@inproceedings{gan-etal-2026-denoising,
title = "Denoising the Future: Context-Aware Spectral Diffusion for Temporal Knowledge Graph Extrapolation",
author = "Gan, Yanglei and
He, Peng and
Lin, Run and
Jiang, Peiyuan and
Wang, Yifan and
Liu, Qiao",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2060/",
pages = "46895--46913",
ISBN = "979-8-89176-459-0",
abstract = "Temporal Knowledge Graph (TKG) extrapolation seeks to infer future facts from time-varying relational histories. Recent diffusion-based approaches improve uncertainty modeling through generative denoising, but their aggregated conditioning on subject histories may insufficiently distinguish query-specific evidence from non-salient historical facts, thereby diluting target-discriminative signals. To bridge this gap, we propose **FreqDiff**, a **Freq**uency-aware **Diff**usion framework for TKG extrapolation. Specifically, FreqDiff formulates future object prediction as query-slot denoising and develops a dual-stream denoiser that integrates temporal dependency modeling with context-aware spectral calibration. The spectral branch synthesizes history-conditioned filters from learnable bases to adaptively re-calibrate denoising representations, while a frequency-domain regularizer is proposed to align the denoised target with the gold object in spectral space. Experiments on four public TKG benchmarks demonstrate that FreqDiff achieves state-of-the-art performance."
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%0 Conference Proceedings
%T Denoising the Future: Context-Aware Spectral Diffusion for Temporal Knowledge Graph Extrapolation
%A Gan, Yanglei
%A He, Peng
%A Lin, Run
%A Jiang, Peiyuan
%A Wang, Yifan
%A Liu, Qiao
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F gan-etal-2026-denoising
%X Temporal Knowledge Graph (TKG) extrapolation seeks to infer future facts from time-varying relational histories. Recent diffusion-based approaches improve uncertainty modeling through generative denoising, but their aggregated conditioning on subject histories may insufficiently distinguish query-specific evidence from non-salient historical facts, thereby diluting target-discriminative signals. To bridge this gap, we propose **FreqDiff**, a **Freq**uency-aware **Diff**usion framework for TKG extrapolation. Specifically, FreqDiff formulates future object prediction as query-slot denoising and develops a dual-stream denoiser that integrates temporal dependency modeling with context-aware spectral calibration. The spectral branch synthesizes history-conditioned filters from learnable bases to adaptively re-calibrate denoising representations, while a frequency-domain regularizer is proposed to align the denoised target with the gold object in spectral space. Experiments on four public TKG benchmarks demonstrate that FreqDiff achieves state-of-the-art performance.
%U https://aclanthology.org/2026.emnlp-main.2060/
%P 46895-46913
Markdown (Informal)
[Denoising the Future: Context-Aware Spectral Diffusion for Temporal Knowledge Graph Extrapolation](https://aclanthology.org/2026.emnlp-main.2060/) (Gan et al., EMNLP 2026)
ACL