arXiv cs.AI· Bohan Liu, Xiaohan Fan, Wenqian Ye, Aidong Zhang·· 13 小时前AI 评分31
用稀疏自编码器追踪并验证 ESMFold 主干中的折叠机制
Structure, Not Sequence: Tracking and Validating Folding Mechanisms in ESMFold's Trunk with Sparse Autoencoders
AI 导读
研究用稀疏自编码器（SAE）解释 ESMFold 折叠主干的内部计算，发现单表示承载继承的语义、配对表示计算成对几何。团队用推理语言模型大规模解读字典，并通过梯度归因追踪概念跨块组合，验证计算与已知生物化学的一致或偏离之处。这些稀疏字典捕捉到主干在序列模型之外计算的关系几何，其概念激活还能预测模型自身的折叠质量。
正文 · 原文
Abstract:Structure predictors like ESMFold fold proteins using neural network computation and have transformed structural biology. However, the driving mechanism from amino-acid (AA) sequence to 3D structure is insufficiently understood, and biological meaning remains locked in the dense network. Sparse autoencoders (SAEs) have shown promising results in obtaining interpretable concept features over an overcomplete basis of intermediate protein sequence-model representations. Yet folding models, which predict physical shape from AA sequence semantics, have no such account. We hypothesize that the features carrying the folding computation evolve in the 3D structural regression process. In this paper, we interpret the folding trunk in three steps. First, a pilot analysis on the representation distribution across modules and recycles locates distinct spatial computation in the trunk. Second, the dictionaries expose a division of labor: the single representation carries inherited semantics, and the pair representation computes the pairwise geometry. Third, we interpret the dictionary at scale with a reasoning language model, trace how the concepts compose across blocks by gradient-based attribution, and follow the interpreted code to the model's folding output, verifying where the computation aligns with, or departs from, known biochemistry. Together, the sparse dictionaries we train capture the relational geometry the trunk computes beyond the sequence model, and their concept activations forecast the model's own fold quality, turning the black-box folding trunk into a sparse, interpretable, and scientifically verifiable circuit.
| Subjects: | Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.36226 [q-bio.QM] |
| (or arXiv:2610.36226v1 [q-bio.QM] for this version) | |
| https://doi.org/10.48550/arXiv.2610.36226 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Bohan Liu [view email]
[v1]
Mon, 26 Oct 2026 20:04:57 UTC (1,204 KB)
来源：arXiv cs.AI · arxiv.org