Neuro-Symbolic Architecture Using Thermodynamic Refinement-Free Energy Principle

I'm really hoping to get some feedback on this paper.

The core novelty of this architecture is using Thermodynamic Refinement (minimizing a global free energy functional) to ensure logical consistency.

However, treating thought generation as a 'Thermodynamic Settling' process is proving to be a massive compute bottleneck. We are looking for feedback on whether Energy-Based Models (EBMs) can ever scale for real-time agents, or if we are stuck with slow inference forever.

Hoping to hear from some brilliant minds.

We’ve been working on solving the ARC-AGI benchmark and found that standard Transformers hit a hard ceiling on algorithmic search tasks (the "Compositional Drift" problem mentioned in the abstract).

We decided to try a different architectural approach: The Dual-Stream Programmatic Learner (DSPL).

Instead of one monolithic model, we use a Bicameral Latent Space:

1. Logic Stream: A recursive planner that handles abstract algorithmic planning. 2. Canvas Stream: An execution state that handles the pixel grid.

The Engineering Bottleneck:While this separation solves the reasoning drift (accuracy is high), the inference cost of the recursive loop is proving difficult to scale.

We are currently using a Gated Cross-Attention Interface to sync the two streams at every step, but this <$O(N^2)$> sync cost is melting our servers under load.

My question to the HN crowd: For those working on dual-stream or "System 2" architectures—is strictly synchronous Cross-Attention necessary? Has anyone successfully decoupled the "Planner" loop from the "Executor" loop (running them asynchronously) without breaking causality?

We are debating switching to a Linear Attention mechanism (like Mamba) for the interface, but worried about losing the "Sacred Signature" type-safety.

Paper link is above. Happy to discuss the trade-offs of Recursion vs. Depth.