From prompt pile to context structure
Large context windows make it possible to provide models with extraordinary amounts of text. They do not guarantee that the resulting context is coherent, efficiently arranged, or explicit about which concepts and relationships matter most.
Semantic latticing explores a different posture: compile a task-specific cognitive tapestry from stable concepts, relationships, evidence, and constraints, while preserving enough provenance to explain why each part was admitted.
Progressive disclosure
Not every reasoning step needs the entire knowledge substrate. A lattice can expose a compact semantic neighborhood first, then disclose additional evidence, history, or detail when the task requires it.
That makes context construction a policy problem with observable inputs and outputs, rather than a hidden prompt-assembly trick.