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

Sjoerd van Straten; Christine Jacob; Marwan Hassani

cs.LGAug 28, 2026

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

Authors: Sjoerd van Straten, Christine Jacob, Marwan Hassani

Organizations: Department of Mathematics and Computer Science, Eindhoven University of Technology, The Netherlands · Department of Mathematics and Computer Science, University of Wuppertal, Germany

Abstract

Predictive Process Monitoring (PPM) enables organizations to forecast future process behavior, such as the next activity and remaining time of ongoing cases. In practice, three conditions cause existing methods to degrade, namely data scarcity, high process entropy and distributional shift. While Foundation Models (FMs), especially Large Language Models (LLMs), offer a new paradigm through broad sequential reasoning, adapting them to multi-task PPM under these conditions remains an open challenge. Existing FM-based approaches either lack mechanisms for handling distributional shift or rely on direct regression heads that can be structurally misaligned with continuous time prediction tasks. This paper introduces D-TAIA (Domain-aware Training and Attention-based Inference Architecture), a framework for a joint next activity and remaining time prediction task via parameter-efficient fine-tuning of an FM backbone. Our approach combines domain-aware triplet loss (DATL) pre-training with FAISS-based nearest neighbor retrieval for remaining time prediction, and adopts the TAIA inference strategy to preserve pre-trained sequential reasoning during fine-tuning. Evaluated across four real-world event logs, D-TAIA consistently shows SOTA or competitive performance compared to a fine-tuned LLM and a recurrent neural network baseline. Ablation studies confirm that techniques from NLP and computer vision can be transferred effectively to PPM with only a 10M-parameter backbone, though component contributions vary by dataset entropy.

Figures & tables

Authors	Mechanism	Task	LD	HE	OOD	MT
PPM – non-FM
Weinzierl et al. (2025) [ 21 ]	Cross-organization transfer learning	Outcome	$\sim$
Amiri Elyasi et al. (2024) [ 6 ]	Graph-based control-flow encoding	RT		✓
Zare et al. (2025) [ 22 ]	Entropy-weighted attention	NA		✓
van Straten (2026); Hassani et al. (2026) [ 20 , 8 ]	Continual learning under concept drift	NA	$\sim$
Rama-Maneiro et al. (2023) [ 17 ]	Benchmark of 10 non-FM approaches	NA, Suffix, RT				$\sim$

Table 1: Overview of related work and the research gap. Axes: LD = Low volume of data; HE = High-Entropy robustness; OOD = Out-of-Distribution generalization within a log; MT = Multi-task NA and RT prediction. ✓ = explicitly addressed with a dedicated mechanism and supporting experiment;

\sim

= a mechanism is present or claimed but not directly evaluated.

Figure 1: Overview of the D-TAIA framework.

Dataset	# Cases	# Events	# Activities	Avg. Length	Avg. Duration	Entropy
BPI2012	13,087	262,200	24	20.04	8.62	3.688
BPI2017	31,509	1,202,267	26	38.16	21.90	3.786
BPI2015_2	832	44,354	410	53.31	160.49	7.105
BPI2020_DD	10,500	56,437	17	5.37	11.67	2.827

Table 2: Characteristics of the event logs used for evaluation.

Figure 2: D-TAIA achieves higher Macro-F1 score and lower MAE than FT-LLM [ 11 ] across all backbones and datasets.

Figure 3: D-TAIA attains the best point estimate for Macro-F1 and RT MAE at every training-data budget across all four logs. The advantage over FT-LLM is clearest and most persistent at full data on the high-entropy BPI2015_2, while the three models converge most closely on BPI2017 and the near-deterministic BPI2020_DD.

Figure 4: D-TAIA’s advantage over FT-LLM and MT-RNN in macro-F1 and RT MAE is largest at short prefixes (bucket 1) and narrows toward longer prefixes (bucket 5) across all four logs. The residual gap at bucket 5 remains proportionally largest on the high-entropy BPI2015_2.

	High Entropy (BPI2015_2)		Low Entropy (BPI2020_DD)
Variant	Macro-F1	MAE	Macro-F1	MAE
D-TAIA (full)	$\mathbf{.445\pm.032}$	$\mathbf{53.85\pm 2.18}$	$\mathbf{.811\pm.011}$	$\mathbf{6.52\pm 0.48}$
– DATL	$.352\pm.048^{*}$	$68.45\pm 3.95^{*}$	$.783\pm.018$	$7.85\pm 0.68^{*}$
– Domain ID	$.385\pm.042$	$58.22\pm 2.65$	$.790\pm.016$	$6.92\pm 0.55$
– FAISS	$.418\pm.036$	$61.85\pm 2.88^{*}$	$.807\pm.012$	$6.68\pm 0.52$
– TAIA	$.425\pm.034$	$55.28\pm 2.35$	$.806\pm.013$	$6.72\pm 0.50$

Table 3: Ablation results of the framework’s components. Bold marks the best value per column. ∗ marks a variant whose 95% CI does not overlap the full model’s (D-TAIA) on that metric.

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

Lennart Fertig, Lukas Kirchdorfer, Tobias Sesterhenn

University of Mannheim, Mannheim, Germany · SAP Signavio, Walldorf, Germany · Technical University of Clausthal, Clausthal, Germany

Tabular Foundation Models Predictive Process Monitoring Process Mining Sequence Modeling

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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. While PPM research in recent years has been dominated by deep sequence models trained from scratch, such as Long Short-Term Memory (LSTM) models, foundation-model approaches---particularly large language models (LLMs)---are increasingly explored for PPM. At the same time, tabular foundation models with in-context learning capabilities offer a promising alternative but have not yet been systematically benchmarked for PPM. Thus, it remains unclear whether classical sequence-based models remain competitive in this evolving landscape. This paper compares the three modeling paradigms both conceptually and empirically through a controlled benchmark across multiple datasets and prediction tasks. The results show that sequence models consistently perform best for next activity prediction, whereas tabular foundation models are competitive on temporal tasks, with LLMs usually lagging behind despite higher cost.

Abstract

Explore similar work

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

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

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