@inproceedings{huang-etal-2026-trace,
title = "{M}-{TRACE}: Detecting and Mitigating Time-Anchor Drift via Step-wise Conflict Checking in Temporal Reasoning",
author = "Huang, Danyu and
Jiang, Jiayuan and
Zhang, Yao and
Wang, Jun and
Li, Huijia and
Yang, Zhenglu",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1204/",
pages = "24059--24077",
ISBN = "979-8-89176-395-1",
abstract = "As the real world continuously evolves, temporal facts change over time, requiring large language models to simultaneously rely on internal parametric knowledge and externally retrieved evidence for temporal reasoning. However, external knowledge may be inaccurate, while internal knowledge can become outdated. Temporal inconsistencies between these heterogeneous sources can accumulate during multi-step reasoning, leading to Time-Anchor Drift (TAD){---}a phenomenon where an incorrect temporal reference is established early and subsequently propagated, ultimately causing reasoning failure. To address this issue, we propose M-TRACE, a multi-agent reasoning framework for temporal knowledge conflicts. M-TRACE explicitly maintains a State Timeline to perform step-wise temporal alignment and coexistence checks between internal states and external evidence. Detected conflicts are summarized into a structured Conflict Report, which guides conflict-aware final reasoning. We further introduce TimeConfQA, a temporal question answering benchmark with controlled temporal knowledge conflicts. Experimental results show that M-TRACE effectively reduces time-anchor drift and consistently improves performance on complex temporal question answering tasks, demonstrating the value of explicit conflict modeling for temporal reasoning. The code can be found at https://github.com/h-yii/M-TRACE."
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<abstract>As the real world continuously evolves, temporal facts change over time, requiring large language models to simultaneously rely on internal parametric knowledge and externally retrieved evidence for temporal reasoning. However, external knowledge may be inaccurate, while internal knowledge can become outdated. Temporal inconsistencies between these heterogeneous sources can accumulate during multi-step reasoning, leading to Time-Anchor Drift (TAD)—a phenomenon where an incorrect temporal reference is established early and subsequently propagated, ultimately causing reasoning failure. To address this issue, we propose M-TRACE, a multi-agent reasoning framework for temporal knowledge conflicts. M-TRACE explicitly maintains a State Timeline to perform step-wise temporal alignment and coexistence checks between internal states and external evidence. Detected conflicts are summarized into a structured Conflict Report, which guides conflict-aware final reasoning. We further introduce TimeConfQA, a temporal question answering benchmark with controlled temporal knowledge conflicts. Experimental results show that M-TRACE effectively reduces time-anchor drift and consistently improves performance on complex temporal question answering tasks, demonstrating the value of explicit conflict modeling for temporal reasoning. The code can be found at https://github.com/h-yii/M-TRACE.</abstract>
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%0 Conference Proceedings
%T M-TRACE: Detecting and Mitigating Time-Anchor Drift via Step-wise Conflict Checking in Temporal Reasoning
%A Huang, Danyu
%A Jiang, Jiayuan
%A Zhang, Yao
%A Wang, Jun
%A Li, Huijia
%A Yang, Zhenglu
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F huang-etal-2026-trace
%X As the real world continuously evolves, temporal facts change over time, requiring large language models to simultaneously rely on internal parametric knowledge and externally retrieved evidence for temporal reasoning. However, external knowledge may be inaccurate, while internal knowledge can become outdated. Temporal inconsistencies between these heterogeneous sources can accumulate during multi-step reasoning, leading to Time-Anchor Drift (TAD)—a phenomenon where an incorrect temporal reference is established early and subsequently propagated, ultimately causing reasoning failure. To address this issue, we propose M-TRACE, a multi-agent reasoning framework for temporal knowledge conflicts. M-TRACE explicitly maintains a State Timeline to perform step-wise temporal alignment and coexistence checks between internal states and external evidence. Detected conflicts are summarized into a structured Conflict Report, which guides conflict-aware final reasoning. We further introduce TimeConfQA, a temporal question answering benchmark with controlled temporal knowledge conflicts. Experimental results show that M-TRACE effectively reduces time-anchor drift and consistently improves performance on complex temporal question answering tasks, demonstrating the value of explicit conflict modeling for temporal reasoning. The code can be found at https://github.com/h-yii/M-TRACE.
%U https://aclanthology.org/2026.findings-acl.1204/
%P 24059-24077
Markdown (Informal)
[M-TRACE: Detecting and Mitigating Time-Anchor Drift via Step-wise Conflict Checking in Temporal Reasoning](https://aclanthology.org/2026.findings-acl.1204/) (Huang et al., Findings 2026)
ACL