ruview @ v1041

Automated release from CI pipeline

Changes: research(R8): RSSI-only person count retains 95% of full-CSI accuracy (#703)

Builds directly on R5's band-spread observation. If the count-task signal is spread across the WiFi band (R5: max/mean ratio 2.85× across 56 subcarriers), then RSSI — which is the integral of |H_k|^2 across the band — keeps most of the information. The naive prior (RSSI throws away 98% of CSI bytes) is misleading; the relevant metric is how much of the signal is in the integral, not how many bytes are in the representation.

Tested by aggregating each existing [56 × 20] CSI window down to a [20]-vector RSSI proxy (mean across subcarriers per frame), training a tiny MLP (Linear 20→32→8, 656 params, 5 KB) with vanilla NumPy SGD for 200 epochs on the same random 80/20 split as cog-person-count v0.0.2.

Result:

Full CSI v0.0.2 62.3% accuracy RSSI-only (this) 59.1% accuracy = 94.82% retained

Per-class is also markedly more balanced (RSSI: 59.5 / 58.6 ; full CSI: 86.2 / 34.3) — the tiny model on a low-dim input can't cheat by leaning on class 0 the way v0.0.2's larger model does at inference.

What this enables on a 10-year horizon: phones, laptops, smart speakers, smart TVs, smart lights — anything with WiFi reports RSSI and anything with a CPU can run a 656-param MLP. Person counting becomes a federated property of any room with WiFi, not a property of the ESP32-S3 fleet.

What this doesn't prove (called out explicitly in the research note):

• Single room, single operator, single 30-min recording
• 2-class problem (label distribution is {0, 1})
• Single random draw — needs K-fold + multi-room replication

Three follow-up experiments queued in R8-rssi-only-count.md §'What's next on this thread':

• Multi-room replication once #645 lands
• 3-class extension (0 / 1 / 2+) — measure the info-rate cliff
• Run on a non-ESP32 RSSI source (e.g. iw event on Linux laptop)

Files:

• examples/research-sota/r8_rssi_only_count.py — pure-NumPy, no framework deps. Trains + evals in 0.72 s on CPU.
• examples/research-sota/r8_rssi_only_results.json — full JSON dump for cross-tick reproducibility.
• docs/research/sota-2026-05-22/R8-rssi-only-count.md — method, measured numbers, interpretation, what doesn't work yet.
• docs/research/sota-2026-05-22/PROGRESS.md — updated index + Done log.

Coordination note: horizon-tracker is working on tools/ruview-mcp/

• tools/ruview-cli/ + ADR-104 — this commit deliberately stays out of those paths.

Docker Image: ghcr.io/ruvnet/RuView:d9ca9b368489921f460abf7948921113c0636212