Temporal & Representational Forms of Memory

Memory in artificial agents can be understood along two complementary axes: the temporal dimension, which determines how long information persists and how it is used, and the representational form, which defines how information is structured, accessed, and recombined. Together, these axes provide the architectural grammar of memory, shaping the depth, flexibility, and adaptability of intelligence.

Temporal Dimensions

• Short-Term Memory

  • Short-term memory functions as the immediate buffer for context, signals, and transient states. It supports real-time reasoning and decision-making, holding only the most recent or relevant information.
    • Characteristics: volatile, high-frequency updates, constrained capacity.
    • Role: ensures continuity in interaction, prevents disorientation, and maintains flow in dialogue or action.
    • MAS Implication: enables rapid coordination across agents without requiring full access to long-term stores.

• Long-Term Memory

  • Long-term memory serves as the durable repository for knowledge, skills, histories, and policies. It consolidates experience and abstraction into stable forms that shape agent identity and strategy.
    • Characteristics: persistent, expansive, structured by reinforcement and reflection.
    • Role: provides the foundation for cumulative learning, identity formation, and value alignment.
    • MAS Implication: anchors collective memory, cultural persistence, and institutional norms across distributed ecosystems.

Representational Forms

• Vector Memory

  • Vector memory encodes information as neural embeddings, optimized for similarity-based access and generalization.
    • Strengths: efficient search, pattern recognition, semantic approximation.
    • Limitations: lacks explicit structure; relational detail often implicit.
    • Use Cases: perception grounding, semantic retrieval, clustering of experiences.
    • MAS Implication: enables agents to align meanings statistically, even when vocabularies diverge.

• Tree Memory

  • Tree memory organizes knowledge into hierarchical structures, supporting decision-making, planning, and decomposition.
    • Strengths: efficient for branching tasks, structured reasoning, and layered goals.
    • Limitations: rigid, struggles with overlapping or cyclical relations.
    • Use Cases: planning algorithms, hierarchical task execution, taxonomies.
    • MAS Implication: allows distributed agents to synchronize roles and responsibilities along layered hierarchies.

• Graph Memory

  • Graph memory encodes information as networks of nodes and edges, representing semantic relations, causal structures, and ontologies.
    • Strengths: highly expressive, relationally rich, adaptable to complex systems.
    • Limitations: computationally intensive, requires efficient traversal.
    • Use Cases: semantic networks, causal reasoning, knowledge graphs.
    • MAS Implication: provides the substrate for shared ontologies and world models, enabling interoperability across heterogeneous agents.