The paper introduces the AGI Maze Prediction Datasets and Benchmark, a lightweight, procedurally generated grid‑world testbed for evaluating predictive models, particularly Transformers, on tasks such as per‑step transition prediction, fixed‑horizon state prediction, and sequential textual‑observation prediction. It compares byte‑level Transformer baselines with two memory‑augmented architectures, showing that a pseudo‑video spatial‑memory Transformer achieves perfect validation accuracy on selected tasks and improves sequential text‑trace prediction, while a generic auxiliary latent‑memory Transformer does not consistently help. The study highlights that structured, task‑aligned working memory can be more effective than merely increasing latent capacity, and positions the benchmark as a compact setting for testing architectures that couple textual interfaces to learned structured state.
By Alexey Potapov
arXiv:2610.19825v1 Announce Type: cross
Abstract: Integrating world models into Vision-Language-Action models (VLAs) has emerged as a promising direction, enabling policies to anticipate future dynam...
By Qisen Ma, Yixiang Chen, Yuan Xu, Peiyan Li, Jiabing Yang, Kai Wang, Jianhua Yang, Jianlou Si, Jun Huang, Jing Liu, Nianfeng Liu, Yan Huang, Liang Wang
The paper introduces the AGI Maze Prediction Datasets and Benchmark, a lightweight testbed for evaluating how Transformers and other models learn world dynamics. The benchmark, built from procedurally generated grid worlds, includes per‑step transition prediction, fixed‑horizon state prediction, and sequential textual‑observation prediction, with source‑maze‑disjoint training and validation splits to test transferable action‑conditioned dynamics. Experiments show that a pseudo‑video spatial‑memory Transformer, which initializes and updates a two‑dimensional latent workspace from the input map and action history, achieves perfect validation accuracy on selected tasks and improves sequential text‑trace prediction, outperforming byte‑level and unstructured‑memory baselines and suggesting that structured, task‑aligned working memory is more effective than additional latent capacity alone.
arXiv:2609.32193v2 Announce Type: replace
Abstract: Vision Language Action (VLA) models condition actions directly on current visual and language context, without an explicit account of how the scene...
By Hongyi Cai, Yi Herng Ong, Tingshiuan C. Wu, Chiew Hui Lim, Hanxia Li, Kehong Guo, Sze Yuan Cheong
arXiv:2506. 06006v3 Announce Type: replace-cross Abstract: Can unified vision-language models (VLMs) perform forward dynamics prediction (FDP), i.
By Yifu Qiu, Yftah Ziser, Anna Korhonen, Shay B. Cohen, Edoardo M. Ponti
arXiv:2610.20160v1 Announce Type: new
Abstract: Learning world models across diverse domains calls for a unified control space, yet ac- tion interfaces vary across domains and action annotations are...
By Ziyu Zhu, Zhaoxu Sun, Fan Jiang, Chiyu Wang, Junfeng Ni, Mu Xu