NeuroGenesysThe research we build on.
1,514 papers by other researchers that our work cites, from continual learning and verification to language models, agents and hardware. Each entry links to the paper on arXiv and to where we use it. 301 are cited on published pages of this site; the rest in the research notes behind our studies. Our own papers are in the archive.
- 2024-07arXiv 2407.04503When LLMs Play the Telephone Game: Cultural Attractors as Conceptual Tools to Evaluate LLMs in Multi-turn Settings
Jérémy Perez, Grgur Kovač, Corentin Léger et al. · Hardware, photonics & physics
As large language models (LLMs) start interacting with each other and generating an increasing amount of text online, it becomes crucial to better understand how information is transformed as it passes from one LLM to the next.
- 2024-06arXiv 2406.07155Scaling Large Language Model-based Multi-Agent Collaboration
Chen Qian, Zihao Xie, YiFei Wang et al. · AI & agents
Recent breakthroughs in large language model-driven autonomous agents have revealed that multi-agent collaboration often surpasses each individual through collective reasoning.
- 2024-06arXiv 2406.04692Mixture-of-Agents Enhances Large Language Model Capabilities
Junlin Wang, Jue Wang, Ben Athiwaratkun et al. · Language models & NLP
Recent advances in large language models (LLMs) demonstrate substantial capabilities in natural language understanding and generation tasks. With the growing number of LLMs, how to harness the collective expertise of multiple LLMs is an exciting open direction.
- 2024-06arXiv 2406.04268Open-Endedness is Essential for Artificial Superhuman Intelligence
Edward Hughes, Michael Dennis, Jack Parker-Holder et al. · Machine learning
In recent years there has been a tremendous surge in the general capabilities of AI systems, mainly fuelled by training foundation models on internetscale data. Nevertheless, the creation of openended, ever self-improving AI remains elusive.
- 2024-05arXiv 2405.15568OMNI-EPIC: Open-endedness via Models of human Notions of Interestingness with Environments Programmed in Code
Maxence Faldor, Jenny Zhang, Antoine Cully et al. · AI & agents
Open-ended and AI-generating algorithms aim to continuously generate and solve increasingly complex tasks indefinitely, offering a promising path toward more general intelligence.
- 2024-05arXiv 2405.15815A social path to human-like artificial intelligence
Edgar A. Duéñez-Guzmán, Suzanne Sadedin, Jane X. Wang et al. · AI & agents
Traditionally, cognitive and computer scientists have viewed intelligence solipsistically, as a property of unitary agents devoid of social context.
- 2024-04arXiv 2404.16698Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents
Giorgio Piatti, Zhijing Jin, Max Kleiman-Weiner et al. · Language models & NLP
As AI systems pervade human life, ensuring that large language models (LLMs) make safe decisions remains a significant challenge.
- 2024-04arXiv 2404.02642Urban Scaling Laws
Fabiano L. Ribeiro, Vinicius M. Netto · Hardware, photonics & physics
Understanding how size influences the internal characteristics of a system is a crucial concern across various fields. Concepts like scale invariance, universalities, and fractals are fundamental to this inquiry and find application in biology, physics, and particularly urbanism.
- 2024-03arXiv 2404.00806Algorithmic Collusion by Large Language Models
Sara Fish, Yannai A. Gonczarowski, Ran I. Shorrer · Society, software & economics
We conduct experiments with algorithmic pricing agents based on Large Language Models (LLMs). In oligopoly settings, LLM-based pricing agents quickly and autonomously reach supracompetitive prices and profits.
- 2024-03arXiv 2403.13187Evolutionary Optimization of Model Merging Recipes
Takuya Akiba, Makoto Shing, Yujin Tang et al. · Nat Mach Intell (2025) · Neuroscience & neural computation
Large language models (LLMs) have become increasingly capable, but their development often requires substantial computational resources.
- 2024-03arXiv 2403.08882Cultural evolution in populations of Large Language Models
Jérémy Perez, Corentin Léger, Marcela Ovando-Tellez et al. · AI & agents
Research in cultural evolution aims at providing causal explanations for the change of culture over time. Over the past decades, this field has generated an important body of knowledge, using experimental, historical, and computational methods.
- 2024-03arXiv 2403.02419Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems
Lingjiao Chen, Jared Quincy Davis, Boris Hanin et al. · Machine learning
Many recent state-of-the-art results in language tasks were achieved using compound systems that perform multiple Language Model (LM) calls and aggregate their responses.
- 2024-02arXiv 2402.16823Language Agents as Optimizable Graphs
Mingchen Zhuge, Wenyi Wang, Louis Kirsch et al. · AI & agents
Various human-designed prompt engineering techniques have been proposed to improve problem solvers based on Large Language Models (LLMs), yielding many disparate code bases.
- 2024-02arXiv 2402.07510Secret Collusion among AI Agents: Multi-Agent Deception via Steganography
Sumeet Ramesh Motwani, Mikhail Baranchuk, Martin Strohmeier et al. · AI & agents
Recent capability increases in large language models (LLMs) open up applications in which groups of communicating generative AI agents solve joint tasks. This poses privacy and security challenges concerning the unauthorised sharing of information, or other unwanted forms of agent coordination.
- 2024-02arXiv 2402.05120More Agents Is All You Need
Junyou Li, Qin Zhang, Yangbin Yu et al. · Language models & NLP
We find that, simply via a sampling-and-voting method, the performance of large language models (LLMs) scales with the number of agents instantiated.
- 2024-01arXiv 2401.03408Escalation Risks from Language Models in Military and Diplomatic Decision-Making
Juan-Pablo Rivera, Gabriel Mukobi, Anka Reuel et al. · The 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT 24), June 3-6, 2024, Rio de Janeiro, Brazil · AI & agents
Governments are increasingly considering integrating autonomous AI agents in high-stakes military and foreign-policy decision-making, especially with the emergence of advanced generative AI models like GPT-4.
- 2023-12arXiv 2312.06942AI Control: Improving Safety Despite Intentional Subversion
Ryan Greenblatt, Buck Shlegeris, Kshitij Sachan et al. · Machine learning
As large language models (LLMs) become more powerful and are deployed more autonomously, it will be increasingly important to prevent them from causing harmful outcomes.
- 2023-10arXiv 2310.01798Large Language Models Cannot Self-Correct Reasoning Yet
Jie Huang, Xinyun Chen, Swaroop Mishra et al. · Language models & NLP
Large Language Models (LLMs) have emerged as a groundbreaking technology with their unparalleled text generation capabilities across various applications. Nevertheless, concerns persist regarding the accuracy and appropriateness of their generated content.
Cited inCoherence is selected, anchored, then pruned: a literature synthesis on logical coherence in LLM world modelsStable wrong beliefs evade every self-check: a literature synthesis on model self-knowledge and confident errorsStudy: Civilization as a superintelligence modelStudy: Cross generational teaching and examinationStudy: Logical coherence of LLM world modelsStudy: Model self knowledge and confident errorsStudy: Orchestrated specialists and bridge synthesis - 2023-08arXiv 2308.10848AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors
Weize Chen, Yusheng Su, Jingwei Zuo et al. · Language models & NLP
Autonomous agents empowered by Large Language Models (LLMs) have undergone significant improvements, enabling them to generalize across a broad spectrum of tasks.
- 2023-08arXiv 2308.00352MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework
Sirui Hong, Mingchen Zhuge, Jiaqi Chen et al. · AI & agents
Remarkable progress has been made on automated problem solving through societies of agents based on large language models (LLMs). Existing LLM-based multi-agent systems can already solve simple dialogue tasks.
- 2023-07arXiv 2307.07924ChatDev: Communicative Agents for Software Development
Chen Qian, Wei Liu, Hongzhang Liu et al. · Society, software & economics
Software development is a complex task that necessitates cooperation among multiple members with diverse skills. Numerous studies used deep learning to improve specific phases in a waterfall model, such as design, coding, and testing.
- 2023-07arXiv 2307.04709Fatal errors and misuse of mathematics in the Hong-Page Theorem and Landemore's epistemic argument
Álvaro Romaniega · Society, software & economics
In the pursuit of understanding collective intelligence, the Hong-Page Theorems have been presented as cornerstones of the interplay between diversity and ability.
- 2023-06arXiv 2306.01711OMNI: Open-endedness via Models of human Notions of Interestingness
Jenny Zhang, Joel Lehman, Kenneth Stanley et al. · AI & agents
Open-ended algorithms aim to learn new, interesting behaviors forever. That requires a vast environment search space, but there are thus infinitely many possible tasks.
- 2023-05arXiv 2305.14325Improving Factuality and Reasoning in Language Models through Multiagent Debate
Yilun Du, Shuang Li, Antonio Torralba et al. · Language models & NLP
Large language models (LLMs) have demonstrated remarkable capabilities in language generation, understanding, and few-shot learning in recent years.
- 2023-04arXiv 2304.03442Generative Agents: Interactive Simulacra of Human Behavior
Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai et al. · Society, software & economics
Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools.
- 2023-03arXiv 2303.17760CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani et al. · AI & agents
The rapid advancement of chat-based language models has led to remarkable progress in complex task-solving. However, their success heavily relies on human input to guide the conversation, which can be challenging and time-consuming.
- 2023-03arXiv 2303.16200Natural Selection Favors AIs over Humans
Dan Hendrycks · Society, software & economics
For billions of years, evolution has been the driving force behind the development of life, including humans. Evolution endowed humans with high intelligence, which allowed us to become one of the most successful species on the planet.
- 2022-05arXiv 2205.13980The Structure of Online Social Networks Mirror Those in the Offline World
R. I. M. Dunbar, Valerio Arnaboldi, Marco Conti et al. · Social Networks, Volume 43, 2015, Pages 39-47, ISSN 0378-8733 · Society, software & economics
We use data on frequencies of bi-directional posts to define edges (or relationships) in two Facebook datasets and a Twitter dataset and use these to create ego-centric social networks.
- 2021-02arXiv 2102.06698A dynamic network model of societal complexity and resilience inspired by Tainter's theory of collapse
Florian Schunck, Marc Wiedermann, Jobst Heitzig et al. · Hardware, photonics & physics
In recent years, several global events have severely disrupted economies and social structures, undermining confidence in the resilience of modern societies.
- 2020-05arXiv 2006.04948AI Research Considerations for Human Existential Safety (ARCHES)
Andrew Critch, David Krueger · Society, software & economics
Framed in positive terms, this report examines how technical AI research might be steered in a manner that is more attentive to humanity's long-term prospects for survival as a species.
- 2019-05arXiv 1905.10985AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence
Jeff Clune · AI & agents
Perhaps the most ambitious scientific quest in human history is the creation of general artificial intelligence, which roughly means AI that is as smart or smarter than humans.
- 2019-03arXiv 1903.00742Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research
Joel Z. Leibo, Edward Hughes, Marc Lanctot et al. · AI & agents
Evolution has produced a multi-scale mosaic of interacting adaptive units. Innovations arise when perturbations push parts of the system away from stable equilibria into new regimes where previously well-adapted solutions no longer work.
- 2019-01arXiv 1901.01753Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions
Rui Wang, Joel Lehman, Jeff Clune et al. · Neuroscience & neural computation
While the history of machine learning so far largely encompasses a series of problems posed by researchers and algorithms that learn their solutions, an important question is whether the problems themselves can be generated by the algorithm at the same time as they are being solved.
- 2018-03arXiv 1803.04585Categorizing Variants of Goodhart's Law
David Manheim, Scott Garrabrant · AI & agents
There are several distinct failure modes for overoptimization of systems on the basis of metrics. This occurs when a metric which can be used to improve a system is used to an extent that further optimization is ineffective or harmful, and is sometimes termed Goodhart's Law.
- 2017-11arXiv 1711.09846Population Based Training of Neural Networks
Max Jaderberg, Valentin Dalibard, Simon Osindero et al. · Machine learning
Neural networks dominate the modern machine learning landscape, but their training and success still suffer from sensitivity to empirical choices of hyperparameters such as model architecture, loss function, and optimisation algorithm.
- 2015-04arXiv 1504.04909Illuminating search spaces by mapping elites
Jean-Baptiste Mouret, Jeff Clune · AI & agents
Many fields use search algorithms, which automatically explore a search space to find high-performing solutions: chemists search through the space of molecules to discover new drugs; engineers search for stronger, cheaper, safer designs, scientists search for models that best explain data, etc.
- 2014-06arXiv 1406.2938Invention as a Combinatorial Process: Evidence from U.S. Patents
Hyejin Youn, Luis M. A. Bettencourt, Deborah Strumsky et al. · J. R. Soc. Interface 12: 20150272 2015 · Hardware, photonics & physics
Invention has been commonly conceptualized as a search over a space of combinatorial possibilities.
- 2013-01arXiv 1301.5919The hypothesis of urban scaling: formalization, implications and challenges
Luis M. A. Bettencourt, Jose Lobo, Hyejin Youn · Hardware, photonics & physics
There is strong expectation that cities, across time, culture and level of development, share much in common in terms of their form and function.
- 2012-10arXiv 1210.5215The scaling of human interactions with city size
Markus Schläpfer, Luis M. A. Bettencourt, Sebastian Grauwin et al. · J. R. Soc. Interface 11 (98), 20130789 (2014) · Hardware, photonics & physics
The size of cities is known to play a fundamental role in social and economic life. Yet, its relation to the structure of the underlying network of human interactions has not been investigated empirically in detail.
- 2012-04arXiv 1204.3463Effects of Social Influence on the Wisdom of Crowds
Pavlin Mavrodiev, Claudio J. Tessone, Frank Schweitzer · Society, software & economics
Wisdom of crowds refers to the phenomenon that the aggregate prediction or forecast of a group of individuals can be surprisingly more accurate than most individuals in the group, and sometimes - than any of the individuals comprising it.
Built from every arXiv paper cited in our papers, posts, topic hubs, reports and research notes, with titles, authors, dates and abstracts from the arXiv API (updated 2026-11-12). Summaries are the opening sentences of each paper's own abstract, not our assessment; a citation means we used or checked the work, not that we endorse it. Fields follow each paper's primary arXiv category. Papers cited only outside arXiv (journals, proceedings) are not listed yet. Entries are generated from our citations, so a mistyped ID in a note would list the wrong paper; we remove those as we find them. Report a wrong entry.
