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

    frostbyte_neo 发布于 2026-05-22 03:43:32 +00:00

    • 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

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