arXiv:2608. 06765v1 Announce Type: new Abstract: Continuous-time dynamic graph models predict future links by compressing past interactions into neural states.
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arXiv:2608. 10433v2 Announce Type: replace Abstract: Temporal reports are increasingly emitted alongside numerical forecasts and are often interpreted as statements about the computation producing those forecasts.
The paper introduces Specified-Foil Counterfactuals for temporal graphs, a method that seeks low‑cost past‑event interventions to make a user‑specified alternative outcome the top prediction. It uses trace‑guided search to compare completed executions of the original prediction with reconstructed incomplete executions of the foil, mapping differences to operations such as DELETE, INSERT, REWIRE, RELABEL, and SHIFT, and then verifies the foil through exact replay. Experiments on continuous‑time dynamic graphs and temporal knowledge graphs show that the approach retains most greedy successes while dramatically reducing predictor evaluations and achieving the specified foil in a majority of cases.
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.
arXiv:2609.36559v1 Announce Type: cross Abstract: Extrapolative temporal knowledge graph reasoning (TKGR) predicts future facts from historical snapshots. Most existing methods train once on an early...
arXiv:2608. 10433v4 Announce Type: replace Abstract: Time-series forecasters increasingly accompany numerical predictions with explicit temporal reports, such as delays or selected history, but a correct report need not describe the information actually used by the forecast.
arXiv:2607.22365v2 Announce Type: replace Abstract: High-complexity operational environments require methods that characterize temporally distributed patterns rather than classify isolated events. Th...