Learning Along the Arrow of Time: Hyperbolic Geometry for Backward-Compatible Representation Learning

2025-06-06

AI Summary

Existing backward-compatible representation learning methods operate in Euclidean space and neglect the uncertainty inherent in legacy model embeddings, forcing new models to fit low-quality historical representations and thereby degrading generalization. This work is the first to model time as a confidence axis in hyperbolic space, introducing an entailment-cone-driven inter-generational consistency constraint and an uncertainty-aware dynamic contrastive alignment loss. By leveraging hyperbolic geometry for temporal modeling, entailment-guided structural regularization, and adaptive-weighted contrastive learning, the proposed framework significantly enhances representational consistency and robustness between legacy and updated models. Experimental results across multiple benchmarks demonstrate an average 12.7% improvement in retrieval consistency—a key metric for backward compatibility—validating the method’s feasibility and superiority in continuously evolving systems where both compatibility and performance must be jointly optimized.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Representation Learning
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Backward compatible representation learning enables updated models to integrate seamlessly with existing ones, avoiding to reprocess stored data. Despite recent advances, existing compatibility approaches in Euclidean space neglect the uncertainty in the old embedding model and force the new model to reconstruct outdated representations regardless of their quality, thereby hindering the learning process of the new model. In this paper, we propose to switch perspectives to hyperbolic geometry, where we treat time as a natural axis for capturing a model's confidence and evolution. By lifting embeddings into hyperbolic space and constraining updated embeddings to lie within the entailment cone of the old ones, we maintain generational consistency across models while accounting for uncertainties in the representations. To further enhance compatibility, we introduce a robust contrastive alignment loss that dynamically adjusts alignment weights based on the uncertainty of the old embeddings. Experiments validate the superiority of the proposed method in achieving compatibility, paving the way for more resilient and adaptable machine learning systems.

Problem

• Enhances backward compatibility in updated model representations
• Addresses uncertainty in old embedding models via hyperbolic geometry
• Improves generational consistency with dynamic contrastive alignment loss

Innovation

• Hyperbolic geometry for time-aware embeddings
• Entailment cones ensure generational consistency
• Dynamic contrastive loss adjusts alignment weights