Update README.md

README.md

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-license: mit
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+---
+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](https://arxiv.org/abs/2602.05903)  
+HuggingFace: [https://huggingface.co/papers/2602.05903](https://huggingface.co/papers/2602.05903)
+
+All corresponding code and links to further resources are available at [https://github.com/szegedai/world-model-verification](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}
+}
+```