arXiv Machine Learning September 25, 2026 By Zeyan Li, Libing Chen, Shengda Zhuo, Yin Tang, Jianfeng Xu

BridgeMem: Causal Dyadic Transition Residuals for Temporal Knowledge Graph Forecasting

Read the original on arXiv Machine Learning →

BridgeMem is a new method for temporal knowledge graph forecasting that focuses on pair‑specific transition evidence, adding a residual correction to the log scores of a frozen full‑vocabulary forecaster. It retrieves and encodes prior events between a query actor and candidate, converting them into a likelihood‑ratio correction via a support‑adaptive empirical‑Bayes reader. Across five benchmarks, BridgeMem outperforms nine baselines from 2021–2026, improving filtered MRR and Hits@{1,3,10} metrics by up to 0.0216.

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