Knowledge Compilation
In Agentic RAG, a configurable ingestion pipeline serving as the supply layer, in which an LLM supplements documents with structured context such as navigation hierarchies, entity graphs, and timelines, supplying it to the model in a governable form.
Timeline (last 90 days)
Under a sufficiently capable external harness, the structured products of knowledge compilation show no detectable accuracy gain on the FRAMES benchmark.
The navigation tree mechanism (with leaf nodes being 'claim-evidence' pairs) cuts query tokens by 91% when the evidence is sufficient.
The navigation tree mechanism instead increases token consumption by 1.9% when evidence is insufficient.
The navigation tree mechanism reduces query tokens by 38% on average.
Compiled artifacts such as ontologies, graphs, wikis, timelines, mind maps, and memory show no advantage on accuracy benchmarks; their returns appear in deployment phases such as governance, integration, onboarding, temporal orientation, and continuity.
All Facts (5)
Under a sufficiently capable external harness, the structured products of knowledge compilation show no detectable accuracy gain on the FRAMES benchmark.
50%UnverifiedThe navigation tree mechanism (with leaf nodes being 'claim-evidence' pairs) cuts query tokens by 91% when the evidence is sufficient.
50%UnverifiedThe navigation tree mechanism instead increases token consumption by 1.9% when evidence is insufficient.
50%UnverifiedThe navigation tree mechanism reduces query tokens by 38% on average.
50%UnverifiedCompiled artifacts such as ontologies, graphs, wikis, timelines, mind maps, and memory show no advantage on accuracy benchmarks; their returns appear in deployment phases such as governance, integration, onboarding, temporal orientation, and continuity.
50%