[2606.15868] David vs. Goliath in Next Activity Prediction: Argmax vs. LSTM, Transformer, and LLM

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Computer Science > Machine Learning

arXiv:2606.15868 (cs)

[Submitted on 14 Jun 2026]

Title:David vs. Goliath in Next Activity Prediction: Argmax vs. LSTM, Transformer, and LLM

Authors:Hans Weytjens, Ingo Weber<br>View a PDF of the paper titled David vs. Goliath in Next Activity Prediction: Argmax vs. LSTM, Transformer, and LLM, by Hans Weytjens and 1 other authors

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Abstract:Next activity prediction (NAP) is a cornerstone of predictive process monitoring (PPM), enabling organizations to move from retrospective analysis to proactive process steering. The PPM field has progressed from classical machine learning through deep learning architectures such as LSTMs and Transformers to large language models (LLMs). Despite growing model complexity, no benchmark jointly compares LLMs, Transformers, LSTMs, and simple baselines in a direct sequence modeling setting for NAP. In this paper, we fill this gap with a systematic benchmark. We compare vocabulary-adapted LLMs, Transformers trained from scratch, LLM-distilled Transformers, and LSTMs against a simple counting-based argmax baseline across seven real-life event logs. Our results tell a David vs. Goliath story: pretraining confers no consistent improvement over training from scratch, model size shows little effect on performance, and on most datasets the argmax baseline matches or approaches the performance of billion-parameter LLMs.

Comments:<br>Accepted for 24th International Conference on Business Process Management (2026) Forum

Subjects:

Machine Learning (cs.LG)

Cite as:<br>arXiv:2606.15868 [cs.LG]

(or<br>arXiv:2606.15868v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.15868

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Hans Weytjens [view email]<br>[v1]<br>Sun, 14 Jun 2026 15:40:21 UTC (92 KB)

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