References

Appendix

Temporal and spatial coherence

Temporal and spatial coherence algorithms are concepts primarily from computer graphics, physics simulations, and distributed systems, but they can be adapted to broader contexts like AI coordination. Here's a breakdown of these concepts:

Temporal Coherence:

Temporal coherence refers to the consistency of an attribute or state over time. In computing or simulation contexts, it often implies that changes to a system or data set are gradual, predictable, or follow a pattern.

For AI or network coordination, temporal coherence means ensuring that all nodes or agents are aligned to a common timeline or sequence of events, reducing discrepancies due to network latency or processing differences.

Spatial Coherence:

Spatial coherence refers to the consistency of an attribute or state across different points in space or within a spatial system. It assumes that entities close to each other in space will have similar properties or react similarly to changes.

AI agents in a specific network, region or server will have similar or coordinated behaviors, or that data would be consistent across different nodes in a network based on spatial relations.

Data Replication and Consistency: In distributed systems, ensuring that data is replicated or cached in a way that reflects spatial relationships, reducing the need for global updates.

By combining Temporal and Spatial Coherence to build the Hyper-Coherent Network for AI Agents:

• Temporal Algorithms would ensure that all agents are making decisions based on a coherent timeline, reducing errors from time discrepancies.

• Spatial Algorithms would help in coordinating actions among agents based on their spatial context, ensuring that nearby agents react in a synchronized manner to environmental changes.

This setup redefines efficiency, synchronization, and predictive action in AI systems, overcoming traditional barriers in multi-agent coordination.