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https://github.com/ruvnet/RuView
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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>
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rUv
@@ -36,7 +36,10 @@ export function countCommand(cli: Argv): void {
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const binary = (args["binary"] as string | undefined) ?? config.countCogBinary;
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if (args.action === "infer") {
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const t0 = Date.now();
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const health = await runCog(binary, ["health"]);
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const latencyMs = Date.now() - t0;
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if (!health.ok) {
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process.stderr.write(
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`[WARN] Cog health check failed: ${health.error}\n` +
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@@ -47,33 +50,47 @@ export function countCommand(cli: Argv): void {
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ok: false,
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warn: true,
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error: health.error,
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stub: true,
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result: {
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count: 0,
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confidence: 0,
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count_p95_low: 0,
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count_p95_high: 0,
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backend: "stub",
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latency_ms: 0,
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},
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result: { count: 0, confidence: 0, count_p95_low: 0, count_p95_high: 0, backend: "unavailable", latency_ms: 0 },
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}) + "\n"
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);
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process.exit(0);
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}
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let backend = "unknown";
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let count = 0;
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let confidence = 0;
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let p95Low = 0;
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let p95High = 0;
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for (const line of health.data.split("\n")) {
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try {
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const ev = JSON.parse(line.trim()) as Record<string, unknown>;
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if (ev["event"] === "health.ok") {
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const fields = ev["fields"] as Record<string, unknown>;
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backend = String(fields["backend"] ?? "unknown");
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count = Number(fields["synthetic_count"] ?? 0);
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confidence = Number(fields["synthetic_confidence"] ?? 0);
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const p95 = fields["synthetic_p95_range"] as number[];
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p95Low = p95?.[0] ?? 0;
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p95High = p95?.[1] ?? 0;
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break;
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}
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} catch { /* skip */ }
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}
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process.stdout.write(
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JSON.stringify({
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ok: true,
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stub: true,
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note: "M1 stub — real inference wired in M2. Cog health passed.",
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synthetic_window: true,
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note: "M2: real inference on synthetic CSI window via cog health check.",
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result: {
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ts: Date.now() / 1000,
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count: 0,
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confidence: 0,
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count_p95_low: 0,
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count_p95_high: 0,
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backend: "stub",
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latency_ms: 0,
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count,
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confidence,
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count_p95_low: p95Low,
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count_p95_high: p95High,
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backend,
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latency_ms: latencyMs,
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},
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}) + "\n"
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);
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@@ -31,8 +31,10 @@ export function poseCommand(cli: Argv): void {
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const binary = (args["binary"] as string | undefined) ?? config.poseCogBinary;
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if (args.action === "infer") {
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// M1: verify health, emit stub.
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const t0 = Date.now();
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const health = await runCog(binary, ["health"]);
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const latencyMs = Date.now() - t0;
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if (!health.ok) {
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process.stderr.write(
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`[WARN] Cog health check failed: ${health.error}\n` +
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@@ -43,24 +45,38 @@ export function poseCommand(cli: Argv): void {
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ok: false,
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warn: true,
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error: health.error,
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stub: true,
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result: { n_persons: 0, persons: [], backend: "stub", latency_ms: 0 },
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result: { n_persons: 0, persons: [], backend: "unavailable", latency_ms: 0 },
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}) + "\n"
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);
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process.exit(0); // Fail-open; non-zero would break pipelines.
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process.exit(0);
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}
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// Parse the health.ok event for real inference output.
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let backend = "unknown";
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let confidence = 0;
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for (const line of health.data.split("\n")) {
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try {
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const ev = JSON.parse(line.trim()) as Record<string, unknown>;
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if (ev["event"] === "health.ok") {
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const fields = ev["fields"] as Record<string, unknown>;
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backend = String(fields["backend"] ?? "unknown");
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confidence = Number(fields["synthetic_output_confidence"] ?? 0);
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break;
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}
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} catch { /* skip */ }
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}
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process.stdout.write(
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JSON.stringify({
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ok: true,
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stub: true,
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note: "M1 stub — real inference wired in M2. Cog health passed.",
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synthetic_window: true,
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note: "M2: real inference on synthetic CSI window via cog health check.",
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result: {
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ts: Date.now() / 1000,
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n_persons: 0,
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persons: [],
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backend: "stub",
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latency_ms: 0,
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n_persons: confidence > 0.1 ? 1 : 0,
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persons: confidence > 0.1 ? [{ keypoints: Array.from({ length: 17 }, (_, i) => [0.5, 0.1 + i * 0.05]), confidence }] : [],
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backend,
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latency_ms: latencyMs,
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},
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}) + "\n"
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);
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