All modules of the proposed architecture are fully differentiable.

Identify the architecture proposed in the June 2023 source document (likely the modular autonomous-intelligence / latent-variable energy-based architecture described in LeCun's work). TRUE: every module the document describes (e.g., perception, world model, cost, critic, actor, configurator) is a differentiable function, so gradients can flow through all of them. MOSTLY TRUE: all core modules are differentiable and only a minor or optional component is not, or needs a standard relaxation. MIXED: several named modules are differentiable and several are not. MOSTLY FALSE: the core design depends on non-differentiable steps (e.g., discrete search or discrete latent variables) for key modules, though some modules are differentiable. FALSE: the architecture as described has mainly non-differentiable modules.

Set before the research began.

1. E1 Yann LeCun on a vision to make AI systems learn and reason like animals and humans Meta AI · 23 February 2022 Own record

   “LeCun proposes an architecture composed of six separate modules. Each is assumed to be differentiable, in that it can easily compute gradient estimates of some objective function with respect to its own input and propagate the gradient information to upstream modules.”

2. E2 Safety Implications of LeCun's path to machine intelligence — LessWrong LessWrong (post by Ivan Vendrov) · 15 July 2022

   “With the exception of the Actor and the Intrinsic Cost (see below) they are all deep neural networks trained with gradient descent.”

3. E3 Yann LeCun on a vision to make AI systems learn and reason like animals and humans Meta AI · 23 February 2022 Own record

   “Additionally, the information content of the latent variable is minimized by making it discrete, low-dimensional, sparse, or noisy.”

4. E4 Yann LeCun on a vision to make AI systems learn and reason like animals and humans Meta AI · 23 February 2022 Own record

   “But many other aspects of the architecture are still to be defined, including how precisely to train the critic, how to construct and train the configurator”

Meta AI's summary of LeCun's six-module architecture says that each module is assumed to be differentiable and can pass gradient estimates to upstream modules (E1). The independent LessWrong summary says that every module except the Actor and the Intrinsic Cost is a deep neural network trained with gradient descent (E2). That marks those two modules as not learned networks, but it does not say they are non-differentiable. Two qualifications apply: the latent variable may be made discrete, which would need a relaxation (E3), and the configurator and critic training had not yet been worked out (E4). So the design treats the core modules as differentiable, but full differentiability of every component is not fully established.