arXiv cs.AI· Paarth Batra, Aryan Keluskar·· 1 天前AI 评分34
蛋白质语言模型在不变重标记下无法保持接触预测能力
Protein Language Models Fail To Capture Contact Predictions Under Invariant Relabeling
AI 导读
研究提出基于氨基酸字母表全局双射重标记的诊断框架，该变换保持列熵与成对编辑距离不变，Potts 模型和逆协方差估计器均不受影响，但蛋白质语言模型未能通过测试。在 ESM-2、MSA Transformer、AMPLIFY 等架构及 8M 至 3B 参数规模上，pLM 接触图在任意重标记下均崩溃，表明模型依赖显式的规范残基身份（“字母表锚定”）而非纯共进化统计。
正文 · 原文
Abstract:Protein language models (pLMs) produce highly accurate contact maps from single-sequence inputs. People often take this accuracy as proof that these models implicitly learn invariant coevolutionary statistics, extracting the same signals as classical alignment-based methods like Potts. In this work, we introduce a diagnostic framework around a global bijective relabeling of the amino-acid alphabet. This transformation permutes residue letters while leaving all alignment-level information, column entropies, and pairwise edit distances unchanged. Pseudolikelihood Potts models and inverse-covariance estimators remain invariant under this transformation, but pLMs fail the test. Across multiple architectures (ESM-2, MSA Transformer, AMPLIFY) and model scales (8M to 3B parameters), pLM contact maps collapse under arbitrary relabeling. Using Hamming distance controls and biochemical grouping interventions, we attempt to isolate what likely causes this degradation and find that these models rely on explicit, canonical residue identities (``alphabet anchoring'') rather than pure coevolutionary statistics. Increasing scale makes models more responsive to biochemical classes, but their performance still degrades under relabeling. Our findings show a gap between how pLMs and classical coevolutionary models compute contacts. More broadly, our work shows how diagnostic perturbations that leave classical models provably unchanged can expose hidden representation biases and prevent over-reliance on neural black boxes in biological modeling.
| Comments: | Accepted to the ICBINB - Failure Modes of AI in Biology Workshop at NeurIPS 2026. 15 pages, 6 figures |
| Subjects: | Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.16881 [q-bio.QM] |
| (or arXiv:2611.16881v1 [q-bio.QM] for this version) | |
| https://doi.org/10.48550/arXiv.2611.16881 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Aryan Keluskar [view email]
[v1]
Thu, 12 Nov 2026 14:45:07 UTC (3,318 KB)
来源：arXiv cs.AI · arxiv.org