Asymmetric Within-Document Predictive Learning for Scientific Document Representation

• URL: http://arxiv.org/abs/2608.28625v1
• Date: Fri, 31 Jul 2026 09:44:41 GMT
• Title: Asymmetric Within-Document Predictive Learning for Scientific Document Representation
• Abstract summary: We study predictive pretraining for scientific document representation using the discourse structure of papers.<n>We propose SciJEPA, a citation-free framework that learns through asymmetric within-document prediction.
• Score: 16.276576840098247
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study predictive pretraining for scientific document representation using the discourse structure of papers. We propose SciJEPA, a citation-free framework that learns through asymmetric within-document prediction: title and abstract representations are used to predict method representations, and method representations are used to predict conclusion representations. Experiments on RELISH, high-influence citation, SciDocs, and cite prediction show that plain predictive training is viable but weaker than a controlled contrastive baseline using the same section pairs. Adding Sliced Isotropic Gaussian Regularization (SIGReg) substantially improves performance and narrows this gap. The effect of regularization is task-dependent: moderate SIGReg helps fine-grained ranking, while stronger regularization can weaken local alignment. We further show that different encoding branches support different retrieval regimes. These results position within-document predictive learning as a promising citation-free complement for scientific document representation, provided that embedding geometry is carefully controlled.

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