← Back to all news
Hugging Face Trending Papers

Foundation Models for Event Prediction Across Heterogeneous Event Logs

Read the original on Hugging Face Trending Papers →

The paper introduces the Foundation Event Predictor (FEP), a scalable in‑context architecture that performs next‑event prediction across diverse event logs using learned, event‑level nonparametric inference. FEP employs causal self‑attention to encode context and target histories, cross‑attention for relevance‑weighted aggregation, and permutation‑robust embedding learning to decouple event IDs from semantics. Trained on 696 real‑world datasets and evaluated on 50 unseen ones, FEP generalizes effectively to new systems without parameter adaptation, often matching or surpassing models trained separately on each target dataset.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Hugging Face Trending Papers.

Related stories

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
llmscomputer-visionnlpfine-tuningbenchmarks
More like this →
arXiv Machine Learning
Jun 8

One Loss to Rule Them All: Marked Time-to-Event for Structured EHR Foundation Models

arXiv:2602. 00541v2 Announce Type: replace Abstract: Clinical events captured in Electronic Health Records (EHR) are irregularly sampled and may consist of a mixture of discrete events and numerical measurements, such as laboratory values or treatment dosages.

By Zilin Jing, Vincent Jeanselme, Yuta Kobayashi, Simon A. Lee, Chao Pang, Aparajita Kashyap, Yanwei Li, Xinzhuo Jiang, Shalmali Joshi
More like this →
arXiv Machine Learning
Aug 12

ChronoSSM: Training for Temporally Aware Representations in Autoregressive State Space Models

arXiv:2608. 10120v1 Announce Type: new Abstract: Modern sequence models, from Transformers to State Space Models, have enabled powerful generative modeling across diverse domains, yet they are typically trained to predict what happens while treating when it happens as a secondary concern.

By Adrien Schoen, Nachiketa Ratnakar Patil, Arjun Bhagoji, Francesco Bronzino
llms
More like this →
arXiv AI
3d ago

KITE: A Knowledge-Injected Temporal Encoder for Time-Series Event Forecasting

KITE is a knowledge‑injected temporal encoder that augments self‑supervised event forecasting with semantic priors supplied by system engineers. It uses two switchable mechanisms—a covariate mechanism and an attention‑bias mechanism—so the same architecture can incorporate different prior types without per‑dataset tuning. Experiments show that informative priors improve performance in short contexts and enable out‑of‑domain transfer, while priors already present in the observed window have little effect.

By Jonas Petersen, Pratik Jawahar, Riccardo Maggioni, Gian-Alessandro Lombardi, Matteo Cozzi, Marek Gajewski, Federico Martelli, Camilla Mazzoleni, Philipp Christian Petersen
safety
More like this →