replicating any cited paper's hardware result.