arXiv Machine Learning

Efficient Online Continual Foundation Model Fine-Tuning for Predictive Process Monitoring

arXiv AI

Jun 9

LargeMonitor: Monitoring Online Task-Free Continual Learning via Large Pretrained Models

arXiv:2606. 09430v1 Announce Type: cross Abstract: Online task-free continual learning (TFCL) requires intelligent agents to sequentially accumulate knowledge from an unbounded, non-stationary data stream under strict single-pass constraints and without any explicit task identifiers.

By Mingqi Yuan, Xiaoquan Sun, Shihao Luo, Jiayu Chen

agents multimodal benchmarks

arXiv AI

Aug 13

Representation Finetuning for Continual Learning

arXiv:2603. 11201v3 Announce Type: replace-cross Abstract: The world is inherently dynamic, and continual learning aims to enable models to adapt to ever-evolving data streams.

By Haihua Luo, Xuming Ran, Tommi K\"arkk\"ainen, Huiyan Xue, Zhonghua Chen, Qi Xu, Fengyu Cong

fine-tuning benchmarks

arXiv Machine Learning

Jul 31

Revisiting Predictive Process Monitoring in the Age of Foundation Models: A Comparative Study of Sequence, Tabular, and LLM Approaches

arXiv:2607. 27797v1 Announce Type: new Abstract: Predictive process monitoring (PPM) leverages event logs to forecast the future of running process instances, for instance, predicting the next activity, the remaining time until case completion, or the time to the next event.

By Lennart Fertig, Lukas Kirchdorfer, Tobias Sesterhenn

llms benchmarks

arXiv Machine Learning

Jun 30

DRIFT: A Benchmark for Task-Free Continual Graph Learning with Continuous Distribution Shifts

arXiv:2605. 12998v3 Announce Type: replace Abstract: Continual graph learning (CGL) aims to learn from dynamically evolving graphs while mitigating catastrophic forgetting.

By Guiquan Sun, Xikun Zhang, Jingchao Ni, Dongjin Song

benchmarks

arXiv Machine Learning

Sep 15

On the Potential of Multi-Task Learning in Predictive Process Monitoring

arXiv:2609.13477v1 Announce Type: new Abstract: Predictive Process Monitoring (PPM) forecasts how ongoing organizational processes unfold, enabling information systems to move beyond execution suppor...

By Lukas Kirchdorfer, Keyvan Amiri Elyasi, Heiner Stuckenschmidt

arXiv AI

Sep 30

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 Machine Learning

Aug 31

D-TAIA: Domain-Aware LLM Adaptation for Multi-Task Predictive Process Monitoring

D-TAIA is a framework that adapts large language models for multi‑task predictive process monitoring, jointly predicting the next activity and remaining time of ongoing cases. It uses domain‑aware triplet loss pre‑training, FAISS‑based nearest‑neighbor retrieval for time estimation, and a TAIA inference strategy to preserve sequential reasoning while fine‑tuning a 10 M‑parameter backbone. Across four real‑world event logs, D‑TAIA achieves state‑of‑the‑art or competitive results compared to a fine‑tuned LLM and a recurrent neural network baseline, with ablation studies showing the effectiveness of NLP and computer‑vision techniques for this domain.

By Sjoerd van Straten, Christine Jacob, Marwan Hassani

llms computer-vision nlp fine-tuning benchmarks

arXiv AI

Jul 22

Parameter-Efficient Continual Fine-Tuning: A Survey

arXiv:2504. 13822v3 Announce Type: replace-cross Abstract: The emergence of large pre-trained networks has revolutionized the AI field, unlocking new possibilities and achieving unprecedented performance.

By Eric Nuertey Coleman, Luigi Quarantiello, Ziyue Liu, Qinwen Yang, Samrat Mukherjee, Julio Hurtado, Vincenzo Lomonaco

fine-tuning benchmarks

arXiv Machine Learning

Aug 18

Task-Anchored Representation Shaping for Pre-Trained Model-Based Continual Learning

arXiv:2608. 16345v1 Announce Type: new Abstract: Pre-trained models (PTMs) provide a strong foundation for continual learning by offering stable representations that facilitate lightweight adaptation to new tasks.

By Zhiming Xu, Huiyu Yi, Zhen-Hao Xie, Baile Xu, Furao Shen, Jian Zhao, Suorong Yang

arXiv Machine Learning

Jul 10

When Does Continual Learning Require Learning

arXiv:2607. 07847v1 Announce Type: new Abstract: As large language models (LLMs) become increasingly capable, the next question is how can we enable models to continually learn?

By Anne Harrington, Nayan Saxena, Michael Murphy, Anastasia Borovykh, Zeyu Yun, Sridhar Kamath, Ara Eindra Kyi, Trevor Darrell, Jitendra Malik, Yutong Bai

llms agents reinforcement-learning fine-tuning efficiency benchmarks

arXiv Machine Learning

Jun 11

TaskFusion: Continual Anomaly Detection for Heterogeneous Tabular Data

arXiv:2606. 11844v1 Announce Type: new Abstract: Continual anomaly detection in tabular data is challenging and remains largely underexplored, particularly in settings with heterogeneous feature schemas, distribution shifts, and severe class imbalance.

By Dayananda Herurkar, Federico Raue, Joachim Folz, J\"orn Hees, Andreas Dengel

fine-tuning efficiency

arXiv AI

Jul 8

To Retain or to Adapt? Generalizing Continual Learning

arXiv:2607. 05609v1 Announce Type: cross Abstract: The Continual Learning (CL) literature has long been driven by the goal of mitigating catastrophic forgetting.

By Giulia Lanzillotta, Mandana Samiei, Doina Precup, Razvan Pascanu, Claire Vernade

computer-vision reinforcement-learning benchmarks safety

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