Tootfinder

Measuring Implicit Spatial Coordination in Teams: Effects on Collective Intelligence and Performance
Thuy Ngoc Nguyen, Anita Williams Woolley, Cleotilde Gonzalez
https://arxiv.org/abs/2509.09314

Measuring Implicit Spatial Coordination in Teams: Effects on Collective Intelligence and Performance
Coordinated teamwork is essential in fast-paced decision-making environments that require dynamic adaptation, often without an opportunity for explicit communication. Although implicit coordination has been extensively considered in the existing literature, the majority of work has focused on co-located, synchronous teamwork (such as sports teams) or, in distributed teams, primarily on coordination of knowledge work. However, many teams (firefighters, military, law enforcement, emergency respon…

The Computational Foundations of Collective Intelligence
Charlie Pilgrim, Joe Morford, Elizabeth Warren, M\'elisande Aellen, Christopher Krupenye, Richard P Mann, Dora Biro
https://arxiv.org/abs/2509.07999

The Computational Foundations of Collective Intelligence
Why do collectives outperform individuals when solving some problems? Fundamentally, collectives have greater computational resources with more sensory information, more memory, more processing capacity, and more ways to act. While greater resources present opportunities, there are also challenges in coordination and cooperation inherent in collectives with distributed, modular structures. Despite these challenges, we show how collective resource advantages lead directly to well-known forms of …

Crosslisted article(s) found for physics.soc-ph. https://arxiv.org/list/physics.soc-ph/new
[1/1]:
- The Computational Foundations of Collective Intelligence
Pilgrim, Morford, Warren, Aellen, Krupenye, Mann, Biro

HumanAgencyBench: Scalable Evaluation of Human Agency Support in AI Assistants
Benjamin Sturgeon, Daniel Samuelson, Jacob Haimes, Jacy Reese Anthis
https://arxiv.org/abs/2509.08494

HumanAgencyBench: Scalable Evaluation of Human Agency Support in AI Assistants
As humans delegate more tasks and decisions to artificial intelligence (AI), we risk losing control of our individual and collective futures. Relatively simple algorithmic systems already steer human decision-making, such as social media feed algorithms that lead people to unintentionally and absent-mindedly scroll through engagement-optimized content. In this paper, we develop the idea of human agency by integrating philosophical and scientific theories of agency with AI-assisted evaluation me…

Crosslisted article(s) found for cs.NE. https://arxiv.org/list/cs.NE/new
[1/1]:
- The Computational Foundations of Collective Intelligence
Pilgrim, Morford, Warren, Aellen, Krupenye, Mann, Biro

A Decade-long Landscape of Advanced Persistent Threats: Longitudinal Analysis and Global Trends
Shakhzod Yuldoshkhujaev (Sungkyunkwan University), Mijin Jeon (Sungkyunkwan University), Doowon Kim (University of Tennessee), Nick Nikiforakis (Stony Brook University), Hyungjoon Koo (Sungkyunkwan University)
https://arxiv.org/abs/2509.07457

A Decade-long Landscape of Advanced Persistent Threats: Longitudinal Analysis and Global Trends
An advanced persistent threat (APT) refers to a covert, long-term cyberattack, typically conducted by state-sponsored actors, targeting critical sectors and often remaining undetected for long periods. In response, collective intelligence from around the globe collaborates to identify and trace surreptitious activities, generating substantial documentation on APT campaigns publicly available on the web. While prior works predominantly focus on specific aspects of APT cases, such as detection, e…

Crosslisted article(s) found for cs.MA. https://arxiv.org/list/cs.MA/new
[1/1]:
- The Computational Foundations of Collective Intelligence
Pilgrim, Morford, Warren, Aellen, Krupenye, Mann, Biro

A hacking collective calling itself "Scattered LapSus Hunters," has threatened to leak Google databases unless the company sacks two senior employees. Whilst the group has yet to provide any evidence that it holds Google data, Google has recently disclosed a third-party security breach involving Salesforce.

Hackers threaten Google with data leak unless company fires two threat intelligence employees
A hacking collective calling itself "Scattered LapSus Hunters," has threatened to leak Google databases unless the company sacks two senior employees. Whilst the group has ...

Simulacra Naturae: Generative Ecosystem driven by Agent-Based Simulations and Brain Organoid Collective Intelligence
Nefeli Manoudaki, Mert Toka, Iason Paterakis, Diarmid Flatley
https://arxiv.org/abs/2509.02924

Simulacra Naturae: Generative Ecosystem driven by Agent-Based Simulations and Brain Organoid Collective Intelligence
Simulacra Naturae is a data-driven media installation that explores collective care through the entanglement of biological computation, material ecologies, and generative systems. The work translates pre-recorded neural activity from brain organoids, lab-grown three-dimensional clusters of neurons, into a multi-sensory environment composed of generative visuals, spatial audio, living plants, and fabricated clay artifacts. These biosignals, streamed through a real-time system, modulate emergent …

Crosslisted article(s) found for nlin.AO. https://arxiv.org/list/nlin.AO/new
[1/1]:
- The Computational Foundations of Collective Intelligence
Pilgrim, Morford, Warren, Aellen, Krupenye, Mann, Biro

Crosslisted article(s) found for physics.soc-ph. https://arxiv.org/list/physics.soc-ph/new
[1/1]:
- The Computational Foundations of Collective Intelligence
Pilgrim, Morford, Warren, Aellen, Krupenye, Mann, Biro

LLM-Assisted Iterative Evolution with Swarm Intelligence Toward SuperBrain
Li Weigang, Pedro Carvalho Brom, Lucas Ramson Siefert
https://arxiv.org/abs/2509.00510 https://…

LLM-Assisted Iterative Evolution with Swarm Intelligence Toward SuperBrain
We propose a novel SuperBrain framework for collective intelligence, grounded in the co-evolution of large language models (LLMs) and human users. Unlike static prompt engineering or isolated agent simulations, our approach emphasizes a dynamic pathway from Subclass Brain to Superclass Brain: (1) A Subclass Brain arises from persistent, personalized interaction between a user and an LLM, forming a cognitive dyad with adaptive learning memory. (2) Through GA-assisted forward-backward evolution, …

Secure Multi-LLM Agentic AI and Agentification for Edge General Intelligence by Zero-Trust: A Survey
Yinqiu Liu, Ruichen Zhang, Haoxiang Luo, Yijing Lin, Geng Sun, Dusit Niyato, Hongyang Du, Zehui Xiong, Yonggang Wen, Abbas Jamalipour, Dong In Kim, Ping Zhang
https://arxiv.org/abs/2508.19870

Secure Multi-LLM Agentic AI and Agentification for Edge General Intelligence by Zero-Trust: A Survey
Agentification serves as a critical enabler of Edge General Intelligence (EGI), transforming massive edge devices into cognitive agents through integrating Large Language Models (LLMs) and perception, reasoning, and acting modules. These agents collaborate across heterogeneous edge infrastructures, forming multi-LLM agentic AI systems that leverage collective intelligence and specialized capabilities to tackle complex, multi-step tasks. However, the collaborative nature of multi-LLM systems int…

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…

“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”

Diffusion-Guided Renormalization of Neural Systems via Tensor Networks
Nathan X. Kodama
https://arxiv.org/abs/2510.06361 https://arxiv.org/pdf/2510.06361…

Diffusion-Guided Renormalization of Neural Systems via Tensor Networks
Far from equilibrium, neural systems self-organize across multiple scales. Exploiting multiscale self-organization in neuroscience and artificial intelligence requires a computational framework for modeling the effective non-equilibrium dynamics of stochastic neural trajectories. Non-equilibrium thermodynamics and representational geometry offer theoretical foundations, but we need scalable data-driven techniques for modeling collective properties of high-dimensional neural networks from partia…

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…

Adaptive Deception Framework with Behavioral Analysis for Enhanced Cybersecurity Defense
Basil Abdullah AL-Zahrani
https://arxiv.org/abs/2510.02424 https://

Adaptive Deception Framework with Behavioral Analysis for Enhanced Cybersecurity Defense
This paper presents CADL (Cognitive-Adaptive Deception Layer), an adaptive deception framework achieving 99.88% detection rate with 0.13% false positive rate on the CICIDS2017 dataset. The framework employs ensemble machine learning (Random Forest, XGBoost, Neural Networks) combined with behavioral profiling to identify and adapt responses to network intrusions. Through a coordinated signal bus architecture, security components share real-time intelligence, enabling collective decision-making. …

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…

“Against the Uncritical Adoption of 'AI' Technologies in Academia”
https://zenodo.org/records/17065099

Toxicity in Online Platforms and AI Systems: A Survey of Needs, Challenges, Mitigations, and Future Directions
Smita Khapre, Melkamu Abay Mersha, Hassan Shakil, Jonali Baruah, Jugal Kalita
https://arxiv.org/abs/2509.25539

Toxicity in Online Platforms and AI Systems: A Survey of Needs, Challenges, Mitigations, and Future Directions
The evolution of digital communication systems and the designs of online platforms have inadvertently facilitated the subconscious propagation of toxic behavior. Giving rise to reactive responses to toxic behavior. Toxicity in online content and Artificial Intelligence Systems has become a serious challenge to individual and collective well-being around the world. It is more detrimental to society than we realize. Toxicity, expressed in language, image, and video, can be interpreted in various …

Machines in the Margins: A Systematic Review of Automated Content Generation for Wikipedia
Neal Reeves, Elena Simperl
https://arxiv.org/abs/2509.22443 https://

Machines in the Margins: A Systematic Review of Automated Content Generation for Wikipedia
Wikipedia is among the largest examples of collective intelligence on the Web with over 61 million articles covering over 320 languages. Although edited and maintained by an active workforce of human volunteers, Wikipedia is highly reliant on automated bots to fill gaps in its human workforce. As well as administrative and governance tasks, these bots also play a role in generating content, although to date such agents represent the smallest proportion of bots. While there has been considerable…

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 …

The Economics and Game Theory of OSINT Frontline Photography: Risk, Attention, and the Collective Dilemma
Jonathan Teagan
https://arxiv.org/abs/2509.10548 https://

The Economics and Game Theory of OSINT Frontline Photography: Risk, Attention, and the Collective Dilemma
This paper develops an economic model of the Open Source Intelligence (OSINT) attention economy in contemporary armed conflict. We conceptualize attention (e.g. social media views, followers, likes) as revenue, and time and risk spent in analysis as costs. Using utility functions and simple game theoretic setups, we show how OSINT actors (amateurs, journalists, analysts, and state operatives) allocate effort to maximize net attention benefit. We incorporate strategic behaviors such as a first m…

From Pheromones to Policies: Reinforcement Learning for Engineered Biological Swarms
Aymeric Vellinger, Nemanja Antonic, Elio Tuci
https://arxiv.org/abs/2509.20095 https://

From Pheromones to Policies: Reinforcement Learning for Engineered Biological Swarms
Swarm intelligence emerges from decentralised interactions among simple agents, enabling collective problem-solving. This study establishes a theoretical equivalence between pheromone-mediated aggregation in \celeg\ and reinforcement learning (RL), demonstrating how stigmergic signals function as distributed reward mechanisms. We model engineered nematode swarms performing foraging tasks, showing that pheromone dynamics mathematically mirror cross-learning updates, a fundamental RL algorithm. E…

From Pheromones to Policies: Reinforcement Learning for Engineered Biological Swarms
Aymeric Vellinger, Nemanja Antonic, Elio Tuci
https://arxiv.org/abs/2509.20095 https://

From Pheromones to Policies: Reinforcement Learning for Engineered Biological Swarms
Swarm intelligence emerges from decentralised interactions among simple agents, enabling collective problem-solving. This study establishes a theoretical equivalence between pheromone-mediated aggregation in \celeg\ and reinforcement learning (RL), demonstrating how stigmergic signals function as distributed reward mechanisms. We model engineered nematode swarms performing foraging tasks, showing that pheromone dynamics mathematically mirror cross-learning updates, a fundamental RL algorithm. E…

MAS$^2$: Self-Generative, Self-Configuring, Self-Rectifying Multi-Agent Systems
Kun Wang, Guibin Zhang, ManKit Ye, Xinyu Deng, Dongxia Wang, Xiaobin Hu, Jinyang Guo, Yang Liu, Yufei Guo
https://arxiv.org/abs/2509.24323

MAS$^2$: Self-Generative, Self-Configuring, Self-Rectifying Multi-Agent Systems
The past two years have witnessed the meteoric rise of Large Language Model (LLM)-powered multi-agent systems (MAS), which harness collective intelligence and exhibit a remarkable trajectory toward self-evolution. This paradigm has rapidly progressed from manually engineered systems that require bespoke configuration of prompts, tools, roles, and communication protocols toward frameworks capable of automated orchestration. Yet, dominant automatic multi-agent systems, whether generated by extern…

CogniAlign: Survivability-Grounded Multi-Agent Moral Reasoning for Safe and Transparent AI
Hasin Jawad Ali, Ilhamul Azam, Ajwad Abrar, Md. Kamrul Hasan, Hasan Mahmud
https://arxiv.org/abs/2509.13356

CogniAlign: Survivability-Grounded Multi-Agent Moral Reasoning for Safe and Transparent AI
The challenge of aligning artificial intelligence (AI) with human values persists due to the abstract and often conflicting nature of moral principles and the opacity of existing approaches. This paper introduces CogniAlign, a multi-agent deliberation framework based on naturalistic moral realism, that grounds moral reasoning in survivability, defined across individual and collective dimensions, and operationalizes it through structured deliberations among discipline-specific scientist agents. …

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…

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 …