@inproceedings{huang-etal-2026-temptool,
title = "{T}emp{T}ool-R1: Tool-Augmented Reinforcement Learning for Temporal Knowledge Graph Question Answering",
author = "Huang, Zicheng and
Tong, Yajuan and
Tu, Xinhui and
He, Tingting",
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.797/",
pages = "16224--16241",
ISBN = "979-8-89176-395-1",
abstract = "Temporal knowledge graph question answering (TKGQA) addresses time-sensitive queries over temporal knowledge graphs, but existing approaches struggle with multi-hop reasoning and implicit temporal constraints. We introduce TempTool-R1, a novel tool-integrated reasoning framework that enables large language models to explicitly use temporal tools for precise reasoning. First, we design a unified temporal tool-based API capable of transforming implicit temporal cues into executable operations, establishing the structural foundation for tool interaction. In the second stage, supervised fine-tuning teaches the model to interweave chain-of-thought reasoning with think-then-tool usage, allowing it to call temporal tools during inference. Finally, we apply reinforcement learning with fine-grained, order-sensitive reward functions tailored for temporal tool use, further refining the model{'}s tool-use policy. Experiments on three challenging TKGQA benchmarks demonstrate that TempTool-R1 significantly outperforms existing methods. In particular, our approach excels on complex questions requiring multi-hop temporal reasoning, highlighting the effectiveness of temporal tool integration and reward optimization in improving TKGQA performance."
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<abstract>Temporal knowledge graph question answering (TKGQA) addresses time-sensitive queries over temporal knowledge graphs, but existing approaches struggle with multi-hop reasoning and implicit temporal constraints. We introduce TempTool-R1, a novel tool-integrated reasoning framework that enables large language models to explicitly use temporal tools for precise reasoning. First, we design a unified temporal tool-based API capable of transforming implicit temporal cues into executable operations, establishing the structural foundation for tool interaction. In the second stage, supervised fine-tuning teaches the model to interweave chain-of-thought reasoning with think-then-tool usage, allowing it to call temporal tools during inference. Finally, we apply reinforcement learning with fine-grained, order-sensitive reward functions tailored for temporal tool use, further refining the model’s tool-use policy. Experiments on three challenging TKGQA benchmarks demonstrate that TempTool-R1 significantly outperforms existing methods. In particular, our approach excels on complex questions requiring multi-hop temporal reasoning, highlighting the effectiveness of temporal tool integration and reward optimization in improving TKGQA performance.</abstract>
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%0 Conference Proceedings
%T TempTool-R1: Tool-Augmented Reinforcement Learning for Temporal Knowledge Graph Question Answering
%A Huang, Zicheng
%A Tong, Yajuan
%A Tu, Xinhui
%A He, Tingting
%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-temptool
%X Temporal knowledge graph question answering (TKGQA) addresses time-sensitive queries over temporal knowledge graphs, but existing approaches struggle with multi-hop reasoning and implicit temporal constraints. We introduce TempTool-R1, a novel tool-integrated reasoning framework that enables large language models to explicitly use temporal tools for precise reasoning. First, we design a unified temporal tool-based API capable of transforming implicit temporal cues into executable operations, establishing the structural foundation for tool interaction. In the second stage, supervised fine-tuning teaches the model to interweave chain-of-thought reasoning with think-then-tool usage, allowing it to call temporal tools during inference. Finally, we apply reinforcement learning with fine-grained, order-sensitive reward functions tailored for temporal tool use, further refining the model’s tool-use policy. Experiments on three challenging TKGQA benchmarks demonstrate that TempTool-R1 significantly outperforms existing methods. In particular, our approach excels on complex questions requiring multi-hop temporal reasoning, highlighting the effectiveness of temporal tool integration and reward optimization in improving TKGQA performance.
%U https://aclanthology.org/2026.findings-acl.797/
%P 16224-16241
Markdown (Informal)
[TempTool-R1: Tool-Augmented Reinforcement Learning for Temporal Knowledge Graph Question Answering](https://aclanthology.org/2026.findings-acl.797/) (Huang et al., Findings 2026)
ACL