Manifold-Aware Temporal Domain Generalization for Large Language Models

2026-02-12

AI Summary

This work addresses the challenge of temporal distribution shift in real-world applications of large language models, where conventional temporal domain generalization methods incur prohibitive computational costs by modeling dynamics across the full parameter space. To overcome this, the authors propose Manifold-aware Temporal LoRA (MaT-LoRA), a parameter-efficient fine-tuning approach that constrains model evolution to a low-dimensional manifold and embeds a structured temporal kernel within the low-rank adaptation subspace to capture dynamic shifts. By doing so, MaT-LoRA substantially reduces modeling complexity while preserving expressive capacity. Extensive experiments on both real-world datasets—including scientific literature, news articles, and user reviews—and synthetic benchmarks demonstrate its superior temporal generalization performance, scalability, and practical utility.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Temporal Planning

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Temporal distribution shifts are pervasive in real-world deployments of Large Language Models (LLMs), where data evolves continuously over time. While Temporal Domain Generalization (TDG) seeks to model such structured evolution, existing approaches characterize model adaptation in the full parameter space. This formulation becomes computationally infeasible for modern LLMs. This paper introduces a geometric reformulation of TDG under parameter-efficient fine-tuning. We establish that the low-dimensional temporal structure underlying model evolution can be preserved under parameter-efficient reparameterization, enabling temporal modeling without operating in the ambient parameter space. Building on this principle, we propose Manifold-aware Temporal LoRA (MaT-LoRA), which constrains temporal updates to a shared low-dimensional manifold within a low-rank adaptation subspace, and models its evolution through a structured temporal core. This reparameterization dramatically reduces temporal modeling complexity while retaining expressive power. Extensive experiments on synthetic and real-world datasets, including scientific documents, news publishers, and review ratings, demonstrate that MaT-LoRA achieves superior temporal generalization performance with practical scalability for LLMs.

Problem

• Temporal Domain Generalization
• Distribution Shift
• Large Language Models
• Parameter-Efficient Fine-Tuning
• Manifold Learning

Innovation

• Temporal Domain Generalization
• Manifold-aware Learning
• Parameter-Efficient Fine-Tuning
• Low-Rank Adaptation
• Temporal Distribution Shift