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In recent work, we showed that stable formations can emerge through negotiation in a lower-dimensional latent space (which we called geometric embeddings). Appropriately constructed embeddings yield globally stable equilibria based solely on local observations and decisions. We extend this work by applying learning techniques to optimize the geometry of the swarm along the resultant equilibrium manifold. We extend previous work by applying reinforcement learning (RL) to control the orientation of the swarm. In a proof-of-concept, we implem this idea using Continuous Action Learning Automata to learn the optimal orientation (azimuth and altitude angle) of the embedding plane. In this implementation, collective learning is coordinated by a randomly-selected leader agent. ...

A new theory for why the Machines kept humans alive in The Matrix —inspired by recent discoveries in scaling large language models. One measure of a film’s quality is the diversity of fan theories it inspires. When a story has the right blend of depth, ambiguity, and cultural timing, the entertainment value extends past the credits—it compels audiences to dissect and reinterpret decades later. The Matrix is a great example of this: 25 years on, people are still following the white rabbit down Reddit threads. A quick internet search reveals a myriad of fan theories about the true nature of its characters and storylines. One even claims John Wick is actually a sequel to The Matrix. ...

A small story about the density of honey at airports. There I was standing in front of a stainless steel table, belongings strewn about, a sweater spilling out of my half-unzipped duffel bag. A stranger was rummaging through my stuff. I had no shoes on. I had arrived three hours early — probably too early — mostly out of anxiety surrounding the unpredictability of moments like this. Then the airport security agent pulled a small jar of honey from my bag, as if it were an emerald excavated from an ancient tomb in some long-forgotten jungle. ...