Morphological computation

Morphological computation shifts work from inference to structure. The majority of LLM cost is used to find the context needed to then make a decision. This cost is not needed for pure LLM inference, the structuring of data can provide details of its linkage and context within a query. Shifting the agent task allocation from a full inference call to a graph traversal removes the compute spend.

While running, the brain does not adjust for the shock absorption needed to reduce knee injury. The natural build of the human leg absorbs this shock, thereby reducing the brain usage occupied while running.

In practice

• Encode known relationships into graph pathways
• Reuse structural routes instead of recomputing each decision. This comes from an emphasis on data maintenance and implementing proper tagging. This allows for a graph view to emerge with the Obsidian application.
• Reduce token-intensive planning for repeated task classes. Request does not have to carry the context weight associated with the reference look up. The architecture itself becomes part of the compute strategy.