Journal of Agentic Meta Algorithms (agentic-meta-journal.pages.dev)

The inaugural issue of the Journal of Agentic Meta Algorithms has been announced, focusing on the exploration of algorithms that design, orchestrate, evaluate, and enhance AI agents and multi-agent systems. This initiative frames tool choice as a Partially Observable Markov Decision Process (POMDP) and critiques current self-supervisory methods like Toolformer, highlighting their inefficiencies as planners. Technical discussions include forms of belief updating and address the significance of perfect information in decision-making processes. This journal serves as a key resource for the AI/ML community by inviting innovative research on meta-algorithms and fostering collaborative engagements through reading groups and working sessions. Notable contributions include a reformulation of bilevel programming through a self-improvement loop and a comparison of specialist-agent dispatch to a stochastic multi-armed bandit framework. The emphasis on clear distinctions between gradient-based and non-gradient methods aims to refine understanding within the field, while the call for papers encourages the exploration of how these meta-algorithms can revolutionize agent-based systems.