Rethinking Self-Replication: Detecting Distributed Selfhood in the Outlier Cellular Automaton
- URL: http://arxiv.org/abs/2508.08047v1
- Date: Mon, 11 Aug 2025 14:49:11 GMT
- Title: Rethinking Self-Replication: Detecting Distributed Selfhood in the Outlier Cellular Automaton
- Authors: Arend Hintze, Clifford Bohm,
- Abstract summary: Spontaneous self-replication in cellular automata has long been considered rare.<n>We present formal, causal evidence that such replication can emerge unassisted.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Spontaneous self-replication in cellular automata has long been considered rare, with most known examples requiring careful design or artificial initialization. In this paper, we present formal, causal evidence that such replication can emerge unassisted -- and that it can do so in a distributed, multi-component form. Building on prior work identifying complex dynamics in the Outlier rule, we introduce a data-driven framework that reconstructs the full causal ancestry of patterns in a deterministic cellular automaton. This allows us to rigorously identify self-replicating structures via explicit causal lineages. Our results show definitively that self-replicators in the Outlier CA are not only spontaneous and robust, but are also often composed of multiple disjoint clusters working in coordination, raising questions about some conventional notions of individuality and replication in artificial life systems.
Related papers
- Sample Complexity of Causal Identification with Temporal Heterogeneity [6.5822033630228916]
We show that temporal structure is shown to effectively substitute for missing environmental diversity.<n>This work shifts the focus from whether causal structure is identifiable to whether it is statistically recoverable in practice.
arXiv Detail & Related papers (2026-02-06T17:44:00Z) - Latent Causal Diffusions for Single-Cell Perturbation Modeling [83.47931153555321]
We present a generative model that frames single-cell gene expression as a stationary diffusion process observed under measurement noise.<n> LCD outperforms established approaches in predicting the distributional shifts of unseen perturbation combinations in single-cell RNA-sequencing screens.<n>We develop an approach we call causal linearization via perturbation responses (CLIPR), which yields an approximation of the direct causal effects between all genes.
arXiv Detail & Related papers (2026-01-20T16:15:38Z) - Noise & pattern: identity-anchored Tikhonov regularization for robust structural anomaly detection [58.535473924035365]
Anomaly detection plays a pivotal role in automated industrial inspection, aiming to identify subtle or rare defects in otherwise uniform visual patterns.<n>We tackle structural anomaly detection using a self-supervised autoencoder that learns to repair corrupted inputs.<n>We introduce a corruption model that injects artificial disruptions into training images to mimic structural defects.
arXiv Detail & Related papers (2025-11-10T15:48:50Z) - Mechanical Self-replication [0.0]
This study presents a theoretical model for a self-replicating mechanical system inspired by biological processes within living cells.
The model decomposes self-replication into core components, each of which is executed by a single machine constructed from a set of basic block types.
arXiv Detail & Related papers (2024-07-18T09:49:50Z) - Computational Life: How Well-formed, Self-replicating Programs Emerge from Simple Interaction [37.95302339577743]
We show that when random, non self-replicating programs are placed in an environment lacking any explicit fitness landscape, self-replicators tend to arise.
We also show how increasingly complex dynamics continue to emerge following the rise of self-replicators.
arXiv Detail & Related papers (2024-06-27T11:34:35Z) - Self-Supervised Multi-Object Tracking For Autonomous Driving From
Consistency Across Timescales [53.55369862746357]
Self-supervised multi-object trackers have tremendous potential as they enable learning from raw domain-specific data.
However, their re-identification accuracy still falls short compared to their supervised counterparts.
We propose a training objective that enables self-supervised learning of re-identification features from multiple sequential frames.
arXiv Detail & Related papers (2023-04-25T20:47:29Z) - Learning Causal Representations of Single Cells via Sparse Mechanism
Shift Modeling [3.2435888122704037]
We propose a deep generative model of single-cell gene expression data for which each perturbation is treated as an intervention targeting an unknown, but sparse, subset of latent variables.
We benchmark these methods on simulated single-cell data to evaluate their performance at latent units recovery, causal target identification and out-of-domain generalization.
arXiv Detail & Related papers (2022-11-07T15:47:40Z) - Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data.
We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism.
We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
arXiv Detail & Related papers (2022-06-30T06:00:13Z) - Growing Isotropic Neural Cellular Automata [63.91346650159648]
We argue that the original Growing NCA model has an important limitation: anisotropy of the learned update rule.
We demonstrate that cell systems can be trained to grow accurate asymmetrical patterns through either of two methods.
arXiv Detail & Related papers (2022-05-03T11:34:22Z) - Discovering Latent Causal Variables via Mechanism Sparsity: A New
Principle for Nonlinear ICA [81.4991350761909]
Independent component analysis (ICA) refers to an ensemble of methods which formalize this goal and provide estimation procedure for practical application.
We show that the latent variables can be recovered up to a permutation if one regularizes the latent mechanisms to be sparse.
arXiv Detail & Related papers (2021-07-21T14:22:14Z) - High-dimensional separability for one- and few-shot learning [58.8599521537]
This work is driven by a practical question, corrections of Artificial Intelligence (AI) errors.
Special external devices, correctors, are developed. They should provide quick and non-iterative system fix without modification of a legacy AI system.
New multi-correctors of AI systems are presented and illustrated with examples of predicting errors and learning new classes of objects by a deep convolutional neural network.
arXiv Detail & Related papers (2021-06-28T14:58:14Z) - Automatic Recall Machines: Internal Replay, Continual Learning and the
Brain [104.38824285741248]
Replay in neural networks involves training on sequential data with memorized samples, which counteracts forgetting of previous behavior caused by non-stationarity.
We present a method where these auxiliary samples are generated on the fly, given only the model that is being trained for the assessed objective.
Instead the implicit memory of learned samples within the assessed model itself is exploited.
arXiv Detail & Related papers (2020-06-22T15:07:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.