The allosteric networks in Bravi et al.'s DCA study are evolved by Monte-Carlo optimization, not trained by coupled learning

A scope distinction that matters for how Bravi, Ravasio, Brito, and Wyart's DCA result (claim-bravi-2020-applies-dca-to-evolved-allosteric-networks-as-synthetic-msa) connects to the vault's physical-learning cluster: the allosteric networks it studies are produced by an in-silico evolutionary search, not by a local physical learning rule. The paper's own description — "Such networks are evolved by changing the position of springs according to a Metropolis-Monte-Carlo routine to maximize F" — places the method in the evolved/designed elastic-network tradition of Yan, Ravasio, Brito & Wyart (2017, 2018) and of Rocks et al. (PNAS 2017), which the paper cites as reference [27] and which the vault already holds as claim-removing-one-percent-of-bonds-makes-a-random-network-allosteric.

That is a different mechanism from coupled learning, the local contrastive rule in which each spring updates its own rest length using only its free-vs-clamped response, with no external optimizer and no global error signal (claim-coupled-learning-elastic-networks-compute-without-a-processor). Both produce networks that perform allostery, and both may land in the same soft-mode structural regime (claim-physical-networks-become-what-they-learn-soft-modes) — but a global fitness-maximizing search over spring configurations and a decentralized in-situ learning rule are not the same process, even when they reach a similar artifact.

The distinction guards against overstating the bridge. Read broadly — "physically-instantiated network models of allostery evolved or optimized for mechanical function" — this DCA study does bridge the vault's mechanical-allostery lineage to protein-sequence coevolution. Read in the narrower, technical sense the vault reserves for coupled learning, it does not test that specific class of network. Whether reaching the same structure by different routes implies anything shared beyond the structure is the standing question of backpropagation-gap.