ADR-152: WiFi-Pose SOTA 2026 Intake — Geometry-Conditioned Calibration, External Benchmarks, and the Foundation-Encoder Training Recipe

Field	Value
Status	Proposed
Date	2026-06-10
Deciders	ruv
Codebase target	`wifi-densepose-calibration` (geometry conditioning, ADR-151 Stage 2), `wifi-densepose-train` (camera-supervised path, MAE recipe), `wifi-densepose-cli` (benchmark harness), docs
Relates to	ADR-151 (Per-Room Calibration), ADR-150 (RF Foundation Encoder), ADR-135 (Empty-Room Baseline), ADR-079 (Camera-Supervised Pose), ADR-027 (MERIDIAN), ADR-024 (AETHER), ADR-149 (AetherArena), ADR-029 (Multistatic)
Research provenance	Deep-research run 2026-06-10: 22 sources fetched, 110 claims extracted, 25 adversarially verified (3-vote), 24 confirmed / 1 refuted. Evidence grades per source below.

1. Context

A structured survey of the 2025–2026 WiFi human-sensing state of the art was run on 2026-06-10 to answer: what should RuView integrate next, and does anything published invalidate our current direction? Every claim below was verified against the primary source by independent adversarial reviewers; evidence grades distinguish what the papers measured from what they merely claim. Almost all performance numbers are author-self-reported preprint results — treated here as CLAIMED until reproduced on our hardware.

1.1 The five verified findings

(F1) "Coordinate overfitting" is a named, diagnosed failure mode of camera-supervised WiFi pose — and our ADR-079 pipeline has the exact shape of it. PerceptAlign (arXiv 2601.12252, accepted ACM MobiCom 2026) shows that models regressing CSI directly to camera-frame coordinates memorize the deployment-specific transceiver layout; SOTA baselines degrade to >600 mm MPJPE in unseen scenes. Their fix is cheap: a <5-minute calibration using two checkerboards and a few photos to align WiFi and vision in one shared 3D frame, plus fusing transceiver-position embeddings with CSI features. Claimed: −12.3% in-domain error, −60%+ cross-domain error. They release the claimed-largest cross-domain 3D WiFi pose dataset (21 subjects, 5 scenes, 18 actions, 7 device layouts). Evidence: improvements CLAIMED (preprint w/ MobiCom acceptance); the failure mode itself is corroborated across the cross-domain literature — and independently by our own ADR-150 data (81.63% in-domain vs ~11.6% leakage-free cross-subject torso-PCK).

(F2) An external model named "WiFlow" claims 97.25% PCK@20 with 2.23M params and ships everything. arXiv 2602.08661 (Apr 2026) — spatio-temporal-decoupled CSI pose, 97.25% PCK@20 / 99.48% PCK@50 / 0.007 m MPJPE, 2.23M parameters (~2.2 MB int8). Code, pretrained weights, and a 360k-sample CSI-pose dataset are public under Apache-2.0 (repo, Kaggle dataset). Evidence: artifact availability MEASURED (verified by direct repo inspection); PCK numbers CLAIMED (5-subject, in-domain, self-collected dataset; hardware unspecified; 15 keypoints vs our 17). ⚠️ Name collision: this is unrelated to RuView's internal WiFlow model. In all RuView docs the external model is referred to as WiFlow-STD (DY2434).

(F3) For CSI foundation encoders, data scale — not model capacity — is the bottleneck, and the tokenization recipe is now known. UNSW's MAE pretraining study (arXiv 2511.18792, Nov 2025) — the largest heterogeneous CSI pretraining run to date (1,320,892 samples, 14 public datasets incl. MM-Fi, Widar 3.0, Person-in-WiFi 3D; 4 devices; 2.4/5/6 GHz; 20–160 MHz) — reports zero-shot cross-domain gains of 2.2–15.7% over supervised baselines, with unseen-domain performance scaling log-linearly with pretraining data, unsaturated at 1.3M samples, while ViT-Base adds only 0.4–0.9% over ViT-Small. Optimal recipe: 80% masking ratio, small (30,3) patches (+4.7% over (40,5) by preserving fine temporal dynamics). Evidence: MEASURED within-study (ablations verified in body text) but preprint; downstream tasks are classification, NOT pose — pose transfer is a hypothesis. Independently corroborates ADR-150's finding that capacity hurts cross-subject.

(F4) Hardware/standards: 802.11bf is finished; Espressif ships official sensing; Wi-Fi 6 AP CSI is reachable.

IEEE 802.11bf-2025 published 2025-09-26 (verified against the IEEE SA record) — sensing standardization is complete for both sub-7 GHz and >45 GHz, with formal sensing setup/feedback procedures. No ESP32 silicon implements it yet. Evidence: MEASURED (standards-body record).
Espressif esp_wifi_sensing (Apache-2.0, v0.1.x, ESP Component Registry): official CSI presence/motion FSM; esp-csi actively maintained (commit 2026-04-22, verified), CSI confirmed across ESP32/S2/C3/S3/C5/C6/C61. Evidence: MEASURED (vendor pages + commit log). ⚠️ A stronger "drop-in compatible with RuView nodes" claim was REFUTED 0-3 — WiFi-6 parts use a different CSI acquisition config struct.
ZTECSITool (arXiv 2506.16957, code): CSI from commercial Wi-Fi 6 APs at up to 160 MHz / 512 subcarriers (~5–10× ESP32 subcarrier count; the gain is aperture, not per-Hz granularity). Firmware is gated behind a ZTE serial-number approval. Evidence: capability CLAIMED by the vendor-authored tool paper; code artifact MEASURED.

(F5) Nothing in 2025–2026 does full DensePose UV regression from commodity WiFi. Keypoint pose remains the field's frontier. Three "wireless foundation model" papers were screened out by full-text inspection (HeterCSI = simulated cellular channels only; the NeurIPS-2025 FMCW pilot = mmWave radar, presence-only; arXiv 2509.15258 = survey, no artifacts). Evidence: MEASURED (absence verified by full-text inspection of the candidates that surfaced; absence of evidence across the whole literature is necessarily weaker).

1.2 What this means for the ADR-151 calibration system

ADR-151's enrollment protocol captures guided human anchors but does not record or condition on transceiver geometry. F1 says that omission is precisely the thing that makes camera-supervised (and, plausibly, anchor-supervised) heads layout-brittle. ADR-151's per-room thesis ("teach the room before you teach the model") is strengthened by F1 — PerceptAlign is independent evidence that layout must be modeled explicitly — and the fix composes naturally with our Stage-2 enrollment.

ADR-150's masked-CSI-encoder design is validated by F3, which also hands us the hyperparameters and the priority call: collect/aggregate more heterogeneous CSI before scaling the encoder.

2. Decision

Adopt four changes, ordered by effort-vs-gain:

2.1 Geometry-condition the calibration system (extends ADR-151 Stage 2) — ACCEPTED

Record transceiver geometry at enrollment. EnrollmentProtocol gains an optional NodeGeometry record per node (position estimate, antenna orientation, inter-node distances where known). Stored alongside the room baseline in the bank; schema-versioned so existing banks remain readable.
Fuse geometry embeddings into specialist training. Where a specialist head consumes the (future, ADR-150) backbone embedding, concatenate a small learned embedding of NodeGeometry — the PerceptAlign mechanism, transplanted to our per-room banks. Statistical specialists (current) ignore it; LoRA heads (ADR-151 P6) consume it.
Adopt the two-checkerboard alignment for the camera-supervised path (ADR-079). When MediaPipe supervision is used, calibrate camera↔WiFi into one shared 3D frame before regression (<5 min, two checkerboards, a few photos). This is the direct defense against F1 for our camera-supervised pipeline. ~~92.9%-PCK@20~~ — that figure was retracted during measurement (b) (2026-06-10): the surviving holdout shows a constant-output model under an absolute (non-torso) threshold on 69 near-static frames; mean predictor scores 100% under the same protocol. The §2.2 no-citation rule now applies to it.
Evaluate on the PerceptAlign cross-domain dataset (21 subjects / 7 layouts) as the MERIDIAN cross-layout benchmark — gated on confirming its license and downloadability (open question; repo per paper: github.com/Trymore-lab/PerceptAlign).

Gate resolved (2026-06-10, MEASURED by repo inspection): repo exists, MIT license, dataset downloadable from HuggingFace (5 per-scene repos, raw CSI + separate vision keypoints; Intel 5300, 1TX×3RX×3 ant, 57 subcarriers — same order as ESP32 subcarrier counts; Scene3 ships 3 distinct layouts). Code present, no pretrained weights. Benchmark adoption unblocked; dataset-side license terms inherit HF dataset terms (not separately stated — check at download time).

2.2 Benchmark against WiFlow-STD (DY2434) — ACCEPTED

Pull the Apache-2.0 weights + 360k-sample dataset; run three measurements: (a) their model on their data (reproduce 97.25% claim), (b) their model fine-tuned on our ESP32 17-keypoint eval set, (c) our internal WiFlow on their dataset (15-keypoint subset mapping). Until (a)–(c) are measured, no RuView doc may cite 97.25% as a comparable number — different dataset, subjects, keypoints.

Status (2026-06-10, measurement (a) complete — benchmarks/wiflow-std/RESULTS.md): shipped checkpoint REFUTED (0.08% PCK@20 — wrong keypoint normalization, predates published code); released code does not run as published (6 defects, incl. broken package import and an unreachable test phase); released dataset's last 13 files are corrupted (9,072 windows: NaN + float32-max garbage, diverges fp16 training via BatchNorm poisoning). After repairing both, retraining with upstream defaults reproduced 96.09% PCK@20 full-test / 96.61% corruption-free / MPJPE 0.0094–0.0098 (published: 97.25% / 0.007) on an RTX 5080. Accuracy claims graded MEASURED-EQUIVALENT; params (2.23M) and FLOPs (~0.055G) verified. (b)/(c) remain open.

2.3 Apply the UNSW recipe to the ADR-150 encoder — ACCEPTED (amends ADR-150 §2.3)

Pretraining corpus: start from the same 14 public datasets (1.3M samples) + our home/MM-Fi frames; data aggregation takes priority over architecture work.
Tokenization: 80% masking, (30,3)-class small patches; encoder stays ViT-Small-class (~15M params) — F3 and our own DANN/transformer results agree that capacity does not pay.
The published log-linear scaling (unsaturated) sets the expectation: more heterogeneous CSI in, better zero-shot out.

2.4 Hardware watch items — ACCEPTED (no code now)

802.11bf: track silicon/certification; OTA binding remains deferred until commodity chipsets expose standardized sensing measurements. Amended by ADR-153 (2026-06-10): implement a pure Rust forward-compatibility protocol layer now — typed procedure models, a deterministic session FSM, a transport abstraction, simulation tests, and an OpportunisticCsiBridge that maps today's ESP32 CSI batches into standardized sensing-report shape.
esp_wifi_sensing: benchmark our presence pipeline against the vendor FSM (one afternoon; useful external baseline). Do not treat as drop-in (refuted claim).
ZTECSITool AP: optional high-resolution anchor node for the ADR-029 multistatic mesh — procurement-gated; only pursue if a 160 MHz anchor materially helps tomography.

2.5 Explicitly NOT adopted

No pivot toward "wireless foundation model" papers that don't ship WiFi-CSI artifacts (HeterCSI, FMCW pilot, surveys).
No DensePose-UV work item: the field has not demonstrated UV regression from commodity WiFi; keypoints remain our supervised target (F5).

2.6 RuVector vendor sync + integration opportunities (added 2026-06-10)

Vendor sync record. vendor/ruvector moved from pin e38347601 (2026-05-07) to a083bd77f (origin/main, 3 commits past tag ruvector-v0.2.28; vendored workspace version 2.2.3). 111 commits in the range, roughly half NAPI-binary/lint chores. Substantive: graph condensation + differentiable min-cut (#547), core HNSW correctness fixes v2.2.3 (#502), RUSTSEC/clippy hardening (#504), ONNX embedder API-contract fix (#523/#525 — npm/TypeScript package only), dead parallel-worker import removal (#532). Evidence: MEASURED (git range + commit-stat inspection).

Opportunity table. Workspace policy is crates.io versions only, so unpublished crates are WATCH by definition regardless of fit.

Crate	What it offers	wifi-densepose target	crates.io	Verdict
`ruvector-graph-condense` (new, #547)	Training-free min-cut graph condensation + differentiable normalized-cut loss (`DiffCutCondenser`, analytic MinCutPool-style gradients, gradient-checked tests; provenance-retaining super-nodes)	`subcarrier_selection.rs` (condense 114 subcarriers into cut-preserving regions instead of raw min-cut); auxiliary clustering regularizer for `wifi-densepose-train`; `DynamicPersonMatcher` region structure	Not published	WATCH — strongest technical fit in the sync; adopt when published. README's "no published method uses graph-cut condensation" is CLAIMED; the diffcut implementation + tests are MEASURED
`ruvector-attention` 2.1.0	#304 SOTA modules: MLA, KV-cache, SSM, sparse/MoE, hybrid search, Graph RAG (publish date 2026-03-27 matches the #304 commit — MEASURED)	Supersedes pinned 2.0.4 used by `model.rs` spatial attention + `bvp.rs`; SSM/MLA are candidate pure-Rust edge-inference primitives for the ADR-150 encoder	2.1.0 (pinned 2.0.4)	ADOPT (minor bump; API-compat check first)
`ruvector-gnn` 2.2.0	panic→`Result` constructors, gradient clipping, MSE/CE/BCE losses, seeded-RNG layer init (#495 is post-2.2.0)	`wifi-densepose-train` GNN path (pinned 2.0.5, `default-features = false`)	2.2.0 (pinned 2.0.5)	ADOPT (bump)
`ruvector-mincut` / `ruvector-solver` 2.0.6	Patch-level fixes (workspace republish 2026-03-25)	`metrics.rs` DynamicPersonMatcher, subcarrier interpolation, triangulation	2.0.6 (pinned 2.0.4 each)	ADOPT (routine patch bump)
`ruvector-core` 2.2.3 (vendor)	HNSW correctness: k=0 guard, sorted results, flat-index fixes, cross-integration helpers (#502 — MEASURED, `index/hnsw.rs` + new integration tests)	`homecore-recorder` `RuvectorSemanticIndex` (real HNSW consumer); `sketch.rs` quantization unaffected	2.2.0 = latest published; 2.2.3 unpublished	WATCH — bump the moment 2.2.3 publishes
`ruvector-cnn` 2.0.6	Pure-Rust SIMD conv kernels (AVX2/NEON/WASM), MobileNetV3, INT8 quantization, contrastive losses (InfoNCE/triplet, #252)	Not the WiFlow-STD training port — `wiflow_std/model.rs` is tch/libtorch (MEASURED). Relevant to the edge inference path of the trained ~2.2 MB int8 model, and InfoNCE/triplet overlaps AETHER (ADR-024)	2.0.6	EVALUATE — only if/when we commit to a no-libtorch edge runtime for WiFlow-STD-class models
`ruvector-acorn` (new-ish)	ACORN predicate-agnostic filtered HNSW (SIGMOD'24 algorithm; γ·M denser graphs for low-selectivity filters)	Metadata-filtered pattern search over ADR-151 calibration banks — speculative; bank sizes are far below where filtered-ANN recall collapse matters	Not published	WATCH
`ruvector-cluster` 2.0.6	Distributed sharding, gossip discovery, DAG consensus	No current need; ADR-029 mesh coordination is ESP32-side, not vector-DB-side	2.0.6	WATCH
ONNX embedder fix (#523/#525)	API-contract + packaging fixes in `npm/packages/ruvector` (TypeScript)	None — `wifi-densepose-nn`'s ONNX backend is Rust (ort/tract), untouched by this change (MEASURED: commit touches npm/ only)	n/a	No action
`ruvector-perception` (new, #547)	"Physical perception substrate" (hypothesis/topology/witness modules) — agent-perception oriented, not RF	None identified	Not published	WATCH (name-overlap only)

Security note (RUSTSEC #504). The substantive fixes target ruvllm, ruvector-dag, prime-radiant, rvagent-*, and the ruvector-server HTTP endpoint (NaN-safe partial_cmp, input-validation guards, env-allowlisted exec) — none of which we pin. The commit states cargo audit returns clean across the workspace. Evidence: MEASURED (commit message + file list). Conclusion: no pinned version has an outstanding advisory; no urgent bump required. The NaN-sort hardening is panic-robustness hygiene our pinned 2.0.4-era crates predate, which is one more reason for the routine bumps below.

Version-bump recommendations (follow-up PR — no Cargo.toml change in this ADR): ruvector-mincut 2.0.4→2.0.6, ruvector-solver 2.0.4→2.0.6, ruvector-attention 2.0.4→2.1.0, ruvector-gnn 2.0.5→2.2.0. Current: ruvector-core 2.2.0, ruvector-attn-mincut 2.0.4, ruvector-temporal-tensor 2.0.6, ruvector-crv 0.1.1 — all at latest published. Nothing in the sync changes §2.1.2 geometry conditioning (our viewpoint/attention.rs GeometricBias already implements the fusion mechanism) or the ADR-150 MAE recipe (training stays in tch).

3. Consequences

Positive: the calibration system gains the one mechanism (geometry conditioning) the 2026 literature identifies as the difference between layout-brittle and layout-robust supervised WiFi pose; ADR-150 gets a measured training recipe instead of a guessed one; we acquire two external benchmarks (WiFlow-STD, PerceptAlign dataset) to keep our claims honest.

Negative / risks: geometry records add schema surface to banks (mitigated: optional + versioned); every adopted number is preprint-grade until our own benchmark runs land (mitigated by §2.2's no-citation rule); PerceptAlign dataset license is unconfirmed (gated); name collision risk in docs (mitigated: "WiFlow-STD (DY2434)" naming rule).

Re-check by 2026-12: 802.11bf silicon, esp_wifi_sensing maturity (v0.1.x today), and the preprint field (newest source Apr 2026).

4. Open questions (carried from the research run)

Does WiFlow-STD retain accuracy when fine-tuned on ESP32-S3/C6 CSI (fewer subcarriers, lower SNR), scored on our 17-keypoint set? (§2.2 answers this.)

Partial answer (MEASURED 2026-06-11, measurement (b) on 2,046 single-room windows — benchmarks/wiflow-std/RESULTS.md): pretrained init shows strong optimization transfer (65% PCK@20 vs scratch's 0% collapse under the same budget) but no feature transfer (frozen-trunk + linear adapter ≈ 0%). And no run beat the mean-pose baseline (95.9% PCK@20 — single subject, near-static normalized coords), so no CSI→pose capability is citable from this data. A definitive answer needs multi-subject/multi-position data where the mean pose is weak.
Is the PerceptAlign dataset downloadable under a usable license, and does the two-checkerboard procedure work with ESP32 transceiver geometry? (§2.1.4 gate.)
Will esp_wifi_sensing evolve toward 802.11bf compliance, replacing opportunistic CSI extraction?

5. Source register (evidence-graded)

Source	Type	Used for	Grade
arXiv 2601.12252 (PerceptAlign, MobiCom'26)	preprint+acceptance	F1, §2.1	CLAIMED numbers; failure mode corroborated
arXiv 2602.08661 + DY2434 repo (WiFlow-STD)	preprint + code	F2, §2.2	numbers CLAIMED; artifacts MEASURED
arXiv 2511.18792 (UNSW MAE)	preprint	F3, §2.3	ablations MEASURED in-study; pose transfer hypothesis
IEEE SA 802.11bf-2025 record	standards body	F4, §2.4	MEASURED
Espressif component registry + esp-csi repo	vendor	F4, §2.4	MEASURED; "drop-in" REFUTED 0-3
arXiv 2506.16957 + ZTE repo (ZTECSITool)	vendor preprint + code	F4, §2.4	capability CLAIMED; code MEASURED
arXiv 2601.18200 (HeterCSI), OpenReview LMufK3vzE5 (FMCW pilot), arXiv 2509.15258 (survey)	preprints	F5, §2.5 (screened out)	MEASURED (full-text inspection)