Original Reddit post

I would like to share a modest experimental project that offers an alternative perspective for understanding transformer architectures through an educational lense. The repository is available at: Qualitative Self-Attention on a colony of agents inside a Conway grid You’ve seen those minecraft computers on redstone? Here is a conversational chatbot where an LLM is simulated on top of many other LLMs and where each LLM component can be seen as a cell in a 3D voxel grid. The system simulates each transformer block as a cycle within a multi-agent society. Agents (typically 48 to 512) reside on a 96×96 grid inspired by Conway’s Game of Life. Qualitative attention emerge as agents form and strengthen dependencies based on personality traits and contextual relevance. Cliques and alliances develop through sustained interactions. Institutions and policies crystallize from repeated patterns via an institution condenser. Conflict resolution and collective regret auditing drive adaptation and role adjustments. Feedback propagate along residual-like connections, with persistent learnings blended into the social playbook graph. These processes are rendered in an isometric visualization, allowing direct viewing of how individual cell-level behaviors give rise to higher-order structures. Side-by-side comparison with a single-agent baseline further highlights the societal dynamics at work. The simulation supports heuristic mode for fully local execution without an LLM API key. It is offered as a humble educational aid for those interested in interpretability and alternative perspectives on transformer operation. This work is part of an effort to find clever analogies for mathematical algorithms in social dynamics, and to also find mathematical relevance for things we dismiss as purely social and qualitative in nature. I welcome any thoughts on how this visualization contributes to understanding transformer dynamics. Thank you for your time. Best regards, Andrew submitted by /u/causality-ai

Originally posted by u/causality-ai on r/ArtificialInteligence