A capable model handed a huge, unstructured context routinely violates constraints that are technically present but buried — Context Entropy Collapse. The Deep Context Graph (DCG) answers it by storing knowledge as a compositional, typed hypergraph and projecting a tiny, task-specific aperture instead of dumping raw text. This brief traces how DCG advanced from an agentic memory-navigator into an information-theoretic engine that computes the minimal sufficient context — and measures that it is enough.
↗ Open the full brief — with the aperture operator, the evolution timeline, and the Conditions 0–4 benchmark.
The headline result
On a reference database-migration task, replacing the 93k-token raw dump with the four-layer aperture collapses context ~600× (96,043 → 164 tokens), raises density ~300× (0.016 → 5.07 bits/token), and takes hard-constraint compliance from 0% to 100%. The brief is candid that total Shannon entropy is not the win — a repetitive dump is highly compressible — so the defensible gains are tokens and density.
- From navigation to projection: the aperture becomes a formal operator, 𝒜(τ) = σ(𝒢_D ∘ 𝒢_C ∘ 𝒢_T ∘ 𝒢_K)│τ.
- Governance-as-projection and Pearlian
do(X)causality — neither present in the original. - Measurement replaces judgment: non-circular fidelity via an independent multi-language verifier, plus an epistemic-entropy firewall and an aperture sufficiency certificate.
