@inproceedings{tang-etal-2026-implicit,
title = "From Implicit Graph Encoding to Explicit Evidence: A Training-Free {LLM} Framework for Temporal Knowledge Graph Reasoning",
author = "Tang, Guo and
Cheng, Ke and
Fan, Huiming and
Chang, Heng and
Zheng, Wenxiang and
Ou, Xianhao and
Xiang, Junjia and
Liu, Ming and
Zhou, Yujun and
Lanyu, Li and
Qin, Bing",
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.266/",
pages = "5383--5405",
ISBN = "979-8-89176-395-1",
abstract = "Temporal Knowledge Graph (TKG) forecasting faces significant challenges due to distribution shifts and poor inductive generalization in parametric models. While Large Language Models (LLMs) offer potent semantic reasoning, existing LLM-based approaches struggle with implicit modality alignment and suboptimal graph linearization, failing to capture complex topologies without retraining. To bridge this gap, we propose ExE-LLM, a training-free, test-time adaptive framework that reframes TKG prediction as explicit evidence-driven reasoning. Our core philosophy is to decouple topological calculation from semantic reasoning: a heuristic module translates latent graph signals into natural language evidence, enabling the LLM to perform multi-source judgment. ExE-LLM incorporates a task-aware scheduler for test-time adaptation, a heuristic synthesizer for explicit modality alignment, and a self-diagnosis module for iterative optimization. Extensive experiments on four benchmarks demonstrate that ExE-LLM achieves SOTA performance in inductive settings, significantly outperforming fully trained graph neural networks without updating LLM parameters. The source code is available at https://github.com/JENLISA4EVER/ExE-LLM."
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<abstract>Temporal Knowledge Graph (TKG) forecasting faces significant challenges due to distribution shifts and poor inductive generalization in parametric models. While Large Language Models (LLMs) offer potent semantic reasoning, existing LLM-based approaches struggle with implicit modality alignment and suboptimal graph linearization, failing to capture complex topologies without retraining. To bridge this gap, we propose ExE-LLM, a training-free, test-time adaptive framework that reframes TKG prediction as explicit evidence-driven reasoning. Our core philosophy is to decouple topological calculation from semantic reasoning: a heuristic module translates latent graph signals into natural language evidence, enabling the LLM to perform multi-source judgment. ExE-LLM incorporates a task-aware scheduler for test-time adaptation, a heuristic synthesizer for explicit modality alignment, and a self-diagnosis module for iterative optimization. Extensive experiments on four benchmarks demonstrate that ExE-LLM achieves SOTA performance in inductive settings, significantly outperforming fully trained graph neural networks without updating LLM parameters. The source code is available at https://github.com/JENLISA4EVER/ExE-LLM.</abstract>
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%0 Conference Proceedings
%T From Implicit Graph Encoding to Explicit Evidence: A Training-Free LLM Framework for Temporal Knowledge Graph Reasoning
%A Tang, Guo
%A Cheng, Ke
%A Fan, Huiming
%A Chang, Heng
%A Zheng, Wenxiang
%A Ou, Xianhao
%A Xiang, Junjia
%A Liu, Ming
%A Zhou, Yujun
%A Lanyu, Li
%A Qin, Bing
%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 tang-etal-2026-implicit
%X Temporal Knowledge Graph (TKG) forecasting faces significant challenges due to distribution shifts and poor inductive generalization in parametric models. While Large Language Models (LLMs) offer potent semantic reasoning, existing LLM-based approaches struggle with implicit modality alignment and suboptimal graph linearization, failing to capture complex topologies without retraining. To bridge this gap, we propose ExE-LLM, a training-free, test-time adaptive framework that reframes TKG prediction as explicit evidence-driven reasoning. Our core philosophy is to decouple topological calculation from semantic reasoning: a heuristic module translates latent graph signals into natural language evidence, enabling the LLM to perform multi-source judgment. ExE-LLM incorporates a task-aware scheduler for test-time adaptation, a heuristic synthesizer for explicit modality alignment, and a self-diagnosis module for iterative optimization. Extensive experiments on four benchmarks demonstrate that ExE-LLM achieves SOTA performance in inductive settings, significantly outperforming fully trained graph neural networks without updating LLM parameters. The source code is available at https://github.com/JENLISA4EVER/ExE-LLM.
%U https://aclanthology.org/2026.findings-acl.266/
%P 5383-5405
Markdown (Informal)
[From Implicit Graph Encoding to Explicit Evidence: A Training-Free LLM Framework for Temporal Knowledge Graph Reasoning](https://aclanthology.org/2026.findings-acl.266/) (Tang et al., Findings 2026)
ACL
- Guo Tang, Ke Cheng, Huiming Fan, Heng Chang, Wenxiang Zheng, Xianhao Ou, Junjia Xiang, Ming Liu, Yujun Zhou, Li Lanyu, and Bing Qin. 2026. From Implicit Graph Encoding to Explicit Evidence: A Training-Free LLM Framework for Temporal Knowledge Graph Reasoning. In Findings of the Association for Computational Linguistics: ACL 2026, pages 5383–5405, San Diego, California, United States. Association for Computational Linguistics.