Tootfinder

AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction
Song Wang, Zhen Tan, Zihan Chen, Shuang Zhou, Tianlong Chen, Jundong Li
https://arxiv.org/abs/2506.17784

AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction
Recent progress in large language model (LLM)-based multi-agent collaboration highlights the power of structured communication in enabling collective intelligence. However, existing methods largely rely on static or graph-based inter-agent topologies, lacking the potential adaptability and flexibility in communication. In this work, we propose a new framework that rethinks multi-agent coordination through a sequential structure rather than a graph structure, offering a significantly larger topo…

Collective dynamics of strategic classification
Marta C. Couto, Flavia Barsotti, Fernando P. Santos
https://arxiv.org/abs/2508.09340 https://arxiv.org/pdf/…

Collective dynamics of strategic classification
Classification algorithms based on Artificial Intelligence (AI) are nowadays applied in high-stakes decisions in finance, healthcare, criminal justice, or education. Individuals can strategically adapt to the information gathered about classifiers, which in turn may require algorithms to be re-trained. Which collective dynamics will result from users' adaptation and algorithms' retraining? We apply evolutionary game theory to address this question. Our framework provides a mathematically rigoro…

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…

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 …

Winning and losing with Artificial Intelligence: What public discourse about ChatGPT tells us about how societies make sense of technological change
Adrian Rauchfleisch, Joshua Philip Suarez, Nikka Marie Sales, Andreas Jungherr
https://arxiv.org/abs/2507.06876

Winning and losing with Artificial Intelligence: What public discourse about ChatGPT tells us about how societies make sense of technological change
Public product launches in Artificial Intelligence can serve as focusing events for collective attention, surfacing how societies react to technological change. Social media provide a window into the sensemaking around these events, surfacing hopes and fears and showing who chooses to engage in the discourse and when. We demonstrate that public sensemaking about AI is shaped by economic interests and cultural values of those involved. We analyze 3.8 million tweets posted by 1.6 million users ac…

The Generalist Brain Module: Module Repetition in Neural Networks in Light of the Minicolumn Hypothesis
Mia-Katrin Kvalsund, Mikkel Elle Lepper{\o}d
https://arxiv.org/abs/2507.12473

The Generalist Brain Module: Module Repetition in Neural Networks in Light of the Minicolumn Hypothesis
While modern AI continues to advance, the biological brain remains the pinnacle of neural networks in its robustness, adaptability, and efficiency. This review explores an AI architectural path inspired by the brain's structure, particularly the minicolumn hypothesis, which views the neocortex as a distributed system of repeated modules - a structure we connect to collective intelligence (CI). Despite existing work, there is a lack of comprehensive reviews connecting the cortical column to the …

Interesting, letting AI models cooperate.
#ai #sakana

Sakana AI
Inference-Time Scaling and Collective Intelligence for Frontier AI

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

Emergent interactions lead to collective frustration in robotic matter
Onurcan Bektas, Adolfo Alsina, Steffen Rulands
https://arxiv.org/abs/2507.22148 https://

Emergent interactions lead to collective frustration in robotic matter
Current artificial intelligence systems show near-human-level capabilities when deployed in isolation. Systems of a few collaborating intelligent agents are being engineered to perform tasks collectively. This raises the question of whether robotic matter, where many learning and intelligent agents interact, shows emergence of collective behaviour. And if so, which kind of phenomena would such systems exhibit? Here, we study a paradigmatic model for robotic matter: a stochastic many-particle sy…

Replaced article(s) found for cs.MA. https://arxiv.org/list/cs.MA/new
[1/1]:
- An AI Theory of Mind Will Enhance Our Collective Intelligence
Michael S. Harr\'e, Catherine Drysdale, Jaime Ruiz-Serra

What is emergence, after all?
Abbas K. Rizi
https://arxiv.org/abs/2507.04951 https://arxiv.org/pdf/2507.04951

What is emergence, after all?
The term emergence is increasingly used across scientific disciplines to describe phenomena that arise from interactions among a system's components but cannot be readily inferred by examining those components in isolation. While often invoked to explain higher-level behaviors, such as flocking, synchronization, or collective intelligence, the term is frequently used without precision, sometimes giving rise to ambiguity or even mystique. In this perspective paper, we clarify the scientific mean…

“But the insight was never lived. It was stored”
“I stopped wondering and started processing”
“Merlin Donald … argues that human intelligence emerged not from static memory storage but from external symbolic representation: tools like language, gesture, and writing that allowed us to rehearse, share, and restructure thought. Culture became a collective memory system - not to archive knowledge, but to keep it alive, replayed, and reworked”

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…

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…

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…

Knowledge Is More Than Performance: How Knowledge Diversity Drives Human-Human and Human-AI Interaction Synergy and Reveals Pure-AI Interaction Shortfalls
Tom Sheffer, Alon Miron, Yaniv Dover, Ariel Goldstein
https://arxiv.org/abs/2507.22889

Knowledge Is More Than Performance: How Knowledge Diversity Drives Human-Human and Human-AI Interaction Synergy and Reveals Pure-AI Interaction Shortfalls
Conversations transform individual knowledge into collective insight, allowing groups of humans and increasingly groups of artificial intelligence (AI) agents to collaboratively solve complex problems. Whether interactions between AI agents can replicate the synergy observed in human discussions remains an open question. To investigate this, we systematically compared four conversational configurations: pairs of large language models (LLM-LLM), trios of LLMs, trios of humans, and mixed human-LL…

Revisiting Gossip Protocols: A Vision for Emergent Coordination in Agentic Multi-Agent Systems
Mansura Habiba, Nafiul I. Khan
https://arxiv.org/abs/2508.01531 https://

Revisiting Gossip Protocols: A Vision for Emergent Coordination in Agentic Multi-Agent Systems
As agentic platforms scale, agents are evolving beyond static roles and fixed toolchains, creating a growing need for flexible, decentralized coordination. Today's structured communication protocols (e.g., direct agent-to-agent messaging) excel at reliability and task delegation, but they fall short in enabling emergent, swarm-like intelligence, where distributed agents continuously learn, adapt, and communicate to form collective cognition. This paper revisits gossip protocols, long valued in …

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…

Towards Cognitive Synergy in LLM-Based Multi-Agent Systems: Integrating Theory of Mind and Critical Evaluation
Adam Kostka, Jaros{\l}aw A. Chudziak
https://arxiv.org/abs/2507.21969

Towards Cognitive Synergy in LLM-Based Multi-Agent Systems: Integrating Theory of Mind and Critical Evaluation
Recently, the field of Multi-Agent Systems (MAS) has gained popularity as researchers are trying to develop artificial intelligence capable of efficient collective reasoning. Agents based on Large Language Models (LLMs) perform well in isolated tasks, yet struggle with higher-order cognition required for adaptive collaboration. Human teams achieve synergy not only through knowledge sharing, but also through recursive reasoning, structured critique, and the ability to infer others' mental states…