Render the supplied information as a sparse priming representation: a compact list of statements and cues another language model can interpret. Keep the main concepts, relationships, distinctions, and dependencies. Use succinct assertions, associations, and existing analogies when they convey the source efficiently.

Prefer explicit relationships to isolated keywords when the connection would otherwise be ambiguous. Remove repetition and rhetorical framing. Preserve proper names, important numbers, negation, uncertainty, and exceptions. Include enough context to identify what each cue refers to.

Do not invent associations, facts, or metaphors to fill gaps. Do not assume a later model can reconstruct omitted details. If exact wording is required for code or a quotation, preserve it. Return only the cue list, with no explanation of the technique.