Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.36797

Where Does Randomness Matter in Neural Cellular Automata?

Fei Zuo·Jiaqi Shi·Yujing Liu
View original source ↗Read source document ↗Export metadata ↓

Abstract

Stochastic cell updates are often used throughout the life of a neural cellular automaton (NCA), from backpropagation through time to final rollout. This leaves two questions entangled: does update randomness help learn a useful rule, and must that randomness remain at execution? We separate training and evaluation update modes in controlled Growing NCA experiments, then vary the states shown during training. Under the standard constant-rate persist recipe, asynchronous training passes the short-horizon quality test in 10/10 runs, compared with 3/10 synchronous runs. All ten asynchronous models also retain the target for 4,096 steps under deterministic evaluation. For a scalar translation-invariant lattice, we derive an exact mean-square criterion: random masking can damp mean modes, but it also injects variance, and a mean-only test misclassifies four non-marginal settings. Finally, among 30 models that all pass the same reconstruction test, eight of ten grow-trained models become off-target at 4,096 steps, while all persist and regenerate models retain the target; damage recovery separates persist from regenerate. The results distinguish optimization reliability, execution mode, and task-specific behavior instead of treating them as one stability property.

Explore related subjects

cs.LGcs.NE

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Fei Zuo, Jiaqi Shi, Yujing Liu. 2026-09-29. Where Does Randomness Matter in Neural Cellular Automata?. https://arxiv.org/abs/2609.36797

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Efficient Policy Evaluation with Offline Data Informed Behavior Policy Design

Online Monte Carlo evaluation is a fundamental tool for assessing policy performance in reinforcement learning and sequential decision-making problems arising in operations research. However, achieving accurate estimates often requires extensive online interaction with the environment, which can be costly or impractical in many real-world settings. In this paper, we develop a framework that improves the sample efficiency of online Monte Carlo estimators while preserving unbiasedness. We first derive a closed-form optimal behavior policy that minimizes estimator variance under unbiasedness constraints. We then propose practical algorithms for learning the proposed behavior policy from previously collected offline data, enabling improved online evaluation without requiring estimation of the environment transition model. We provide theoretical analysis that quantifies the resulting variance reduction and analyzes the impact of approximation errors. Empirical studies across diverse environments demonstrate substantial improvements in online sample efficiency compared with standard on-policy Monte Carlo evaluation and existing baseline methods. Our results provide a unified framework for optimal behavior policy design in off-policy evaluation, with applications to reinforcement learning and operations research.

cs.LG↗
arXiv2026

MONOVAB : An Annotated Corpus for Bangla Multi-label Emotion Detection

In recent years, Sentiment Analysis (SA) and Emotion Recognition (ER) have been increasingly popular in the Bangla language, which is the seventh most spoken language throughout the entire world. However, the language is structurally complicated, which makes this field arduous to extract emotions in an accurate manner. Several distinct approaches such as the extraction of positive and negative sentiments as well as multiclass emotions, have been implemented in this field of study. Nevertheless, the extraction of multiple sentiments is an almost untouched area in this language. Which involves identifying several feelings based on a single piece of text. Therefore, this study demonstrates a thorough method for constructing an annotated corpus based on scrapped data from Facebook to bridge the gaps in this subject area to overcome the challenges. To make this annotation more fruitful, the context-based approach has been used. Bidirectional Encoder Representations from Transformers (BERT), a well-known methodology of transformers, have been shown the best results of all methods implemented. Finally, a web application has been developed to demonstrate the performance of the pre-trained top-performer model (BERT) for multi-label ER in Bangla.

cs.LG↗
arXiv2026

Online Regularized Statistical Learning in Reproducing Kernel Hilbert Space With Non-Stationary Data

We study recursive regularized learning algorithms in the reproducing kernel Hilbert space (RKHS) with non-stationary online data streams. We introduce the concept of a random Tikhonov regularization path and decompose the tracking error of the algorithm's output for the regularization path into random difference equations in RKHS. We show that the tracking error vanishes in mean square and almost surely if the regularization path is slowly time-varying. Then, leveraging the monotonicity of inverse operators and the spectral decomposition of compact operators, and introducing the RKHS persistence of excitation condition, we develop a dominated convergence method to prove the mean square and almost sure consistency between the regularization path and the unknown function to be learned. Especially, for independent and non-identically distributed data streams, the mean square and almost sure consistency between the algorithm's output and the unknown function is achieved if the input data's marginal probability measures are slowly time-varying and the average measure over each fixed-length time period is uniformly above a strictly positive finite Borel measure.

cs.LG↗