# Collective Intelligence for Deep Learning: A Survey of Recent Developments

arXiv: https://arxiv.org/abs/2111.14377 · HTML: https://mlsanity.com/paper/2111.14377/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2021.

## Built on by (most cited first, 30 of 34)

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- [Automating the Search for Artificial Life with Foundation Models](https://mlsanity.com/paper/2412.17799/) (2024)
- [HyperNCA: Growing Developmental Networks with Neural Cellular Automata](https://mlsanity.com/paper/2204.11674/) (2022)
- [Towards Self-Assembling Artificial Neural Networks through Neural Developmental Programs](https://mlsanity.com/paper/2307.08197/) (2023)
- [A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2209.03859/) (2022)
- [Learning General World Models in a Handful of Reward-Free Deployments](https://mlsanity.com/paper/2210.12719/) (2022)
- [Spontaneous Emergence of Agent Individuality through Social Interactions in LLM-Based Communities](https://mlsanity.com/paper/2411.03252/) (2024)
- [A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory Management](https://mlsanity.com/paper/2306.07542/) (2023)
- [Goal-Guided Neural Cellular Automata: Learning to Control Self-Organising Systems](https://mlsanity.com/paper/2205.06806/) (2022)
- [E(n)-equivariant Graph Neural Cellular Automata](https://mlsanity.com/paper/2301.10497/) (2023)
- [Physical Neural Cellular Automata for 2D Shape Classification](https://mlsanity.com/paper/2203.07548/) (2022)
- [An effective theory of collective deep learning](https://mlsanity.com/paper/2310.12802/) (2023)
- [Collective Model Intelligence Requires Compatible Specialization](https://mlsanity.com/paper/2411.02207/) (2024)
- [Generative AI collective behavior needs an interactionist paradigm](https://mlsanity.com/paper/2601.10567/) (2026)
- [Emergent collective intelligence from massive-agent cooperation and competition](https://mlsanity.com/paper/2301.01609/) (2023)
- [Feudal Graph Reinforcement Learning](https://mlsanity.com/paper/2304.05099/) (2023)
- [Evolving Self-Assembling Neural Networks: From Spontaneous Activity to Experience-Dependent Learning](https://mlsanity.com/paper/2406.09787/) (2024)
- [Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges](https://mlsanity.com/paper/2512.04770/) (2025)
- [Holos: A Web-Scale LLM-Based Multi-Agent System for the Agentic Web](https://mlsanity.com/paper/2604.02334/) (2026)
- [Sakana Fugu Technical Report](https://mlsanity.com/paper/2606.21228/) (2026)
- [Social Neuro AI: Social Interaction as the "dark matter" of AI](https://mlsanity.com/paper/2112.15459/) (2021)
- [Artificial Collective Intelligence Engineering: a Survey of Concepts and Perspectives](https://mlsanity.com/paper/2304.05147/) (2023)
- [Collective Intelligence for 2D Push Manipulations with Mobile Robots](https://mlsanity.com/paper/2211.15136/) (2022)
- [Seeking Next Layer Neurons' Attention for Error-Backpropagation-Like Training in a Multi-Agent Network Framework](https://mlsanity.com/paper/2310.09952/) (2023)
- [Problem-Solving in Language Model Networks](https://mlsanity.com/paper/2406.12374/) (2024)
- [Learning Multi-Agent Coordination via Sheaf-ADMM](https://mlsanity.com/paper/2605.31005/) (2026)
- [Off-Beat Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2205.13718/) (2022)
- [Conceptual Framework Toward Embodied Collective Adaptive Intelligence](https://mlsanity.com/paper/2505.23153/) (2025)

## Abstract (arXiv)

In the past decade, we have witnessed the rise of deep learning to dominate the field of artificial intelligence. Advances in artificial neural networks alongside corresponding advances in hardware accelerators with large memory capacity, together with the availability of large datasets enabled practitioners to train and deploy sophisticated neural network models that achieve state-of-the-art performance on tasks across several fields spanning computer vision, natural language processing, and reinforcement learning. However, as these neural networks become bigger, more complex, and more widely used, fundamental problems with current deep learning models become more apparent. State-of-the-art deep learning models are known to suffer from issues that range from poor robustness, inability to adapt to novel task settings, to requiring rigid and inflexible configuration assumptions. Collective behavior, commonly observed in nature, tends to produce systems that are robust, adaptable, and have less rigid assumptions about the environment configuration. Collective intelligence, as a field, studies the group intelligence that emerges from the interactions of many individuals. Within this field, ideas such as self-organization, emergent behavior, swarm optimization, and cellular automata were developed to model and explain complex systems. It is therefore natural to see these ideas incorporated into newer deep learning methods. In this review, we will provide a historical context of neural network research's involvement with complex systems, and highlight several active areas in modern deep learning research that incorporate the principles of collective intelligence to advance its current capabilities. We hope this review can serve as a bridge between the complex systems and deep learning communities.