Method
Offline representation
For each chunk, construct a sparse normalized TF-IDF vector. Keep its top F=4 coordinates as fuzzy branch memberships. For branch j, keep a sparse B=64 membership-weighted center. For every chunk-branch membership, retain only S=16 residual coordinates chosen from terms actually present in the chunk and store their signs, not document-specific residual amplitudes.
A zero-inclusive branch-local sign variance is shrunk toward the global term variance and converted to a mild inverse reliability weight, approximately variance^-0.2. The corpus also produces a sparse PPMI association graph and a second-order context graph for query routing.
Query routing and local scoring
The query retains real TF-IDF amplitudes. Weak second-order expansion exposes nearby branches. A branch-local signed score evaluates only the 16 stored residual coordinates and is weighted by fuzzy membership, route strength, query-mass significance, and inverse local sign variance.
Early rescue and chunk shortlist
The routed representations are cheaply ordered using geometric evidence plus whole-chunk binary IDF^1 support. The best P representations are mapped to their corresponding chunks. No dense 384/768-dimensional embedding is required at this stage.
Final chunk score
For each shortlisted chunk, the final lexical statistic is binary presence weighted by IDF squared:
sum_{t in query ∩ chunk} IDF(t)^2
It is combined with a weak length correction, query coordination, sparse semantic presence, and coverage of the three rarest query terms. The validated score components retain their per-query z-normalization.
High-quality branches and soft diversity
Branch quality is query-specific. For branch j, define branch-specific evidence E_dj using the geometric membership contribution. The quality score is the mean of the top three evidences in that branch:
H_j = mean(top3_d E_dj)
Only the ten branches with largest H_j are eligible for a diversity bonus. Rank 1 is pure relevance. For ranks 2–10, a candidate can receive a small bonus if one of its high-quality supporting branch centers deviates from the centroid of branches already represented. Repeated branches are allowed.
This is not blind diversification and it is not one-document-per-branch.