Adopt RaBitQ-style binary sketches as a first-class cheap similarity
sensor at four points in the RuView pipeline: AETHER re-ID hot-cache
filter, per-room novelty / drift detection, mesh-exchange compression,
and privacy-preserving event logs. Implementation home is
ruvector-core::quantization::BinaryQuantized (already vendored, already
SIMD-accelerated NEON+POPCNT, 32x compression, 1-bit sign quantization
+ hamming distance), re-exported through a thin RuView-flavored API in
wifi-densepose-ruvector::sketch.
Pattern at every site: dense embedding -> RaBitQ sketch -> hamming
pre-filter to top-K -> full-precision refinement only on miss. Decision
boundary unchanged; sketch is a sensor that gates *which* comparisons
run, not *what* they decide.
Acceptance test (per source proposal):
- sketch compare cost reduction: 8x-30x vs full float
- top-K candidate coverage: >= 90% agreement with full-float pass
- end-to-end accuracy regression: < 1 percentage point
Site-by-site rollback if any criterion fails at a given site;
remaining sites continue. Five implementation passes, each
independently testable: ruvector module wrap, AETHER re-ID pre-filter,
cluster-Pi novelty sensor, mesh-exchange compression, privacy log.
Sensor MCU unchanged; sketches happen at the cluster Pi (ADR-083).
Validation requires acceptance numbers on >= 3 of 5 passes.
Open question (out-of-scope until pass-1 benchmark): whether RuView
embeddings need a Johnson-Lindenstrauss / RaBitQ-paper randomized
rotation before sign-quantization, or whether pure 1-bit sign
quantization (today's BinaryQuantized) is sufficient.
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