metadata
license: mit
Models used in 'Verification of the Implicit World Model in a Generative Model via Adversarial Sequences' (ICLR 2026).
This repo contains 48 chess-playing GPT-2 and LLaMA models, as well as 24 board state probes that were used in the experiments of the paper.
Contents
Each model architecture folder contains 6 subfolders for the 6 datasets used in our experiments. Each of these 6 subfolders contains 4 checkpoint files, corresponding to the four training methods we used:
- Next-token prediction (NT) →
next_token.ckpt - Matching the probability distribution (PD) of valid single token continuations →
prob_dist.ckpt - NT with a jointly trained board state probe (NT+JP) →
next_token_joint_probe.ckpt - PD with a jointly trained board state probe (PD+JP) →
prob_dist_joint_probe.ckpt
Models trained without a joint probe have their linear board state probes in the probes folder.
Links
Paper links:
arXiv: https://arxiv.org/abs/2602.05903
HuggingFace: https://huggingface.co/papers/2602.05903
All corresponding code and links to further resources are available at https://github.com/szegedai/world-model-verification
Citation
If you use our code, models, or datasets, please cite the following:
@inproceedings{
balogh2026verification,
title={Verification of the Implicit World Model in a Generative Model via Adversarial Sequences},
author={Andr{\'a}s Balogh and M{\'a}rk Jelasity},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=BLOIB8CwBI}
}