From 0c93c9eb089843f2ea0f190fac29104a7ee0f21f Mon Sep 17 00:00:00 2001 From: ruvnet Date: Tue, 2 Jun 2026 16:59:36 +0000 Subject: [PATCH] deploy: 91b0e625bd3b1a06ccb77f0c504e109019670802 --- api-docs/readme-details.md | 6 +++--- api-docs/user-guide.md | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/api-docs/readme-details.md b/api-docs/readme-details.md index 61a68364..281498ba 100644 --- a/api-docs/readme-details.md +++ b/api-docs/readme-details.md @@ -122,7 +122,7 @@ node scripts/benchmark-ruvllm.js --model models/csi-ruvllm # benchmark | What we measured | Result | Why it matters | |-----------------|--------|---------------| -| **Presence detection** | **100% accuracy** | Never misses a person, never false alarms | +| **CSI embedding quality** | **82.3% held-out temporal-triplet** | Honest label-free metric on the last 20% by time (v1's "100% presence" was a single-class recording — retracted, [#882](https://github.com/ruvnet/RuView/issues/882)) | | **Inference speed** | **0.008 ms** per embedding | 125,000x faster than real-time | | **Throughput** | **164,183 embeddings/sec** | One Mac Mini handles 1,600+ ESP32 nodes | | **Contrastive learning** | **51.6% improvement** | Strong pattern learning from real overnight data | @@ -233,7 +233,7 @@ python firmware/esp32-csi-node/provision.py --port COM9 --hop-channels "1,6,11" | **kNN similarity search** | "Find the 10 most similar states to right now" — anomaly detection, fingerprinting | Cognitum Seed | | **Witness chain** | SHA-256 tamper-evident audit trail for every measurement (1,747 entries validated) | Cognitum Seed | | **Camera-free pose training** | 17 COCO keypoints from 10 sensor signals — PIR, RSSI triangulation, subcarrier asymmetry, vibration, BME280 | 2x ESP32 + Seed | -| **Pre-trained model** | 82.8 KB (8 KB at 4-bit quantization), 100% presence accuracy, 0 skeleton violations | Download from release | +| **Pre-trained model** | 82.8 KB (8 KB at 4-bit quantization), 82.3% held-out temporal-triplet accuracy (v1's "100% presence" was single-class — retracted, [#882](https://github.com/ruvnet/RuView/issues/882)) | Download from release | | **Sub-ms inference** | 0.012 ms latency, 171,472 embeddings/sec on M4 Pro | Any machine with Node.js | | **SONA adaptation** | Adapts to new rooms in <1ms without retraining | ruvllm runtime | | **LoRA room adapters** | Per-node fine-tuning with 2,048 parameters per adapter | Automatic | @@ -262,7 +262,7 @@ node scripts/benchmark-ruvllm.js --model models/csi-ruvllm | What we measured | Result | Why it matters | |-----------------|--------|---------------| -| **Presence detection** | **100% accuracy** | Never misses a person, never false alarms | +| **CSI embedding quality** | **82.3% held-out temporal-triplet** | Honest label-free metric (v1's "100% presence" was single-class — retracted, [#882](https://github.com/ruvnet/RuView/issues/882)) | | **Person counting** | **24/24 correct** (MinCut) | Fixed the #1 user-reported issue | | **Inference speed** | **0.012 ms** per embedding | 83,000x faster than real-time | | **Throughput** | **171,472 embeddings/sec** | One Mac Mini handles 1,700+ ESP32 nodes | diff --git a/api-docs/user-guide.md b/api-docs/user-guide.md index a81d57fa..5bf2e0cb 100644 --- a/api-docs/user-guide.md +++ b/api-docs/user-guide.md @@ -1119,7 +1119,7 @@ What it ships (and what it does not): | Capability | Status | |------------|--------| -| Presence detection (occupied / empty) | ✅ Trained head — 100% accuracy on validation | +| Presence detection (occupied / empty) | ✅ Trained head — v2 encoder reports 82.3% held-out temporal-triplet acc (v1's "100% on validation" was a single-class recording — retracted, [#882](https://github.com/ruvnet/RuView/issues/882)) | | 128-dim CSI embeddings (re-ID, similarity, downstream training) | ✅ Trained encoder | | Single-person breathing / heart-rate | ⚠️ Server still uses heuristic DSP — model does not replace this yet | | 17-keypoint full-body pose | 🔬 No keypoint weights shipped yet — pose pipeline runs but without a learned head | @@ -1824,7 +1824,7 @@ huggingface-cli download ruvnet/wifi-densepose-pretrained --local-dir models/pre # model.safetensors — 48 KB contrastive encoder # model-q4.bin — 8 KB quantized (recommended) # model-q2.bin — 4 KB ultra-compact (ESP32 edge) -# presence-head.json — presence detection head (100% accuracy) +# presence-head.json — presence detection head (v2 encoder: 82.3% held-out triplet acc) # node-1.json — LoRA adapter for room 1 # node-2.json — LoRA adapter for room 2 ``` @@ -1833,7 +1833,7 @@ huggingface-cli download ruvnet/wifi-densepose-pretrained --local-dir models/pre The pre-trained encoder converts 8-dim CSI feature vectors into 128-dim embeddings. These embeddings power all 17 sensing applications: -- **Presence detection** — 100% accuracy, never misses, never false alarms +- **Presence detection** — v2 encoder: 82.3% held-out temporal-triplet accuracy (v1's "100%" was a single-class recording — retracted, [#882](https://github.com/ruvnet/RuView/issues/882)) - **Environment fingerprinting** — kNN search finds "states like this one" - **Anomaly detection** — embeddings that don't match known clusters = anomaly - **Activity classification** — different activities cluster in embedding space