aiqbench analysis
This study applies statistical mechanics principles to transformer models, revealing that intermediate sampling temperatures optimize learning dynamics and free-energy prediction in protein structure tasks. Unlike standard networks lacking attention mechanisms, these transformers exhibit a continuous loss landscape where specific temperature ranges preserve layer parameters while enhancing contact map accuracy. The findings offer a novel optimization strategy for leveraging AI to solve complex biomolecular problems without relying on traditional gradient descent alone.
Sampling at intermediate temperatures is optimal for training large language models in protein structure prediction
arXiv:2603.29529v2 Announce Type: replace-cross Abstract: Using a statistical mechanics framework, we investigate the parameter space of transformer models trained on protein sequence data. We sample the loss landscape at varying temperatures using Langevin dynamics to characterize the low-loss manifold, and to understand the mechanisms underlying transformers' superior performance in protein structure prediction. We find that, at variance with networks not based on the attention mechanism, the lack of a first--order--like transition in the loss of the transformer produces a range of intermediate temperatures with good learning properties; this is true both for synthetic and natural protein sequences. We also show that the parameters of most layers are highly conserved at these temperatures if the dimension of the embedding is optimal, and we provide an operative way to find this dimension. Additionally, we show that the attention matrix is more predictive of the contact maps of the protein at higher temperatures and for higher dimensions of the embedding than those optimal for learning. Finally, we showed that the models sampled at intermediate temperatures can predict the free-energy variation upon mutation, better than models obtained through standard optimization techniques.
Abstract:Using a statistical mechanics framework, we investigate the parameter space of transformer models trained on protein sequence data. We sample the loss landscape at varying temperatures using Langevin dynamics to characterize the low-loss manifold, and to understand the mechanisms underlying transformers' superior performance in protein structure prediction. We find that, at variance with networks not based on the attention mechanism, the lack of a first--order--like transition in the loss of the transformer produces a range of intermediate temperatures with good learning properties; this is true both for synthetic and natural protein sequences. We also show that the parameters of most layers are highly conserved at these temperatures if the dimension of the embedding is optimal, and we provide an operative way to find this dimension. Additionally, we show that the attention matrix is more predictive of the contact maps of the protein at higher temperatures and for higher dimensions of the embedding than those optimal for learning. Finally, we showed that the models sampled at intermediate temperatures can predict the free-energy variation upon mutation, better than models obtained through standard optimization techniques.
Disordered Systems and Neural Networks (cond-mat.dis-nn); Machine Learning (cs.LG); Biomolecules (q-bio.BM)
arXiv:2603.29529 [cond-mat.dis-nn]
(or arXiv:2603.29529v2 [cond-mat.dis-nn] for this version)
https://doi.org/10.48550/arXiv.2603.29529 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 282 words
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