fix(cog-person-count): flag untrained-class counts low_confidence (ADR-159 A2)

The count head has 8 classes but count_train_results.json only has
support for classes 0/1 (presence, not multi-occupant counting). An
argmax on classes 2..=7 is out-of-distribution, yet the cog emitted it
as a confident headcount and the crate billed itself a "multi-person
counter".

- Add MAX_TRAINED_CLASS=1, CountPrediction::is_low_confidence() and
  clamped_count().
- person.count events now carry low_confidence + raw_count, downgrade to
  level "warn" when OOD, and clamp the reported count to the trained
  range (no fabricated headcount).
- run.started discloses count_max_trained_class / count_classes.
- Cargo.toml description: "multi-person counter" ->
  "presence detector + (data-gated) person count".

Multi-occupant accuracy stays DATA-GATED (not fabricated).

Failing-on-old test: untrained_class_argmax_is_flagged_low_confidence.

Co-Authored-By: claude-flow <ruv@ruv.net>

v2/crates/cog-person-count/Cargo.toml

@@ -5,7 +5,7 @@ edition.workspace = true
 authors.workspace = true
 license.workspace = true
 repository.workspace = true
-description = "Cognitum Cog: learned multi-person counter from WiFi CSI (ADR-103). Replaces the PR #491 slot heuristic with a Candle-based count head + Stoer-Wagner multi-node fusion."
+description = "Cognitum Cog: WiFi-CSI presence detector + (data-gated) person count (ADR-103). Candle-based head trained on classes 0/1 (presence); the 8-class count head ships but counts above the trained range are flagged low_confidence. Stoer-Wagner multi-node fusion."
 
 [[bin]]
 name = "cog-person-count"

v2/crates/cog-person-count/src/inference.rs

@@ -24,6 +24,17 @@ pub const INPUT_TIMESTEPS: usize = 20;
 /// Count classification over {0, 1, ..., 7} persons.
 pub const COUNT_CLASSES: usize = 8;
 
+/// Highest class the shipped `count_v1` weights were actually **trained** on.
+///
+/// The count head has 8 logits, but `count_train_results.json` only has support
+/// for classes 0 and 1 (`per_class_accuracy` keys are `"0"` and `"1"`). The model
+/// is a presence detector (0 vs ≥1 person), **not** a calibrated multi-occupant
+/// counter. An argmax landing on classes 2..=7 is out-of-distribution: the logits
+/// there were never supervised against labelled data. We flag such outputs
+/// `low_confidence` so downstream consumers don't trust a fabricated headcount.
+/// (Multi-occupant *accuracy* is DATA-GATED — not fabricated here.)
+pub const MAX_TRAINED_CLASS: usize = 1;
+
 #[derive(Debug, Clone)]
 pub struct CsiWindow {
     pub data: Vec<f32>,
@@ -45,6 +56,23 @@ impl CountPrediction {
         self.probs.iter().all(|v| v.is_finite()) && self.confidence.is_finite()
     }
 
+    /// True when the maximum-likelihood class is beyond what the shipped weights
+    /// were trained on ([`MAX_TRAINED_CLASS`]). Such a prediction is out-of-
+    /// distribution — the count head's logits for classes 2..=7 were never
+    /// supervised, so the headcount is not trustworthy. Surfaced as the
+    /// `low_confidence` field on the `person.count` event (honest-clip pattern).
+    pub fn is_low_confidence(&self) -> bool {
+        self.argmax() > MAX_TRAINED_CLASS
+    }
+
+    /// Argmax clamped to [`MAX_TRAINED_CLASS`]. When the raw argmax is an
+    /// untrained class we clamp the *reported* count to the highest trained
+    /// class rather than emit a fabricated multi-occupant headcount. The raw
+    /// distribution is still available in `probs` for diagnostics.
+    pub fn clamped_count(&self) -> usize {
+        self.argmax().min(MAX_TRAINED_CLASS)
+    }
+
     /// Maximum-likelihood class.
     pub fn argmax(&self) -> usize {
         let mut best_i = 0;

v2/crates/cog-person-count/src/publisher.rs

@@ -45,20 +45,35 @@ pub fn run_started(cog_id: &str, sensing_url: &str, poll_ms: u64, model_path: &s
             "sensing_url": sensing_url,
             "poll_ms": poll_ms,
             "model_path": model_path,
+            // Honest disclosure: the count head has 8 classes but the shipped
+            // weights were only trained on classes 0..=MAX_TRAINED_CLASS
+            // (presence, not multi-occupant counting). Counts above this are
+            // flagged `low_confidence` on each person.count event.
+            "count_max_trained_class": crate::inference::MAX_TRAINED_CLASS,
+            "count_classes": crate::inference::COUNT_CLASSES,
         }),
     });
 }
 
 pub fn person_count(tick: u64, fused: &CountPrediction, n_nodes: usize) {
     let (lo, hi) = fused.p95_range();
+    let low_confidence = fused.is_low_confidence();
     emit_event(&Event {
         ts: now_secs(),
-        level: "info",
+        // An out-of-distribution count (argmax beyond the trained classes) is
+        // a warning, not a clean info reading.
+        level: if low_confidence { "warn" } else { "info" },
         event: "person.count",
         fields: json!({
             "tick": tick,
-            "count": fused.argmax(),
+            // Reported count is clamped to the trained range — we never emit a
+            // fabricated multi-occupant headcount the weights can't back.
+            "count": fused.clamped_count(),
+            // Raw argmax kept for diagnostics/audit.
+            "raw_count": fused.argmax(),
             "confidence": fused.confidence,
+            // True when argmax > MAX_TRAINED_CLASS (untrained class).
+            "low_confidence": low_confidence,
             "count_p95_low": lo,
             "count_p95_high": hi,
             "n_nodes": n_nodes,

v2/crates/cog-person-count/tests/smoke.rs

@@ -4,7 +4,7 @@ use cog_person_count::{
     fusion::{fuse_confidence_weighted, fuse_with_mincut_clip},
     inference::{
         CountPrediction, CsiWindow, InferenceEngine, SyntheticInput, COUNT_CLASSES,
-        INPUT_SUBCARRIERS, INPUT_TIMESTEPS,
+        INPUT_SUBCARRIERS, INPUT_TIMESTEPS, MAX_TRAINED_CLASS,
     },
 };
 
@@ -83,6 +83,51 @@ fn fusion_passes_through_single_node() {
     assert!((out.confidence - 0.6).abs() < 1e-6);
 }
 
+/// ADR-159 §A2 — the 8-class count head ships, but the weights were only
+/// trained on classes 0/1 (presence). A prediction whose argmax lands on an
+/// UNTRAINED class (2..=7) must be flagged `low_confidence` and the reported
+/// count clamped to the trained range, so we never emit a fabricated
+/// multi-occupant headcount. Fails on old code (no such flag/clamp existed).
+#[test]
+fn untrained_class_argmax_is_flagged_low_confidence() {
+    // Sanity: the trained ceiling is below the head width.
+    assert!(MAX_TRAINED_CLASS < COUNT_CLASSES - 1);
+
+    // Mass on an untrained class (5 persons) — out-of-distribution.
+    let mut probs = [0.0_f32; COUNT_CLASSES];
+    probs[5] = 0.9;
+    probs[1] = 0.1;
+    let oodp = CountPrediction {
+        probs,
+        confidence: 0.95, // even a "confident" softmax must be flagged
+    };
+    assert_eq!(oodp.argmax(), 5);
+    assert!(
+        oodp.is_low_confidence(),
+        "argmax beyond MAX_TRAINED_CLASS must be flagged low_confidence"
+    );
+    assert_eq!(
+        oodp.clamped_count(),
+        MAX_TRAINED_CLASS,
+        "reported count must clamp to the trained ceiling, not fabricate a headcount"
+    );
+
+    // A trained-range prediction (1 person) is NOT flagged.
+    let mut probs2 = [0.0_f32; COUNT_CLASSES];
+    probs2[1] = 0.8;
+    probs2[0] = 0.2;
+    let inp = CountPrediction {
+        probs: probs2,
+        confidence: 0.8,
+    };
+    assert_eq!(inp.argmax(), 1);
+    assert!(
+        !inp.is_low_confidence(),
+        "a trained-range count must not be flagged"
+    );
+    assert_eq!(inp.clamped_count(), 1);
+}
+
 #[test]
 fn mincut_clip_with_high_cap_is_noop() {
     let mut probs = [0.0_f32; COUNT_CLASSES];