Thermo-NN optimizes model architecture via thermodynamic cost

A project named Thermo-NN proposes optimizing neural network architecture by minimizing thermodynamic information cost based on Landauer's principle, rather than relying on the standard FLOPs and parameter count tradeoff.

The approach utilizes a causal derivation step called CAMOS before mapping results to hardware. It posits that unnecessary information destruction within a network may contribute to alignment failure, suggesting that preserving physical information could serve as a useful constraint alongside interpretability methods.

Currently, the project presents only a conceptual pipeline with no published benchmarks or performance numbers, so it is considered an interesting idea worth watching rather than proven technology.