Asymmetric Within-Document Predictive Learning for Scientific Document Representation
SciJEPA is a citation-free framework that leverages the discourse structure of scientific papers through asymmetric within-document prediction, where title/abstract representations predict method representations, and method representations predict conclusion representations Plain predictive pretraining is viable for scientific document representation but underperforms compared to a controlled contrastive baseline using the same section pairs Sliced Isotropic Gaussian Regularization (SIGReg) subs
Analysis
TL;DR
- SciJEPA is a citation-free framework that leverages the discourse structure of scientific papers through asymmetric within-document prediction, where title/abstract representations predict method representations, and method representations predict conclusion representations
- Plain predictive pretraining is viable for scientific document representation but underperforms compared to a controlled contrastive baseline using the same section pairs
- Sliced Isotropic Gaussian Regularization (SIGReg) substantially improves predictive training performance and narrows the gap with contrastive approaches
- The regularization effect is task-dependent: moderate SIGReg benefits fine-grained ranking tasks, while stronger regularization can weaken local alignment
- Different encoding branches within the framework support different retrieval regimes, suggesting modular design opportunities for scientific document representation
Why It Matters
This work addresses a critical need in scientific information retrieval: developing robust document representations without relying on citation networks, which are sparse, biased, and unavailable for newer or less-cited papers. By demonstrating that discourse-structured predictive learning can serve as a viable citation-free alternative, the research opens pathways for representation learning in domains where citation data is limited or unreliable, directly impacting scientific search, recommendation, and analysis systems.
Technical Details
- SciJEPA Architecture: An asymmetric predictive framework that maps the natural discourse flow of scientific papers (Introduction/Abstract → Methods → Conclusions) into a predictive pretraining objective, eliminating the need for external citation signals
- Regularization Strategy: Sliced Isotropic Gaussian Regularization (SIGReg) is introduced to control embedding geometry, with tunable strength that balances global structure preservation against local alignment fidelity
- Evaluation Benchmarks: Experiments conducted across four benchmarks—RELISH (scientific document retrieval), high-influence citation prediction, SciDocs, and cite prediction—providing both retrieval and citation-based evaluation
- Contrastive Baseline: A controlled contrastive learning setup using identical section pairs enables fair comparison, isolating the effect of predictive versus contrastive objectives
- Branch-Specific Retrieval: The multi-branch encoding design reveals that different sections (title/abstract vs. methods vs. conclusions) excel in different retrieval regimes, suggesting specialized downstream usage
Industry Insight
- Citation-free representation learning should be prioritized for scientific AI systems targeting emerging or under-cited research domains where traditional citation-based methods fail due to data sparsity
- The task-dependent nature of SIGReg regularization implies that production systems should tune regularization strength per downstream objective rather than applying a one-size-fits-all approach
- The finding that different encoding branches support different retrieval regimes suggests that modular, section-aware retrieval systems could outperform monolithic document embeddings by routing queries to the most appropriate encoding branch
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