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This work examines the design aspects of memory, context management, and evolution within AI and AGI systems.

February 13, 2026Open Access

Design Notes on Memory, Context, Orchestration, and Self-Directed Evolution in AI and AGI Systems

Puntos clave

This work examines the design aspects of memory, context management, and evolution within AI and AGI systems.
Presents design hypotheses related to memory and context in AI
Discusses memory maps and selective context retrieval
Explores adaptive forgetting and dynamic personality in AI systems
Suggests orchestration and modular skill approaches
Analyzes self-directed updates for AI internal structures
Highlights the need for effective memory management in AGI systems
Identifies the significance of context retrieval methods
Proposes aggressive design hypotheses for evolving AI systems
Emphasizes cooperation among AI components through orchestration

Resumen

This paper presents opinionated design notes on memory, context management, orchestration, and self-directed evolution in AI and AGI systems. In this context, AI and AGI are primarily discussed with current LLM-based systems in mind. As interest in Artificial General Intelligence (AGI) continues to grow, it becomes increasingly important to examine not only model scaling or training methods, but also how systems manage memory over long time horizons, revise their own internal structures, and coordinate complex internal processes. Rather than proposing a finalized architecture or implementation, this work explores a set of aggressive design hypotheses, including memory maps, selective context retrieval, adaptive forgetting mechanisms, greed and dynamic personality, private interiority, controlled and extensible orchestration, modular skill representations, multi-model utilization, and self-directed internal updates. Related links and updates are available at: https://hajimetwi3.github.io/AGI-Systems/

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