arXiv Machine Learning

CountTRuCoLa: Rule Learning for Interpretable Temporal Knowledge Graph Forecasting

arXiv:2509. 09474v2 Announce Type: replace Abstract: We address the task of temporal knowledge graph forecasting with an inherently interpretable method based on symbolic rules.

arXiv AI

Jun 10

MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoning

arXiv:2602. 03164v2 Announce Type: replace-cross Abstract: Time series forecasting (TSF) plays a critical role in decision-making for many real-world applications.

By Xiaoyu Tao, Mingyue Cheng, Ze Guo, Shuo Yu, Yaguo Liu, Qi Liu, Shijin Wang

llms

arXiv AI

4d ago

HiTS-CL: A Continual Learning Framework for Long-Horizon Temporal Knowledge Graph Extrapolation

arXiv:2609.36559v1 Announce Type: cross Abstract: Extrapolative temporal knowledge graph reasoning (TKGR) predicts future facts from historical snapshots. Most existing methods train once on an early...

By Yansong Liu, Rui Liu, Yuan Zuo, Hongwei Zhao, Da Fu, Fuwei Zhang, Fuzhen Zhuang, Yong Chen, Zhe Li

fine-tuning efficiency benchmarks

arXiv AI

Aug 10

LiFTER: A Grounded Neuro-Symbolic Microscope for Continuous-Time Dynamic Graph Forecasting

arXiv:2608. 06765v1 Announce Type: new Abstract: Continuous-time dynamic graph models predict future links by compressing past interactions into neural states.

By Minwoo Yu, Young-guk Ha

benchmarks

arXiv AI

Jun 6

Generating Graph-Like Logical Rules for Knowledge Graph Reasoning via Diffusion Models

arXiv:2605. 30747v2 Announce Type: replace Abstract: Logical rules constitute a cornerstone of knowledge graph (KG) reasoning, valued for their interpretability and ability to model relational patterns.

By Haoxiang Cheng, Yunfei Wang, Chao Chen, Kewei Cheng, Zhipeng Lin, Haoxuan Li, Changjun Fan, Shixuan Liu

diffusion reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Jul 13

Temporal Knowledge Graph Forecasting under Distribution Shifts: A Synthetic Evaluation

arXiv:2607. 09232v1 Announce Type: new Abstract: Temporal knowledge graphs (TKGs) represent evolving relational systems, whose underlying data-generating processes often change over time.

By Konrad \"Ozdemir, Julia Gastinger, Lukas Kirchdorfer, Heiner Stuckenschmidt

benchmarks

arXiv AI

Aug 11

Probabilistic Circuits for Knowledge Graph Completion with Reduced Rule Sets

arXiv:2508. 06706v2 Announce Type: replace Abstract: Rule-based methods for knowledge graph completion provide explainable results, but often require tens of thousands of rules to achieve competitive performance.

By Jaikrishna Manojkumar Patil, Nathaniel Lee, Al Mehdi Saadat Chowdhury, YooJung Choi, Paulo Shakarian

benchmarks

arXiv AI

Aug 25

LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applications

arXiv:2608.23058v1 Announce Type: new Abstract: Large language models (LLMs) now support forecasting systems that combine language-based reasoning with temporal data, evidence retrieval, external too...

By Xiaogang Xu, Jiaqi Tang, Jianmin Chen, Yingying Yan, Zhenchao Tang, Xiangxin Zhou, Xiaobin Hu, Wei Wei, Jinfeng Wu, Qifeng Chen, Lu Zhou, Jiafei Wu, Zhe Liu, Jianwei Yin, Weimin Zheng

llms rag agents benchmarks

arXiv AI

Jun 15

AdaTKG: Adaptive Memory for Temporal Knowledge Graph Reasoning

arXiv:2605. 07121v2 Announce Type: replace Abstract: Temporal knowledge graphs (TKGs) represent time-stamped relational facts and support a wide range of reasoning tasks over evolving events.

By Seunghan Lee, Jun Seo, Jaehoon Lee, Sungdong Yoo, Minjae Kim, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, SoonYoung Lee, Wonbin Ahn

arXiv AI

Jul 28

GTIN: A Unified Framework for Joint Event and Time Prediction in Temporal Graphs

arXiv:2607. 23556v1 Announce Type: cross Abstract: Temporal graphs are increasingly used to model dynamic systems in diverse domains such as social networks, financial networks, and traffic networks.

By Mohammad Ostadmohammadi, Sepehr Kazemi, Hamid R. Rabiee

arXiv AI

Jul 7

RLIE: Rule Generation with Logistic Regression, Iterative Refinement, and Evaluation for Large Language Models

arXiv:2510. 19698v3 Announce Type: replace Abstract: Large Language Models (LLMs) can propose rules in natural language, sidestepping the need for a predefined predicate space in traditional rule learning.

By Yang Yang, Hua XU, Zhangyi Hu, Yutao Yue

llms

arXiv Machine Learning

4d ago

CHAIN: Calibrated LLM Forecasting via Causal-Temporal Hypergraph Inference

arXiv:2609.36689v1 Announce Type: new Abstract: Large language models have achieved significant progress in event forecasting, yet their probability outputs exhibit systematic calibration bias that v...

By Wenjin Liu, Chenxi Wang, Yue Lu, Zhe Cui, Haoran Luo

llms benchmarks safety

arXiv Machine Learning

Sep 22

CTRL: Control-Based Time Series Forecasting with LLM-Guided Residual Learning

CTRL is a new framework for time‑series forecasting that separates semantic reasoning from quantitative prediction. It uses a frozen backbone to produce base forecasts, while LLM agents act as controllers that analyze prediction errors by decomposing them into trend, seasonal, and irregular components. The agents generate compact control signals that a lightweight residual decoder uses to correct the forecasts, and the system can adapt at test time to distribution shifts with only a few LLM calls.

By Minkyoung Kim, Daeun Ji, Yohan Lee, Beomsoo Kim, Beakcheol Jang

llms agents

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