Alignment in a multi-agent world
Swarm Intelligence: From Natural to Artificial Systems
Abstract. Social insects--ants, bees, termites, and wasps--can be viewed as powerful problem-solving systems with sophisticated collective intelligence. Co
Patterns and problems in multiagent systems
We ran experiments on swarms of Claude agents and found coordination failures, collusion, and sabotage. Here, we share what they mean for AI safety.
Dicklesworthstone/frankenterm
Terminal hypervisor for AI agent swarms: real-time pane capture, state-machine pattern detection, and a JSON API for coordinating fleets of coding agents across WezTerm
Agent swarms and the new model economics · Cursor
We compared old and new agent swarms building SQLite from scratch and found that better coordination delivers similar quality at a fraction of the cost.
JUMPERZ on Twitter / X
took karpathy's wiki pattern and wired it into my 10 agent swarm and here is what the architecture looks like when you make it multi agent:>every agent auto dumps its output into a raw/ folder as it works>a compiler runs every few hours and organises everything into… pic.twitter.com/bNsRKhH3Ka— JUMPERZ (@jumperz) April 3, 2026
Multi-player ai is here — nimbleco ai
First of its kind, a point and click GUI for not just managing Hermes runtimes, but also who can do what and where. Solves the multi-tenant Hermes problem. View the godhead of complexity without derealizing. Share compute.
Stanford AO — Building the autonomous organization
Stanford AO is building the autonomous organization — an incubator at Stanford OpenLab for agent swarms, DAOs, AI villages, and related experiments.
Emmett Shear: Alignment Protocols
The emergence of consensus: a primer
The origin of population-scale coordination has puzzled philosophers and scientists for centuries. Recently, game theory, evolutionary approaches and complex systems science have provided quantitative insights on the mechanisms of social consensus. However, the literature is vast and widely scattered across fields, making it hard for the single researcher to navigate it. This short review aims to provide a compact overview of the main dimensions over which the debate has unfolded and to discuss some representative examples. It focuses on those situations in which consensus emerges ‘spontaneously’ in the absence of centralized institutions and covers topics that include the macroscopic consequences of the different microscopic rules of behavioural contagion, the role of social networks and the mechanisms that prevent the formation of a consensus or alter it after it has emerged. Special attention is devoted to the recent wave of experiments on the emergence of consensus in social systems.
Coasts — Containerized Hosts for AI Agents
Free, open source parallel runtimes for AI agents. Run multiple isolated environments on your machine — no cloud, no conflicts.
Self-Organization in Biological Systems
The synchronized flashing of fireflies at night. The spiraling patterns of an aggregating slime mold. The anastomosing network of army-ant trails. The coordinated movements of a school of fish. Researchers are finding in such patterns—phenomena that have fascinated naturalists for centuries—a fertile new approach to understanding biological systems: the study of self-organization. This book, a primer on self-organization in biological systems for students and other enthusiasts, introduces readers to the basic concepts and tools for studying self-organization and then examines numerous examples of self-organization in the natural world. Self-organization refers to diverse pattern formation processes in the physical and biological world, from sand grains assembling into rippled dunes to cells combining to create highly structured tissues to individual insects working to create sophisticated societies. What these diverse systems hold in common is the proximate means by which they acquire order and structure. In self-organizing systems, pattern at the global level emerges solely from interactions among lower-level components. Remarkably, even very complex structures result from the iteration of surprisingly simple behaviors performed by individuals relying on only local information. This striking conclusion suggests important lines of inquiry: To what degree is environmental rather than individual complexity responsible for group complexity? To what extent have widely differing organisms adopted similar, convergent strategies of pattern formation? How, specifically, has natural selection determined the rules governing interactions within biological systems? Broad in scope, thorough yet accessible, this book is a self-contained introduction to self-organization and complexity in biology—a field of study at the forefront of life sciences research.
Container Use for Locally Sandboxed, Background Agents in Zed
From the Zed Blog: Run AI agents in parallel without interference using containerized environments and Git Worktrees.
