Tootfinder

LLM-Powered Swarms: A New Frontier or a Conceptual Stretch?
Muhammad Atta Ur Rahman, Melanie Schranz
https://arxiv.org/abs/2506.14496 https://

LLM-Powered Swarms: A New Frontier or a Conceptual Stretch?
Swarm intelligence traditionally refers to systems of simple, decentralized agents whose local interactions lead to emergent, collective behavior. Recently, the term 'swarm' has been extended to describe AI systems like OpenAI's Swarm, where large language models (LLMs) act as collaborative agents. This paper contrasts traditional swarm algorithms with LLM-driven swarms exploring how decentralization, scalability, and emergence are redefined in modern artificial intelligence (AI). We implement …

Collective Intelligence Outperforms Individual Talent: A Case Study in League of Legends
Angelo Josey Caldeira, Sajan Maharjan, Srijoni Majumdar, Evangelos Pournaras
https://arxiv.org/abs/2506.02706

Collective Intelligence Outperforms Individual Talent: A Case Study in League of Legends
Gaming environments are popular testbeds for studying human interactions and behaviors in complex artificial intelligence systems. Particularly, in multiplayer online battle arena (MOBA) games, individuals collaborate in virtual environments of high realism that involves real-time strategic decision-making and trade-offs on resource management, information collection and sharing, team synergy and collective dynamics. This paper explores whether collective intelligence, emerging from cooperative…

This https://arxiv.org/abs/2505.22767 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

In Dialogue with Intelligence: Rethinking Large Language Models as Collective Knowledge
Large Language Models (LLMs) are typically analysed through architectural, behavioural, or training-data lenses. This article offers a theoretical and experiential re-framing: LLMs as dynamic instantiations of Collective human Knowledge (CK), where intelligence is evoked through dialogue rather than stored statically. Drawing on concepts from neuroscience and AI, and grounded in sustained interaction with ChatGPT-4, I examine emergent dialogue patterns, the implications of fine-tuning, and the …

This https://arxiv.org/abs/2502.12660 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Collective Intelligence in Dynamic Networks
We revisit DeGroot learning to examine the robustness of social learning in dynamic networks -- networks that evolve randomly over time. Dynamics have double-edged effects depending on social structure: while they can foster consensus and boost collective intelligence in "sparse" networks, they can have adverse effects such as slowing down the speed of learning and causing long-run disagreement in "well-connected" networks. Collective intelligence arises in dynamic networks when average influen…

We in Game Workers Unite Ireland stand in solidarity with Animation Workers of Ireland, and the international trade union coalition happening to organise against the threat of AI in the animation industry, and we support their call to acton.
Join them Thursday 12 June at 14:00 at the Pâquier in Annecy!!
#Animation

Recommender systems, stigmergy, and the tyranny of popularity
Zackary Okun Dunivin, Paul E. Smaldino
https://arxiv.org/abs/2506.06162 https://

Recommender systems, stigmergy, and the tyranny of popularity
Scientific recommender systems, such as Google Scholar and Web of Science, are essential tools for discovery. Search algorithms that power work through stigmergy, a collective intelligence mechanism that surfaces useful paths through repeated engagement. While generally effective, this ``rich-get-richer'' dynamic results in a small number of high-profile papers that dominate visibility. This essay argues argue that these algorithm over-reliance on popularity fosters intellectual homogeneity and…

Ethical AI: Towards Defining a Collective Evaluation Framework
Aasish Kumar Sharma, Dimitar Kyosev, Julian Kunkel
https://arxiv.org/abs/2506.00233 https://…

Ethical AI: Towards Defining a Collective Evaluation Framework
Artificial Intelligence (AI) is transforming sectors such as healthcare, finance, and autonomous systems, offering powerful tools for innovation. Yet its rapid integration raises urgent ethical concerns related to data ownership, privacy, and systemic bias. Issues like opaque decision-making, misleading outputs, and unfair treatment in high-stakes domains underscore the need for transparent and accountable AI systems. This article addresses these challenges by proposing a modular ethical assess…

This https://arxiv.org/abs/2402.16887 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

A Comprehensive Survey on Artificial Intelligence for Complex Network: Potential, Methodology and Application
Complex networks pervade various real-world systems, from the natural environment to human societies. The essence of these networks is in their ability to transition and evolve from microscopic disorder-where network topology and node dynamics intertwine-to a macroscopic order characterized by certain collective behaviors. Over the past two decades, complex network science has significantly enhanced our understanding of the statistical mechanics, structures, and dynamics underlying real-world n…

This https://arxiv.org/abs/2410.08948 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

Emergent social conventions and collective bias in LLM populations
Social conventions are the backbone of social coordination, shaping how individuals form a group. As growing populations of artificial intelligence (AI) agents communicate through natural language, a fundamental question is whether they can bootstrap the foundations of a society. Here, we present experimental results that demonstrate the spontaneous emergence of universally adopted social conventions in decentralized populations of large language model (LLM) agents. We then show how strong coll…