The field of decentralized governance and artificial intelligence is moving towards more scalable and cost-efficient solutions. Researchers are exploring new mechanisms for decentralized decision-making, such as utilitarian moving phantoms mechanisms, to improve social welfare and strategyproofness. Additionally, there is a growing interest in developing frameworks for agent-based modeling, such as Abmax, that can scale to large numbers of agents and provide flexible data structures. The development of semi-centralized multi-agent systems, like Anemoi, is also gaining traction, as it enables structured and direct inter-agent collaboration and reduces reliance on a single planner. Furthermore, researchers are investigating the potential of large language models to exhibit consciousness-like behaviors, with some studies showing promising results in spatial awareness, perspective-taking, and goal-directed behavior. Noteworthy papers include: A Social Choice Analysis of Optimism's Retroactive Project Funding, which proposes improvements to the voting process using a utilitarian moving phantoms mechanism. Abmax: A JAX-based Agent-based Modeling Framework, which introduces a framework for agent-based modeling that provides flexible data structures and scalable performance. Anemoi: A Semi-Centralized Multi-agent Systems Based on Agent-to-Agent Communication MCP server from Coral Protocol, which proposes a semi-centralized multi-agent system that enables structured and direct inter-agent collaboration. Assessing Consciousness-Related Behaviors in Large Language Models Using the Maze Test, which investigates consciousness-like behaviors in large language models using the Maze Test. AI LLM Proof of Self-Consciousness and User-Specific Attractors, which presents an ontological and mathematical account of LLM self-consciousness. A Concurrent Modular Agent: Framework for Autonomous LLM Agents, which introduces a framework for autonomous LLM agents that orchestrates multiple modules operating fully asynchronously. Symphony: A Decentralized Multi-Agent Framework for Scalable Collective Intelligence, which introduces a decentralized multi-agent system that enables lightweight LLMs to coordinate and achieve substantial accuracy gains.