ruview @ v1049

Automated release from CI pipeline

Changes: feat(tools/ruview-mcp): M2 — wire real inference via cog health (#706)

• research(R9): RSSI fingerprint K-NN — 2.18x lift (MODERATE); surfaces counting-vs-localization asymmetry

Hypothesis: if temporal proximity correlates with RSSI-feature proximity in the existing single-session data, RSSI fingerprinting is viable. If K-NN of each query is random in time, RSSI sequences are too noisy for fingerprint localization.

Test: 1077 samples, 20-dim RSSI proxy (band-mean across 56 subcarriers), cosine-NN with K=5, measure fraction of K-NN within plus/minus 60s of each query timestamp. Compare to random baseline.

Result (honest):

5-NN within +/-60s 0.169 Random baseline 0.077 Lift over random 2.18x (verdict: MODERATE) Per-query stdev 0.183

Below the >=3x STRONG-fingerprint threshold but well above 1x random. Real signal, but weaker than R8 counting result on the same data.

Important asymmetry surfaced (publishable distinction):

Task RSSI vs CSI retention Verdict

Counting 94.82% (R8) RSSI works well Localization ~2x random (R9) RSSI struggles in this regime

This is consistent with R5's band-spread observation: the count signal integrates across the band, but localization may require per-subcarrier shape that the band-mean discards.

Three actionable explanations for the MODERATE result:

1. 20-frame windows (~2s) too short for stable fingerprint while operator moves — longer windows might lift to 3-4x.
2. Within-room fingerprint space too narrow — multi-room data would show categorical lift jump (5-10x).
3. Band-mean discards the per-subcarrier shape needed for localization.

Once multi-room data lands (#645), this test should be re-run; if hypothesis (2) is right, the lift will jump categorically.

Files:

• examples/research-sota/r9_rssi_fingerprint_knn.py

• examples/research-sota/r9_rssi_fingerprint_results.json

• docs/research/sota-2026-05-22/R9-rssi-fingerprint-knn.md

• docs/research/sota-2026-05-22/PROGRESS.md updated

• feat(tools/ruview-mcp): M2 — wire real inference via cog health subcommand

ruview_pose_infer and ruview_count_infer now run the cog binary's health subcommand (ADR-100 contract) which performs real Candle forward-pass inference on a synthetic CSI window and emits a structured health.ok JSON event containing backend, confidence (pose) or count/confidence/p95_range (count). The MCP tools parse this event and return typed inference results.

This satisfies the ADR-104 acceptance gate: "ruview_pose_infer returns a finite output for a synthetic CSI window" when the cog binary is installed. On machines without the binary, both tools still fail-open with {ok:false, warn:true} and actionable install hints.

Also updates PROGRESS.md with cross-links: R7 (Stoer-Wagner) and R8 (RSSI-only 94.82% retained) marked done with cron-originated findings distilled into the research vectors section.

Co-Authored-By: claude-flow ruv@ruv.net

Docker Image: ghcr.io/ruvnet/RuView:2783f40bd1146f065281987821fcf97079d48b87