LLM context prototype - retrieval structure
Context-Aware Encoding for LLMs
I explored a tree-of-thoughts style encoding algorithm for organizing long-context material before it reaches a language model.
Can context be organized as a navigable structure, rather than treated as a flat bundle of retrieved snippets?
A compact encoding prototype that groups long-document context into branching relationships so retrieval has a shape the model can use.
Better model behavior often starts before generation: with the representation, compression, and routing decisions that frame the prompt.
Why it belongs here
This project is intentionally small, but it captures a useful thread in my work: interfaces and algorithms both shape what a person or model can notice. The prototype asks how we might preserve relationships across context instead of only retrieving fragments.
What I learned
The interesting part was deciding what structure should travel with a query. A tree can make context easier to inspect, summarize, and route, but it also forces design choices about granularity, hierarchy, and what counts as relevant evidence.