FIM4Science/fim-pp-paper-checkpoints

FIM-PP paper checkpoints

This repository holds the checkpoints behind the real-data results of "In-Context Learning of Temporal Point Processes with Foundation Inference Models" (ICLR 2026), other than the pretrained model itself, which is FIM4Science/fim-pp.

Subfolder	Paper result	Description
table1-finetuned/{taxi,taobao,stackoverflow,amazon,retweet}	Table 1 and appendix N = 5/10, FIM-PP (f)	FIM-PP fine-tuned on the CDiff train split of each dataset
table2-zero-shot	Table 2, FIM-PP (zs)	Epoch 38 of the pretraining run. FIM4Science/fim-pp is epoch 36 and was used for Table 1
table2-finetuned/{taxi,taobao}	Table 2, FIM-PP (f)	table2-zero-shot fine-tuned on the EasyTPP train split of each dataset

Each subfolder contains config.json, model-checkpoint.pth and model.safetensors.

Usage

Install OpenFIM and pass <repo>/<subfolder> as the checkpoint:

from fim.models.hawkes import FIMHawkes, FIMHawkesConfig

FIMHawkesConfig.register_for_auto_class()
model = FIMHawkes.load_model("FIM4Science/fim-pp-paper-checkpoints/table1-finetuned/taxi")

The evaluation scripts, configs and a description of the evaluation protocol are in scripts/hawkes/REPRODUCE_FIM_PP.md.

Citation

@inproceedings{fim_pp,
  title={In-Context Learning of Temporal Point Processes with Foundation Inference Models},
  author={David Berghaus and Patrick Seifner and Kostadin Cvejoski and Cesar Ojeda and Ramses J. Sanchez},
  booktitle={The Fourteenth International Conference on Learning Representations},
  year={2026},
  url={https://openreview.net/forum?id=h9HwUAODFP}
}