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.
By Zeyan Li, Libing Chen, Shengda Zhuo, Yin Tang, Jianfeng Xu
arXiv:2606. 03602v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the fundamental limitations of purely statistical methods, such as statistical distinguishability within equivalence classes and sensitivity to finite sample sizes.
By Bo Peng, Kaiwen Wu, Sirui Chen, Zhiheng Wang, Yu Qiao, Chaochao Lu
arXiv:2606. 28225v1 Announce Type: new Abstract: Temporal link prediction is usually evaluated by predictive performance on unseen edges, but in probabilistic temporal graphs this criterion can conflate model error with irreducible uncertainty.
By Aniq Ur Rahman
arXiv:2606. 28225v2 Announce Type: replace Abstract: Temporal link prediction (TLP) is typically evaluated by predictive performance on unseen edges, but this criterion can conflate predictive accuracy with recovery of the underlying causal mechanism.
By Aniq Ur Rahman
arXiv:2606. 11816v1 Announce Type: cross Abstract: Forecasting real-world events requires language-model agents to reason under uncertainty from incomplete, time-bounded information.
By Yizhou Chi, Eric Chamoun, Zifeng Ding, Andreas Vlachos
arXiv:2606. 07525v1 Announce Type: cross Abstract: Causal graphs in text are typically populated by observable, predefined events.
By Liesbeth Allein, Marie-Francine Moens
arXiv:2606. 10607v1 Announce Type: cross Abstract: Causal discovery aims to uncover causal structures from observational data, which is crucial for real-world decision-making.
By Xinyu Li, Yuanyuan Wang, Haoxuan Li, Chuan Zhou, Erdun Gao, Bo Han, Tongliang Liu, Kun Zhang, Howard Bondell, Mingming Gong
arXiv:2602. 01135v3 Announce Type: replace Abstract: Autoregressive models trained via next-token prediction implicitly learn the conditional independence structure of their data-generating process.
By Hugo Math, Rainer Lienhart
arXiv:2404.06349v3 Announce Type: replace
Abstract: The ability to understand causality significantly impacts the competence of large language models (LLMs) in output explanation and counterfactual r...
By Yu Zhou, Xingyu Wu, Jibin Wu, Liang Feng, Kay Chen Tan
The study evaluates 12 instruction‑tuned open‑weight LLMs on six causal‑graph benchmarks, testing five prompting strategies and four confidence sources. Findings show that LLMs tend to over‑predict edges, misclassify indirect or reversed edges as direct, and exhibit high over‑confidence, while conventional confidence estimates are unreliable and agreement signals offer limited improvement. The results suggest LLMs should be used as externally validated soft causal priors rather than definitive causal‑structure evidence.
By Amit Kumar, Elnur Adl Zarabi, Suranjana Trivedy, Zhiqian Chen, Lei Zhang, Kaiqun Fu, Taoran Ji
arXiv:2602. 06337v2 Announce Type: replace-cross Abstract: Causal inference is essential for decision-making but remains challenging for non-experts.
By Junqi Chen, Sirui Chen, Chaochao Lu
arXiv:2606. 01789v1 Announce Type: new Abstract: In graphical causal model, causal discovery aims to construct a causal graph based on numerical data and domain knowledge in plain text.
By Yuzhe Zhang, Chihui Chen, Lina Yao, Chen Wang