Researchers have proposed a new framework called FreqDiff for predicting future facts in temporal knowledge graphs. This approach uses denoising and spectral calibration to improve the accuracy of predictions. The authors argue that previous methods may not effectively distinguish between relevant and irrelevant historical information, leading to diluted signals. They demonstrate the effectiveness of their method on four public benchmarks, achieving state-of-the-art performance. --- Why it matters: This work matters because it addresses a key challenge in temporal knowledge graph extrapolation: accurately inferring future facts from complex relational histories. The proposed FreqDiff framework has the potential to improve the accuracy and reliability of predictions in applications such as event forecasting and recommendation systems. Source: https://arxiv.org/abs/2608.20804