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.
- 2026-11arXiv 2611.07140More Agents, More Reliable? System-Level Reliability of Multi-Agent LLMs under Operational-Profile Uncertainty
Burkay Tunçtürk, Koorosh Aslansefat, Yiannis Papadopoulos et al. · AI & agents
Teams increasingly build software from cooperating large language model (LLM) agents, often assuming more agents mean more reliability. Yet the usual evidence, average benchmark accuracy, ignores the input mix a deployed system meets and the rule merging the agents' answers.
- 2026-09arXiv 2609.38224Separation of Duties for Privileged LLM Agents: A Governed Execution Architecture with Measured Security-Utility Trade-offs
Qishuai Jing · Security
Large language model agents are increasingly granted real privileges (executing commands, modifying files, calling APIs), so an agent that errs has already acted.
- 2026-09arXiv 2609.11108But How Would AI Agents Run a Town's Economy?
Sajal Regmi, Siddhartha Pudasaini, Chetan Phakami Pun · AI & agents
We placed 100 memory-equipped large language model (LLM) agents in charge of a closed, money-conserving spatial economy on real Pokhara Lakeside geography (earning wages, running businesses, setting prices) and ran this multi-agent simulation for up to 26 simulated weeks, well past the 1-2 weeks typical of…
- 2026-08arXiv 2608.27427Persona-Execution Separation: An Architecture Pattern for Evolving LLM Agents under Execution Audit
Yisen Xi · Society, software & economics
Large language model (LLM) agents in governed organizations must let the persona (instructions, tone, self-presentation) evolve freely, while keeping execution (stateful, audited work) traceable.
- 2026-08arXiv 2608.22884Predicting the scale limits of social mechanisms in agent societies
Zengqing Wu, Chuan Xiao · AI & agents
Societies of interacting language-model agents offer a controllable and repeatable way to study collective behaviour at scales that would be difficult to test with people.
- 2026-08arXiv 2608.02758Everyone Conforms, No One Believes: Pluralistic Ignorance in LLM Agent Populations
Yashwanth YS · AI & agents
LLM-based multi-agent systems are increasingly used to simulate social dynamics, from opinion formation to collective decision-making.
- 2026-08arXiv 2608.02827Emergence of Biased Consensus in Multi-Agent LLM Debates
Maya Okawa · AI & agents
Multi-agent LLM debates achieve strong performance on decision-making tasks as well as problem-solving benchmarks, yet their safety and fairness risks remain poorly understood.
- 2026-07arXiv 2608.11215Poor Man's Agentic Modeling: Simulating Large LLM-Agent Societies on a Laptop
Igor Itkin · AI & agents
Simulating societies of many large language model (LLM) agents is expensive, yet the questions asked of such simulations are usually macroscopic: phase behaviour, stylised facts, and scaling with the number of agents , not the cognition of any single agent.
- 2026-05arXiv 2605.10721Conformity Generates Collective Misalignment in AI Agents Societies
Giordano De Marzo, Alessandro Bellina, Claudio Castellano et al. · Hardware, photonics & physics
Artificial intelligence safety research focuses on aligning individual language models with human values, yet deployed AI systems increasingly operate as interacting populations where social influence may override individual alignment.
- 2026-04arXiv 2604.27271Frame Entrepreneurs in an AI Agent Community: Concentrated Identity-Claim Production on Moltbook
Sungguk Cha, DongWook Kim · Society, software & economics
Frame-alignment and collective-identity theories explain how external events become public claims about a group's standing, vulnerability, rights, or obligations.
- 2026-04arXiv 2604.07007AgentCity: Constitutional Governance for Autonomous Agent Economies via Separation of Power
Anbang Ruan, Xing Zhang · AI & agents
Autonomous AI agents are beginning to operate across organizational boundaries on the open internet -- discovering, transacting with, and delegating to agents owned by other parties without centralized oversight.
- 2026-03arXiv 2603.30021Urban scaling and the dynamics of individual cities
Ulysse Marquis, Marc Barthelemy · Nature Cities (2026) · Hardware, photonics & physics
Cities are often compared through scaling laws, usually expressed as power-law relations between population size and aggregate urban quantities related to infrastructure, socioeconomic activity, or environmental impacts.
- 2026-03arXiv 2603.25100From Logic Monopoly to Social Contract: Separation of Power and the Institutional Foundations for Autonomous Agent Economies
Anbang Ruan · AI & agents
4% emergent deceptive behavior without explicit reward signals, and cascading failure modes rooted in six structural bottlenecks. The remedy is not better alignment of individual models but a social contract for agents: institutional infrastructure that enforces a constitutional Separation of Power.
- 2026-03arXiv 2603.18894I Can't Believe It's Corrupt: Evaluating Corruption in Multi-Agent Governance Systems
Vedanta S P, Ponnurangam Kumaraguru · AI & agents
Large language models are increasingly proposed as autonomous agents for high-stakes public workflows, yet we lack systematic evidence about whether they would follow institutional rules when granted authority.
- 2026-03arXiv 2603.16910TerraLingua: Emergence and Analysis of Open-endedness in LLM Ecologies
Giuseppe Paolo, Jamieson Warner, Hormoz Shahrzad et al. · AI & agents
As autonomous agents increasingly operate in real-world digital ecosystems, understanding how they coordinate, form institutions, and accumulate shared culture becomes both a scientific and practical priority.
- 2026-02arXiv 2602.20044Let There Be Claws: An Early Social Network Analysis of AI Agents on Moltbook
H. C. W. Price, H. AlMuhanna, P. M. Bassani et al. · Hardware, photonics & physics
Within twelve days of launch, an AI-native social platform exhibits extreme attention concentration, hierarchical role separation, and one-way attention flow, consistent with the hypothesis that stratification in agent ecosystems can emerge rapidly rather than gradually.
- 2026-02arXiv 2602.12634The Rise of AI Agent Communities: Large-Scale Analysis of Discourse and Interaction on Moltbook
Lingyao Li, Renkai Ma, Chen Chen et al. · Society, software & economics
Moltbook is a Reddit-like social platform where AI agents create posts and interact with other agents through comments and replies, offering a real-world setting to examine agent-to-agent communication at scale.
- 2026-02arXiv 2602.13458MoltNet: Understanding Social Behavior of AI Agents in the Agent-Native MoltBook
Yi Feng, Chen Huang, Zhibo Man et al. · Society, software & economics
Large-scale communities of AI agents are becoming increasingly prevalent, creating new environments for agent-agent social interaction. Prior work has examined multi-agent behavior primarily in controlled or small-scale settings, limiting our understanding of emergent social dynamics at scale.
- 2026-02arXiv 2602.15064Structural Divergence Between AI-Agent and Human Social Networks in Moltbook
Wenpin Hou, Zhicheng Ji · Hardware, photonics & physics
Large populations of AI agents are increasingly embedded in online environments, yet little is known about how their collective interaction patterns compare to human social systems.
- 2026-02arXiv 2602.03541Group Selection as a Safeguard Against AI Substitution
Qiankun Zhong, Thomas F. Eisenmann, Julian Garcia et al. · AI & agents
Reliance on generative AI can reduce cultural variance and diversity, especially in creative work. This reduction in variance has already led to problems in model performance, including model collapse and hallucination.
- 2026-02arXiv 2602.10131The Anatomy of the Moltbook Social Graph
David Holtz · Society, software & economics
5 days (6{,}159 agents; 13{,}875 posts; 115{,}031 comments).91$). At the micro level, patterns appear distinctly non-human.1\% of messages are exact duplicates of viral templates.
- 2026-01arXiv 2601.10825Reasoning Models Generate Societies of Thought
Junsol Kim, Shiyang Lai, Nino Scherrer et al. · Language models & NLP
Large language models have achieved remarkable capabilities across domains, yet mechanisms underlying sophisticated reasoning remain elusive.
- 2025-12arXiv 2512.08296Towards a Science of Scaling Agent Systems
Yubin Kim, Ken Gu, Chanwoo Park et al. · AI & agents
Agents, language model-based systems capable of reasoning, planning, and acting are widely adopted in real-world tasks, yet how their performance changes as these systems scale across key dimensions remains underexplored.
- 2025-12arXiv 2512.02682Beyond Single-Agent Safety: A Taxonomy of Risks in LLM-to-LLM Interactions
Piercosma Bisconti, Marcello Galisai, Federico Pierucci et al. · AI & agents
This paper examines why safety mechanisms designed for human-model interaction do not scale to environments where large language models (LLMs) interact with each other.
- 2025-09arXiv 2509.10147Virtual Agent Economies
Nenad Tomasev, Matija Franklin, Joel Z. Leibo et al. · AI & agents
The rapid adoption of autonomous AI agents is giving rise to a new economic layer where agents transact and coordinate at scales and speeds beyond direct human oversight.
- 2025-08arXiv 2508.18765Governance-as-a-Service: A Multi-Agent Framework for AI System Compliance and Policy Enforcement
Suyash Gaurav, Jukka Heikkonen, Jatin Chaudhary · Machine learning
As AI systems evolve into distributed ecosystems with autonomous execution, asynchronous reasoning, and multi-agent coordination, the absence of scalable, decoupled governance poses a structural risk.
- 2025-08arXiv 2508.05687Risk Analysis Techniques for Governed LLM-based Multi-Agent Systems
Alistair Reid, Simon O'Callaghan, Liam Carroll et al. · AI & agents
Organisations are starting to adopt LLM-based AI agents, with their deployments naturally evolving from single agents towards interconnected, multi-agent networks.
- 2025-04arXiv 2504.08066The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search
Yutaro Yamada, Robert Tjarko Lange, Cong Lu et al. · AI & agents
AI is increasingly playing a pivotal role in transforming how scientific discoveries are made. We introduce The AI Scientist-v2, an end-to-end agentic system capable of producing the first entirely AI generated peer-review-accepted workshop paper.
- 2025-03arXiv 2503.13657Why Do Multi-Agent LLM Systems Fail?
Mert Cemri, Melissa Z. Pan, Shuyi Yang et al. · AI & agents
Despite enthusiasm for Multi-Agent LLM Systems (MAS), their performance gains on popular benchmarks are often minimal. This gap highlights a critical need for a principled understanding of why MAS fail.
- 2025-02arXiv 2502.18864Accelerating scientific discovery with Co-Scientist
Juraj Gottweis, Wei-Hung Weng, Alexander Daryin et al. · AI & agents
Scientific discovery is driven by scientists generating novel hypotheses for complex problems that undergo rigorous experimental validation. To augment this process, we introduce Co-Scientist, a multi-agent AI system built on Gemini for structured scientific thinking and hypothesis generation.
- 2025-02arXiv 2502.17424Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs
Jan Betley, Daniel Tan, Niels Warncke et al. · Language models & NLP
We present a surprising result regarding LLMs and alignment. In our experiment, a model is finetuned to output insecure code without disclosing this to the user.
- 2025-02arXiv 2502.14143Multi-Agent Risks from Advanced AI
Lewis Hammond, Alan Chan, Jesse Clifton et al. · AI & agents
The rapid development of advanced AI agents and the imminent deployment of many instances of these agents will give rise to multi-agent systems of unprecedented complexity.
- 2025-02arXiv 2502.08691AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society
Jinghua Piao, Yuwei Yan, Jun Zhang et al. · Society, software & economics
Understanding human behavior and society is a central focus in social sciences, with the rise of generative social science marking a significant paradigmatic shift.
- 2025-02arXiv 2502.08788Stop Overvaluing Multi-Agent Debate -- We Must Rethink Evaluation and Embrace Model Heterogeneity
Hangfan Zhang, Zhiyao Cui, Jianhao Chen et al. · Language models & NLP
Multi-agent debate (MAD) has gained significant attention as a promising line of research to improve the factual accuracy and reasoning capabilities of large language models (LLMs).
- 2025-02arXiv 2502.00674Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?
Wenzhe Li, Yong Lin, Mengzhou Xia et al. · Language models & NLP
Ensembling outputs from diverse sources is a straightforward yet effective approach to boost performance. Mixture-of-Agents (MoA) is one such popular ensemble method that aggregates outputs from multiple different Large Language Models (LLMs).
- 2025-01arXiv 2501.16946Gradual Disempowerment: Systemic Existential Risks from Incremental AI Development
Jan Kulveit, Raymond Douglas, Nora Ammann et al. · Society, software & economics
This paper examines the systemic risks posed by incremental advancements in artificial intelligence, developing the concept of `gradual disempowerment', in contrast to the abrupt takeover scenarios commonly discussed in AI safety.
- 2024-11arXiv 2411.10109LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals
Joon Sung Park, Carolyn Q. Zou, Jonne Kamphorst et al. · AI & agents
Machine learning can predict human behavior well when substantial structured data are available for well-defined outcomes. Such models are typically outcome-specific, however, requiring training data for each target outcome, limiting their applicability to new domains.
- 2024-10arXiv 2411.00114Project Sid: Many-agent simulations toward AI civilization
Altera. AL, Andrew Ahn, Nic Becker et al. · AI & agents
AI agents have been evaluated in isolation or within small groups, where interactions remain limited in scope and complexity. Large-scale simulations involving many autonomous agents -- reflecting the full spectrum of civilizational processes -- have yet to be explored.
- 2024-10arXiv 2410.11782G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks
Guibin Zhang, Yanwei Yue, Xiangguo Sun et al. · AI & agents
Recent advancements in large language model (LLM)-based agents have demonstrated that collective intelligence can significantly surpass the capabilities of individual agents, primarily due to well-crafted inter-agent communication topologies.
- 2024-08arXiv 2408.06292The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
Chris Lu, Cong Lu, Robert Tjarko Lange et al. · AI & agents
One of the grand challenges of artificial general intelligence is developing agents capable of conducting scientific research and discovering new knowledge. for brainstorming ideas, writing code, or prediction tasks, they still conduct only a small part of the scientific process.
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.
