concept-synthesis

Deduplicating raw concept stubs into a tiered intellectual map involves clustering related concepts across timelines, scoring them into T1–T4 tiers by frequency and breadth, and synthesizing narratives for canonical pages. The process traces idea evolution and writes outputs to a configured concepts directory.

Concept clustering manages knowledge from ingestion pipelines by grouping near-duplicate signals and voice notes into canonical pages. It removes the noise of thousands of stubs by preserving aliases and summarizing domains into a navigable intellectual universe.

Concepts are scored and tiered into T1–T4 levels using frequency, timespan, breadth, and optional engagement signals. This scoring ensures recurring frameworks surface as canon, and LLM synthesis is applied only to T1/T2 concepts to generate rich narratives.

Yes, concept synthesis is designed for ingestion pipelines generating many duplicate concept pages like signals and voice notes. It satisfies deterministic dedup, merge, and scoring requirements to handle large-scale knowledge management before writing synthesized outputs.

The best way to trace idea evolution across sources over time is to deduplicate and merge concept stubs into canonical pages, then synthesize timelines and domain clusters. This generates a navigable intellectual map with related links and best articulations.

Knowledge management pipelines create duplicate concept pages because raw ingestion of signals, ideas, and voice notes generates many near-duplicate stubs. Deduplicating and clustering these stubs into a tiered intellectual map removes the noise and synthesizes canonical pages.