ResearchResearch paperTraining & Scaling · Large Language Models · Reinforcement Learning1 source · Oct 20, 2026

From Complex Histories to Simple Predictors: Predictive Dimension in Dynamic Graph Learning

Existing continuous-time graph learning methods rely heavily on complex neural architectures to encode ever-expanding interaction histories.

However, the future conditional distribution induced by complex histories can often be characterized by a low-dimensional predictive state manifold, implying that only a few predictive degrees of freedom are essential for future interactions.
We therefore argue that continuous-time graph learning should identify low-dimensional states sufficient for future prediction rather than reconstruct the full history.
To make this perspective measurable, we define and independently estimate the intrinsic predictive dimension, which quantifies the effective predictive degrees of freedom required by a given task.
Based on this finding, we propose PreDyG, a lightweight model whose capacity is matched to the intrinsic predictive dimension.

[1]From Complex Histories to Simple Predictors: Predictive Dimension in Dynamic Graph Learning
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 12:08 PM
Existing continuous-time graph learning methods rely heavily on complex neural architectures to encode ever-expanding interaction histories.
However, the future conditional distribution induced by complex histories can often be characterized by a low-dimensional predictive state manifold, implying that only a few predictive degrees of freedom are essential for future interactions.

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