Tootfinder

The psychology of collective abandonment https://www.psychologytoday.com/us/blog/harnessing-hybrid-intelligence/202511/the-psychology-of-collective-abandonment Why we choose

The Psychology of Collective Abandonment
What psychological mechanisms allow us to pour hundreds of billions into artificial intelligence while 600 million people will live in extreme poverty by 2030?

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…

One of the things that made organizing a lot easier with the GDC was a thing called "GDC in a box." It was a zip file with all kinds of resources. There was a directory structure, templates for all kinds of things like meetings and paperwork you had to file (for legal reasons) and "read me" files.
We had all kinds of support. There were people you could talk to who had been there. There were people you could call to walk through legal paperwork (taxes). Centralized orgs are vulnerable and easy to infiltrate. They're easy for states to shut down. But there are benefits to org structures.
I think it's possible to have the type of support we had with the GDC, but without the politics of an org (even the IWW). I hope this most recent essay has some of the same properties. I hope that it makes building something new, something no one has really imagined before, easier.
This whole project is something a bit different. It's a collective vision and collective project, from the ground up. Some of it has felt like a brain dump, just getting things that have been swimming around in my head down somewhere. But I hope this feels more like an invitation.
Everything thus far written is all useless unless people do things with it. Only from that point does it become a thing that lives, a thing with its own consciousness that can't be controlled by any individual human.
Tech billionaire cultists want to bring a new era of humanity with AGI. That is definitely not possible with LLMs, and may not be possible at all. But there is a super intelligence that is possible, though it's been constrained by capitalism: collective human intelligence.
The grand vision of the tech dystopians is that of the ultimate slave that can then enslave all humans on their behalf. I think we can build a humanity that can liberate itself from their grasp, crush their vision, and build for itself a world in which people will never be enslaved again. Not only do I think it's possible, I think it's necessary. I think there are only two choices: collective liberation or death.
And that's what I plan to write about next time to wrap this whole project up. Today things often feel impossible. But people talked about the Middle Ages as though they were the end of the world, and then everything changed in unimaginable ways. Everything can, and will, change again.
"The profit motive often is in conflict with the aims of art. We live in capitalism. Its power seems inescapable. So did the divine right of kings."

Bookmarked: Against the Uncritical Adoption of 'AI' Technologies in Academia #KI Under the banner of progress, products have been uncritically adopted or even imposed on user…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[5/8]:
- A Human Behavioral Baseline for Collective Governance in Software Projects
Mobina Noori, Mahasweta Chakraborti, Amy X Zhang, Seth Frey

HyperAgent: Leveraging Hypergraphs for Topology Optimization in Multi-Agent Communication
Heng Zhang, Yuling Shi, Xiaodong Gu, Zijian Zhang, Haochen You, Lubin Gan, Yilei Yuan, Jin Huang
https://arxiv.org/abs/2510.10611

HyperAgent: Leveraging Hypergraphs for Topology Optimization in Multi-Agent Communication
Recent advances in large language model-powered multi-agent systems have demonstrated remarkable collective intelligence through effective communication. However, existing approaches face two primary challenges: (i) \textit{Ineffective group collaboration modeling}, as they rely on pairwise edge representations in graph structures, limiting their ability to capture relationships among multiple agents; and (ii) \textit{Limited task-adaptiveness in communication topology design}, leading to exces…

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. …

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…

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…

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 …

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…