The paper investigates Neural Cellular Automata (NCAs), which are networks of recurrent cells that rely on local connectivity and asynchronous updates. It demonstrates that NCAs can solve complex visual reasoning tasks such as large mazes, Sudoku, and ARC-AGI-1, and that they generalize to larger grids, longer rollouts, and parallel trials. The study also shows that training with sample replay and stochastic perturbations enhances generalization, and that NCAs can recover from damage and scale to raw pixel reasoning.
By Mayalen Etcheverry, Pietro Miotti, Aidan Sirbu, Konstantin Sch\"urholt, Mariia Drozdova, Arna Ghosh, Blaise Ag\"uera y Arcas, James Manyika, Blake Richards, Eyvind Niklasson
arXiv:2608. 07955v1 Announce Type: new Abstract: Large vision-language models have achieved strong performance in multimodal reasoning, but they remain unreliable on fine-grained spatial tasks that demand both precise spatial perception and fine-grained geometric computation beyond end-to-end generation.
By Shi-Yu Tian, Zhuo-Xia Wang, Xuan-Yi Zhu, Zhi Zhou, Xinwei Yang, Kun-Yang Yu, Ming Yang, Yang Chen, Yu-Feng Li
arXiv:2604.22868v2 Announce Type: replace-cross
Abstract: Visual planning represents a crucial facet of human intelligence, especially in tasks that require complex spatial reasoning and navigation....
By Zhimu Zhou, Yanpeng Zhao, Qiuyu Liao, Bo Zhao, Xiaojian Ma
arXiv:2606. 11918v1 Announce Type: new Abstract: Current Large Reasoning Models (LRMs) exhibit remarkable general capabilities but significantly underperform in spatial reasoning tasks.
By Theo Uscidda, Marta Tintore Gazulla, Maks Ovsjanikov, Federico Tombari, Leonidas Guibas
arXiv:2610.09876v1 Announce Type: cross
Abstract: Diffusion models generate realistic images but often fail on visual reasoning tasks, such as filling in a Sudoku or drawing the path through a maze....
By Pawe{\l} Skier\'s, Ma{\l}gorzata Grzanka, Wojciech Masarczyk, Jan-Willem van de Meent, Kamil Deja
Enabling Vision-Language Models (VLMs) to perform spatial reasoning remains challenging. Existing approaches treat VLMs as passive observers, which is difficult for real-world applications.
The paper introduces Spatial Alignment via Geometric Evolution (SAGE), a self‑evolving framework designed to strengthen robust spatial reasoning in Vision‑Language Models (VLMs). SAGE leverages geometric and linguistic duality operations, incorporating duality consistency into training to ensure coherent answers across original and transformed inputs. The method dynamically updates its operation pool, focusing on challenging dualities while retiring mastered ones, and is model‑agnostic, data‑efficient, and applicable as a lightweight adaptation stage to existing VLMs, yielding consistent improvements on video and spatial reasoning benchmarks.
By Junming Liu, Yuqi Li, Yifei Sun, Maonan Wang, Yiming Cheng, Rui Qian, Piotr Koniusz, Yirong Chen, Ding Wang
arXiv:2503. 19990v4 Announce Type: replace Abstract: Many real-world applications of spatial intelligence, such as robotic control, autonomous driving, and automated assembly, require spatial reasoning across multiple sequential steps.
By Kexian Tang, Junyao Gao, Yanhong Zeng, Haodong Duan, Yanan Sun, Zhening Xing, Wenran Liu, Kai Chen, Kaifeng Lyu
arXiv:2610.09780v1 Announce Type: new
Abstract: Diffusion models excel at image synthesis, but they remain limited in their ability to reliably satisfy structured spatial reasoning constraints. In co...
By Ana Ezquerro, Ozan \"Ozdenizci
Traditional heuristic solvers for the 2D irregular nesting problem share a fundamental limitation: they are blind to polygon geometry, relying on guided brute-force to navigate the continuous placement space with minimal geometrical guidance. In this paper, we argue that Reinforcement Learning is uniquely positioned to overcome this bottleneck.
arXiv:2606. 10611v1 Announce Type: new Abstract: Traditional heuristic solvers for the 2D irregular nesting problem share a fundamental limitation: they are blind to polygon geometry, relying on guided brute-force to navigate the continuous placement space with minimal geometrical guidance.
By Auguste Lehuger, Guillaume Henon-Just
arXiv:2606. 13673v1 Announce Type: cross Abstract: Spatial reasoning, the ability to determine where objects are, how they relate, and how they move in 3D, remains a fundamental challenge for vision-language models (VLMs).
By Seokju Cho, Ryo Hachiuma, Abhishek Badki, Hang Su, Byung-Kwan Lee, Chan Hee Song, Sifei Liu, Subhashree Radhakrishnan, Seungryong Kim, Yu-Chiang Frank Wang, Min-Hung Chen