@inproceedings{hemmat-etal-2026-tdgnet,
title = "{TDGN}et: Hallucination Detection in Diffusion Language Models via Temporal Dynamic Graphs",
author = "Hemmat, Arshia and
Torr, Philip and
Chen, Yongqiang and
Yu, Junchi",
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.2479/",
pages = "56277--56301",
ISBN = "979-8-89176-459-0",
abstract = "Diffusion language models (D-LLMs) offer parallel denoising and bidirectional context, but hallucination detection for D-LLMs remains underexplored. Prior detectors developed for auto-regressive LLMs typically rely on single-pass cues and do not directly transfer to diffusion generation, where factuality evidence is distributed across the denoising trajectory and may appear, drift, or be self-corrected over time. We introduce TDGNet, a temporal dynamic graph framework that formulates hallucination detection as learning over evolving token-level attention graphs. At each denoising step, we sparsify the attention graph and update per-token memories via message passing, then apply temporal attention to aggregate trajectory-wide evidence for final prediction. Experiments across four D-LLMs spanning dense and Mixture-of-Experts architectures (7B{---}16B parameters) and multiple diffusion paradigms show consistent AUROC improvements over output-based, latent-based, and static-graph baselines, with single-pass inference and modest overhead. These results highlight the importance of temporal reasoning on attention graphs for robust hallucination detection in diffusion language models."
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<abstract>Diffusion language models (D-LLMs) offer parallel denoising and bidirectional context, but hallucination detection for D-LLMs remains underexplored. Prior detectors developed for auto-regressive LLMs typically rely on single-pass cues and do not directly transfer to diffusion generation, where factuality evidence is distributed across the denoising trajectory and may appear, drift, or be self-corrected over time. We introduce TDGNet, a temporal dynamic graph framework that formulates hallucination detection as learning over evolving token-level attention graphs. At each denoising step, we sparsify the attention graph and update per-token memories via message passing, then apply temporal attention to aggregate trajectory-wide evidence for final prediction. Experiments across four D-LLMs spanning dense and Mixture-of-Experts architectures (7B—16B parameters) and multiple diffusion paradigms show consistent AUROC improvements over output-based, latent-based, and static-graph baselines, with single-pass inference and modest overhead. These results highlight the importance of temporal reasoning on attention graphs for robust hallucination detection in diffusion language models.</abstract>
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%0 Conference Proceedings
%T TDGNet: Hallucination Detection in Diffusion Language Models via Temporal Dynamic Graphs
%A Hemmat, Arshia
%A Torr, Philip
%A Chen, Yongqiang
%A Yu, Junchi
%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 hemmat-etal-2026-tdgnet
%X Diffusion language models (D-LLMs) offer parallel denoising and bidirectional context, but hallucination detection for D-LLMs remains underexplored. Prior detectors developed for auto-regressive LLMs typically rely on single-pass cues and do not directly transfer to diffusion generation, where factuality evidence is distributed across the denoising trajectory and may appear, drift, or be self-corrected over time. We introduce TDGNet, a temporal dynamic graph framework that formulates hallucination detection as learning over evolving token-level attention graphs. At each denoising step, we sparsify the attention graph and update per-token memories via message passing, then apply temporal attention to aggregate trajectory-wide evidence for final prediction. Experiments across four D-LLMs spanning dense and Mixture-of-Experts architectures (7B—16B parameters) and multiple diffusion paradigms show consistent AUROC improvements over output-based, latent-based, and static-graph baselines, with single-pass inference and modest overhead. These results highlight the importance of temporal reasoning on attention graphs for robust hallucination detection in diffusion language models.
%U https://aclanthology.org/2026.emnlp-main.2479/
%P 56277-56301
Markdown (Informal)
[TDGNet: Hallucination Detection in Diffusion Language Models via Temporal Dynamic Graphs](https://aclanthology.org/2026.emnlp-main.2479/) (Hemmat et al., EMNLP 2026)
ACL