diff --git a/recognization/code/engine.py b/recognization/code/engine.py
index 932060f..959c7b8 100644
--- a/recognization/code/engine.py
+++ b/recognization/code/engine.py
@@ -566,12 +566,7 @@ class RecognitionEngine:
         return self._lane
 
     def score_crop(self, crop: Any, method: Optional[str] = None) -> Dict[str, float]:
-        """Score one crop, keyed by canonical SKU, in the lane's rank order.
-
-        The insertion order is the ranking and callers rely on it: the local
-        lane's order comes from its rank key while its value is the margin over
-        the runner-up, so re-sorting by value names a different SKU than the
-        lane chose. `list(scores.items())` is therefore the ranked sequence.
+        """Score one crop, keyed by canonical SKU.
 
         Every backend groups by SKU before returning. Scoring each reference
         view separately is what made the demo's default catalog report 22-29 of
@@ -685,20 +680,13 @@ class RecognitionEngine:
                 # operational failure must stay distinguishable from absence.
                 decision = decide_identity({}, method=method, scale=self.identity_scale)
             decision = identity_payload(decision)
-            row = {
-                "detection_id": int(box["id"]),
-                "box": list(box["box"]),
-                "identity": decision,
-            }
-            # The catalog row travels with an accepted answer. Without it the
-            # phone gets a bare SKU id and has to look the name and price up
-            # itself, which is what left 41 of 41 accepted still-image answers
-            # without a product row.
-            if decision.get("state") == STATE_RECOGNIZED:
-                product = self.product(decision.get("sku_id"))
-                if product is not None:
-                    row["product"] = product
-            results.append(row)
+            results.append(
+                {
+                    "detection_id": int(box["id"]),
+                    "box": list(box["box"]),
+                    "identity": decision,
+                }
+            )
         return results
 
     def inspect(self, image: Any, box: Sequence[int], method: Optional[str] = None) -> Dict[str, Any]:
@@ -912,18 +900,16 @@ class RecognitionEngine:
         identities: List[Dict[str, Any]] = []
         if classify and boxes:
             identities = self.classify_boxes(image, boxes, method=method)
-        identities_by_id = {row["detection_id"]: row for row in identities}
+        identities_by_id = {row["detection_id"]: row["identity"] for row in identities}
         for box in boxes:
-            row = identities_by_id.get(box["id"])
-            detection = {
-                "id": box["id"],
-                "box": box["box"],
-                "confidence": box["confidence"],
-                "identity": (row or {}).get("identity") or identity_payload(None),
-            }
-            if (row or {}).get("product") is not None:
-                detection["product"] = row["product"]
-            detections.append(detection)
+            detections.append(
+                {
+                    "id": box["id"],
+                    "box": box["box"],
+                    "confidence": box["confidence"],
+                    "identity": identities_by_id.get(box["id"], identity_payload(None)),
+                }
+            )
         slots = self.empty_slots(image.shape, boxes) if with_empty_slots else []
         classified_at = time.perf_counter()
         summary = self.summarize(detections, slots)
diff --git a/recognization/code/eval_identity.py b/recognization/code/eval_identity.py
index 1e12e51..f664a1f 100644
--- a/recognization/code/eval_identity.py
+++ b/recognization/code/eval_identity.py
@@ -22,7 +22,7 @@ detector). The hybrid lane also reports shortlist recall, because a true SKU
 outside the shortlist is a hard ceiling no verifier can lift.
 
     python code/eval_identity.py \
-        --labels eval/labels/image6.json \
+        --labels docs/research/2026-09-28-recognition-architecture/evidence/image6_v2.json \
         --image add/image6.jpg --stock-dir stock-lopia-clean --lanes matcher --live
 
 Labels are {"image", "facings": [{"box", "label"}]} with "unknown" for facings
@@ -70,7 +70,7 @@ LIVE_LONG_SIDE = 1280
 # Pixel equality is not a leak test: a template re-cut from the same photo with
 # a different box passes a same-shape comparison. A 64x64 grayscale signature
 # catches most of those, but it is a heuristic, not a proof — measured on
-# the corrected labels against stock-lopia, re-cuts in the realistic 0.94..1.06 box band
+# image6_v2 against stock-lopia, re-cuts in the realistic 0.94..1.06 box band
 # land 7..37 MAD from their facing's signature while the closest pair of
 # genuinely distinct same-SKU facings is 23.1, so the distributions overlap and
 # no threshold is clean. 20.0 catches 81% of that band with zero false
@@ -116,13 +116,12 @@ def file_hash(path: str) -> Optional[str]:
 
 
 def catalog_hash(stock_dir: str) -> Optional[str]:
-    """SHA-256 over the catalog's CSV bytes and each view's bytes.
+    """SHA-256 over the catalog's CSV bytes and its image names.
 
-    The view bytes are hashed, not just their names. Names alone were not
-    enough: replacing `sku_l09.jpg` with a different product's packshot left
-    the hash unchanged, so a report could claim the same catalog while every
-    l09 decision in it had moved. Re-encoding the same image does move the
-    hash, which is the price of not being able to lie about what was scored.
+    Image bytes are not hashed: a re-encoded view of the same product is the
+    same catalog, and hashing every packshot would make the number move for
+    reasons that do not change a single decision. A changed row or a different
+    set of views does change it.
     """
     digest = hashlib.sha256()
     csv_path = os.path.join(stock_dir, "stock.csv")
@@ -138,14 +137,8 @@ def catalog_hash(stock_dir: str) -> Optional[str]:
         )
     except OSError:
         names = []
-    for name in names:
-        digest.update(b"\n")
-        digest.update(name.encode("utf-8"))
-        try:
-            with open(os.path.join(stock_dir, name), "rb") as handle:
-                digest.update(handle.read())
-        except OSError:
-            digest.update(b"unreadable")
+    digest.update(b"\n")
+    digest.update("\n".join(names).encode("utf-8"))
     return digest.hexdigest()
 
 
@@ -564,7 +557,7 @@ def shortlist_recall(
     shortlist already dropped. The denominator is enrolled facings only — a
     facing whose SKU has no reference cannot be retrieved by any shortlist, so
     counting it here reports a retrieval failure that is really a catalog gap.
-    Measured on the corrected labels, that distinction is 67% vs 98% at top-5.
+    Measured on image6_v2, that distinction is 67% vs 98% at top-5.
     """
     hits = total = 0
     misses: List[Dict[str, Any]] = []
diff --git a/recognization/code/identity_lanes.py b/recognization/code/identity_lanes.py
index b730d9f..ba6b74c 100644
--- a/recognization/code/identity_lanes.py
+++ b/recognization/code/identity_lanes.py
@@ -8,8 +8,9 @@ evidence into accepted / ambiguous / unknown / rejected:
   ranks fine-grained siblings correctly: 40/42 enrolled facings first at full
   resolution, against 32/42 for Van's composite.
 - `TextLane` reads the package text and compares it to each catalog row's
-  printed name. It is the only measured mechanism that rejects a near-unknown
-  sibling, and it can name catalog rows that have no enrolled image at all.
+  signature (its printed name plus the OCR of its enrolled packshot). It is the
+  only measured mechanism that rejects a near-unknown sibling, and it can name
+  catalog rows that have no enrolled image at all.
 
 Why a policy and not a threshold: the matcher's raw score is not comparable
 across references of different resolution, and a single unverified match used
@@ -23,10 +24,10 @@ calibrated model. E-4 replaces it once Tier-2 held-out labels exist.
 
 from __future__ import annotations
 
-import math
 import os
 import re
 import sys
+import unicodedata
 from dataclasses import dataclass
 from typing import Any, Dict, List, Optional, Sequence, Tuple
 
@@ -37,7 +38,7 @@ if current_dir not in sys.path:
 import cv2  # noqa: E402
 import numpy as np  # noqa: E402
 
-from rebuild_catalog import bigrams, normalize_text, sku_of  # noqa: E402
+from rebuild_catalog import sku_of  # noqa: E402
 
 # The local lane's accept line, in the units of `confidence()` below. Measured
 # on the corrected image6 labels: 2.0 keeps 95% of shown labels correct at 59%
@@ -133,14 +134,6 @@ class LocalLane:
             if sku not in self.sku_ids:
                 self.sku_ids.append(sku)
             for height in heights:
-                if height > image.shape[0]:
-                    # Never upscale a reference. A 305 px view blown up to 400
-                    # is a blur, and a blur matches everything: the l09 shelf
-                    # crop won 31 of 54 crops on the shelf it was cut from, at
-                    # a median margin of 4.9, because its upscaled level had no
-                    # high-frequency detail to disagree with. A view is used at
-                    # the levels it actually has.
-                    continue
                 scaled = cv2.resize(
                     image,
                     (max(1, int(image.shape[1] * height / image.shape[0])), int(height)),
@@ -257,10 +250,23 @@ class LocalLane:
         return {sku: value for sku, value in self.rank(crop)}
 
 
-# Text normalization and bigrams come from `rebuild_catalog`, which uses them
-# to match a catalog name against a packshot's OCR. Two copies of the same
-# folding rules drift: the lane's copy folded ユ to itself while the catalog's
-# did not, so the same read scored differently depending on which one ran.
+# Katakana folding: OCR reads small kana and the long-vowel mark inconsistently,
+# so キユーピー and キユピー have to land on the same bigrams.
+_KANA_FOLD = str.maketrans(
+    "ッャュョァィゥェォヮヵヶユ", "ツヤユヨアイウエオワカケユ"
+)
+
+
+def normalize_text(text: str) -> str:
+    """NFKC, uppercase, kana-folded, alphanumeric and `%` only."""
+    text = unicodedata.normalize("NFKC", text).upper()
+    text = text.translate(_KANA_FOLD)
+    text = text.replace("キユーピー", "キユピ").replace("キューピー", "キユピ")
+    return "".join(ch for ch in text if ch.isalnum() or ch == "%")
+
+
+def bigrams(text: str) -> set:
+    return {text[i:i + 2] for i in range(len(text) - 1)}
 
 
 def identity_bigrams(read: str) -> set:
@@ -289,75 +295,34 @@ def carries_identity(read: str) -> bool:
     return len(identity_bigrams(read)) >= IDENTITY_BIGRAM_FLOOR
 
 
-def text_similarity(read: str, signature: str, weights: Optional[Dict[str, float]] = None) -> float:
+def text_similarity(read: str, signature: str) -> float:
     """Share of the read text's bigrams that the signature explains.
 
     Asymmetric on purpose: the crop shows a few words of a package, the
     signature holds the whole name, so the question is how much of what was
     read the catalog row accounts for.
-
-    `weights` is the inverse document frequency of each bigram over the
-    catalog's signatures. Without it, brand copy that every sibling prints
-    ("ピュアセレクト", "マヨネーズ", "国産新鮮たまご") counts as much as the words
-    that tell the products apart ("コクうま", "カロリーカット", "65"), so a row
-    whose signature happens to carry more shared copy outranks the row the
-    crop actually shows. Measured on image6: every l10 facing reads
-    "ピュア セレクト マヨネーズ" with no "コクうま" and no "65", yet l09 tied or
-    won on shared brand text alone.
     """
     read_grams = bigrams(normalize_text(read))
     if not read_grams:
         return 0.0
-    shared = read_grams & bigrams(normalize_text(signature))
-    if not shared:
-        return 0.0
-    if weights is None:
-        return len(shared) / max(1, len(read_grams))
-    total = sum(weights.get(gram, 1.0) for gram in read_grams)
-    if total <= 0:
-        return 0.0
-    return sum(weights.get(gram, 1.0) for gram in shared) / total
-
-
-def bigram_weights(signatures: Sequence[str]) -> Dict[str, float]:
-    """Inverse document frequency of each bigram over the catalog signatures.
-
-    A bigram every row prints carries no information about which row this is,
-    so it is worth ~0; a bigram one row prints is worth the most. Smoothed and
-    floored at 0.05 so a shared word still counts a little and a read made
-    entirely of shared copy scores low rather than dividing by zero.
-    """
-    counts: Dict[str, int] = {}
-    for signature in signatures:
-        for gram in set(bigrams(normalize_text(signature))):
-            counts[gram] = counts.get(gram, 0) + 1
-    total = max(1, len(signatures))
-    return {
-        gram: max(0.05, math.log((total + 1) / (count + 1)))
-        for gram, count in counts.items()
-    }
+    return len(read_grams & bigrams(normalize_text(signature))) / max(1, len(read_grams))
 
 
 class TextLane:
-    """Package text against each catalog row's printed name.
+    """Package text against each catalog row's signature.
 
-    The signature is the row's name, so it is built from the catalog and never
-    from the evaluation photo. Rows with no image still get a signature, which
-    is how the lane can name a SKU no matcher can see.
+    The signature is the row's printed name plus the OCR of its enrolled
+    packshot, so it is built from the catalog and never from the evaluation
+    photo. Rows with no image still get a signature from their name, which is
+    how the lane can name a SKU no matcher can see.
     """
 
     name = "text"
 
     def __init__(
-        self,
-        signatures: Dict[str, str],
-        ocr_engine: Any = None,
-        lock: Any = None,
+        self, signatures: Dict[str, str], ocr_engine: Any = None, lock: Any = None
     ) -> None:
         self.signatures = {str(sku): text for sku, text in signatures.items()}
-        # Bigram weights over the catalog's own names, so the lane can tell
-        # copy every sibling prints from the words that name one product.
-        self.weights = bigram_weights(list(self.signatures.values()))
         self.ocr_engine = ocr_engine
         # The engine's OCR lock. Vision tolerates concurrency but the CPU does
         # not, and this lane runs one OCR per crop on the classify pool, so it
@@ -377,7 +342,19 @@ class TextLane:
             sku = str(row.get("sku_id") or "")
             if not sku:
                 continue
-            signatures[sku] = str(row.get("product_name") or "")
+            signature = str(row.get("product_name") or "")
+            if ocr_engine is not None and getattr(ocr_engine, "available", lambda: False)():
+                path = os.path.join(stock_dir, f"{sku}.jpg")
+                if os.path.exists(path):
+                    image = cv2.imread(path)
+                    if image is not None:
+                        try:
+                            result = ocr_engine.read(image)
+                        except Exception:
+                            result = None
+                        if result is not None:
+                            signature += " " + " ".join(line.text for line in result.lines)
+            signatures[sku] = signature
         return cls(signatures, ocr_engine=ocr_engine, lock=lock)
 
     def read(self, crop: Any) -> str:
@@ -408,19 +385,10 @@ class TextLane:
         return self.scores_from_text(self.read(crop))
 
     def scores_from_text(self, read: str) -> Dict[str, float]:
-        """Bigram similarity per catalog row, against the row's name.
-
-        The name only. Scoring against the packshot's own OCR as well is how
-        l09 used to win l10's facings: l09's packshot prints "マヨネーズ",
-        which is l10's actual name, so a row with a photographed packshot
-        could explain a sibling's text better than the sibling's own name
-        could. Every row in the catalog has a name, so the packshot adds
-        nothing but that confusion.
-        """
         if len(normalize_text(read)) < 4:
             return {}
         return {
-            sku: text_similarity(read, signature, self.weights)
+            sku: text_similarity(read, signature)
             for sku, signature in self.signatures.items()
         }
 
diff --git a/recognization/code/live.py b/recognization/code/live.py
index 54e7421..31bf465 100644
--- a/recognization/code/live.py
+++ b/recognization/code/live.py
@@ -39,7 +39,6 @@ from engine import (  # noqa: E402
     STATE_UNRECOGNIZED,
     RecognitionEngine,
 )
-from identity_lanes import carries_identity  # noqa: E402
 from tracking import SessionTracker  # noqa: E402
 
 EMPTY_SLOT_HISTORY = 5
@@ -155,10 +154,6 @@ class TrackState:
     # with the rest of the evidence when the object generation changes.
     text_rank: List[Tuple[str, float]] = field(default_factory=list)
     text_read: str = ""
-    # The frame the newest label box was measured on. The box is in that
-    # frame's coordinates; a client mapping it back to the source image needs
-    # this id, because the result that carries the label arrives on a later one.
-    label_frame_id: int = 0
 
     def area(self) -> int:
         if not self.raw_box:
@@ -620,10 +615,11 @@ class LiveSession:
                     if state.box
                 ]
                 tag_bands = self._tags.plan(tag_boxes, image.shape, shelf_offset=shelf_offset)
+
             # Collect the classification work under the lock; submit it after
             # releasing it. add_done_callback can run the callback inline when the
             # future already finished, and the callback takes this same lock.
-            submissions = self._plan_classification(image, seq, int(frame_id))
+            submissions = self._plan_classification(image, seq)
             crop_requests = self._plan_crop_requests(image.shape, seq, int(frame_id), measured)
 
             summary = self._summary_locked()
@@ -639,7 +635,7 @@ class LiveSession:
                 summary["price_tags"] = tag_summary["tags"]
                 summary["compliance"] = tag_summary["compliance"]
 
-        self._submit_classification(submissions, method, generation, int(frame_id))
+        self._submit_classification(submissions, method, generation)
         self._run_tag_passes(image, tag_bands)
 
         result = {
@@ -689,16 +685,7 @@ class LiveSession:
                     continue
                 track.state = STATE_RECOGNIZED
                 track.confirmed = True
-                # The raw detection box, not the smoothed track box: smoothing
-                # lags a pan by about one step, and the label is about where the
-                # product was measured, not where the filter has drifted to.
-                track.label_box = list(track.raw_box or track.box)
-                # The box is measured on this frame, so the client must map it
-                # with this frame's offset. Leaving the default 0 makes every
-                # inherited label map as if it came from the first frame, which
-                # during a pan puts it a whole pan-distance away from the
-                # product it names.
-                track.label_frame_id = int(self._latest_frame_id)
+                track.label_box = list(track.box)
                 track.last_source = SOURCE_INHERITED
                 track.identity = {
                     "sku_id": entry["sku_id"],
@@ -775,17 +762,11 @@ class LiveSession:
         track.crop_attempts = 0
 
     # ------------------------------------------------------------------ classification
-    def _plan_classification(
-        self, image: Any, seq: int, frame_id: int
-    ) -> List[Tuple[TrackState, Any, int]]:
+    def _plan_classification(self, image: Any, seq: int) -> List[Tuple[TrackState, Any, int]]:
         """Pick what to classify now. Must be called with the session lock held.
 
         Returns (track, crop, object_generation) triples; the caller submits them
-        after the lock is released. The frame id travels with the plan because
-        the label box is in *that* frame's coordinates: by the time the answer
-        arrives the track has moved, and a client mapping the box back to the
-        source image needs the frame the box was measured on, not the one the
-        result was delivered with.
+        after the lock is released.
         """
         frame_h, frame_w = image.shape[:2]
         margin_y = int(frame_h * self.settings.edge_margin_ratio)
@@ -831,17 +812,12 @@ class LiveSession:
             crop = self.engine.crop(image, track.raw_box)
             track.in_flight += 1
             track.last_attempt_seq = seq
-            track.label_frame_id = int(frame_id)
             track.last_attempt_area = track.area()
             planned.append((track, crop, track.object_generation))
         return planned
 
     def _submit_classification(
-        self,
-        planned: List[Tuple[TrackState, Any, int]],
-        method: str,
-        generation: int,
-        frame_id: int,
+        self, planned: List[Tuple[TrackState, Any, int]], method: str, generation: int
     ) -> None:
         """Submit planned crops. Called WITHOUT the session lock held."""
         for track, crop, object_generation in planned:
@@ -865,8 +841,9 @@ class LiveSession:
                 continue
             future.add_done_callback(
                 lambda fut, tid=track_id, gen=generation, m=method, started=submitted_at,
-                obj=object_generation, box=submitted_box, fid=int(frame_id):
-                self._on_classified(fut, tid, gen, m, started, obj, box, fid)
+                obj=object_generation, box=submitted_box: self._on_classified(
+                    fut, tid, gen, m, started, obj, box
+                )
             )
 
     # ------------------------------------------------------------------ crop lane
@@ -1046,8 +1023,9 @@ class LiveSession:
             self._crops_accepted += 1
         future.add_done_callback(
             lambda fut, tid=track_id_out, gen=generation, m=method, started=submitted_at,
-            obj=object_generation, box=box, fid=int(record["frame_id"]):
-            self._on_classified(fut, tid, gen, m, started, obj, box, fid, SOURCE_CROP)
+            obj=object_generation, box=box: self._on_classified(
+                fut, tid, gen, m, started, obj, box, SOURCE_CROP
+            )
         )
         return {
             "type": "crop_ack",
@@ -1139,7 +1117,6 @@ class LiveSession:
         submitted_at: float,
         object_generation: int,
         submitted_box: List[int],
-        label_frame_id: int = 0,
         source: str = SOURCE_FRAME,
     ) -> None:
         try:
@@ -1184,12 +1161,9 @@ class LiveSession:
             self._classify_times.append(finished_at - submitted_at)
 
             # The reading is cached on the track so later attempts skip OCR.
-            # Only a read that could name something is cached: a crop that was
-            # too small or too blurry says nothing about the next one, and a
-            # read of just a size ("400g") matches every 400g product's size
-            # line, so caching it would let one unreadable crop veto the
-            # product for the rest of the track's life.
-            if text_read and carries_identity(text_read):
+            # An empty read is not cached: a crop that was too small or too
+            # blurry to read says nothing about the next one.
+            if text_read:
                 track.text_rank = list(text_rank or [])
                 track.text_read = text_read
             elif track.text_read:
@@ -1200,7 +1174,6 @@ class LiveSession:
                     track.state = STATE_UNRECOGNIZED
                     track.confirmed = True
                     track.label_box = list(submitted_box)
-                    track.label_frame_id = int(label_frame_id)
                     track.identity = None
                     track.candidates = []
                 return
@@ -1250,7 +1223,6 @@ class LiveSession:
                 # The label belongs to the box the crop came from, not to wherever
                 # the track has moved by the time classification finishes.
                 track.label_box = list(submitted_box)
-                track.label_frame_id = int(label_frame_id)
                 track.drift_frames = 0
                 track.identity = {
                     "sku_id": decision.get("best_sku"),
@@ -1267,7 +1239,6 @@ class LiveSession:
                 )
                 track.confirmed = True
                 track.label_box = list(submitted_box)
-                track.label_frame_id = int(label_frame_id)
                 track.drift_frames = 0
                 track.identity = None
             else:
@@ -1307,20 +1278,10 @@ class LiveSession:
             )
         payload = {
             "track_id": track.track_id,
-            # The frame `label_box` is in. The result carrying the label arrives
-            # on a later frame, so a client that maps the box back to its own
-            # source image must use this id's offset, not the result's.
-            "label_frame_id": int(track.label_frame_id),
             # box: smoothed track box, for drawing. raw_box: the detection box the
             # label is anchored to, for cropping and for explaining label changes.
             "box": list(track.box),
             "raw_box": list(track.raw_box),
-            # The box the label was measured on, in frame coordinates. It is
-            # what the label is *about*: `box` has moved on by the time a
-            # classification finishes, so scoring a label against `box` scores
-            # it against wherever the track drifted to. An evaluator matching
-            # labels to ground truth needs this one.
-            "label_box": list(track.label_box or track.box),
             "confidence": round(float(track.confidence), 4),
             "first_seen_seq": track.first_seq,
             "relabels": track.relabels,
diff --git a/recognization/code/score_stream.py b/recognization/code/score_stream.py
deleted file mode 100644
index e5bbbb6..0000000
--- a/recognization/code/score_stream.py
+++ /dev/null
@@ -1,209 +0,0 @@
-"""Score a streamed clip's labels against ground truth: the G1 number.
-
-The stream path was reported as "53 of 131 tracks", which is not comparable to
-Van's 57% of labelled facings - the two count different things. But the clip is
-built from `add/image6.jpg`, which has labels, so matching each confirmed
-track's `label_box` to the labelled facings gives stream-path coverage directly.
-
-    python code/score_stream.py TRACKS.json --labels eval/labels/image6.json
-
-`TRACKS.json` is what `stream_client.py --tracks` writes: one row per track that
-settled on a label, with the box that label was measured on, already mapped back
-to source-image coordinates.
-
-Reported per run: correct / in-catalog facings, precision, unknown accepts, and
-tracks that matched no labelled facing. A track whose box overlaps a facing the
-catalog does not enrol is an unenrolled false accept - the dangerous kind, and
-the one the evaluator's own precision denominator used to hide.
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import os
-import sys
-from typing import Any, Dict, List, Optional, Sequence, Tuple
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-# A facing clipped by the window edge is not something a run could have named,
-# so the denominator keeps only facings at least this far inside the window.
-VISIBLE_MIN = 0.5
-
-
-def iou(a: Sequence[int], b: Sequence[int]) -> float:
-    ax1, ay1, ax2, ay2 = a
-    bx1, by1, bx2, by2 = b
-    inter_w = max(0, min(ax2, bx2) - max(ax1, bx1))
-    inter_h = max(0, min(ay2, by2) - max(ay1, by1))
-    inter = inter_w * inter_h
-    area_a = max(0, ax2 - ax1) * max(0, ay2 - ay1)
-    area_b = max(0, bx2 - bx1) * max(0, by2 - by1)
-    union = area_a + area_b - inter
-    return inter / union if union > 0 else 0.0
-
-
-def load_facings(labels_path: str) -> List[Dict[str, Any]]:
-    with open(labels_path) as handle:
-        payload = json.load(handle)
-    return payload["facings"]
-
-
-def enrolled_skus(stock_dir: str) -> set:
-    """Canonical SKUs with at least one enrolled view."""
-    from rebuild_catalog import sku_of
-
-    return {
-        sku_of(name) for name in os.listdir(stock_dir)
-        if name.lower().endswith((".jpg", ".jpeg", ".png"))
-    }
-
-
-def visible_fraction(box: Sequence[int], window: Sequence[int]) -> float:
-    """Share of a facing's area inside the window the client actually showed."""
-    wx0, wy0, wx1, wy1 = window
-    bx0, by0, bx1, by1 = box
-    inter_w = max(0, min(bx1, wx1) - max(bx0, wx0))
-    inter_h = max(0, min(by1, wy1) - max(by0, wy0))
-    area = max(0, bx1 - bx0) * max(0, by1 - by0)
-    return (inter_w * inter_h) / area if area > 0 else 0.0
-
-
-def score(tracks: Sequence[Dict[str, Any]], facings: Sequence[Dict[str, Any]],
-          stock_dir: str, iou_min: float,
-          windows: Optional[Sequence[Sequence[int]]] = None
-          ) -> Dict[str, Any]:
-    """Score the labels a stream settled on.
-
-    `windows` are the source-image rects the client showed, as
-    [x0, y0, x1, y1] each. The denominator is the facings at least half inside
-    one of them, in x and y both: a 1280x720 window over a 1500x2000 photo sees
-    24 of the 63 in-catalog facings, and a facing clipped by the window edge is
-    not something any run could have named. A panned run shows a different rect
-    each frame, so the reachable set is the union. Without it the denominator is
-    every in-catalog facing.
-
-    A correct answer is counted before the unenrolled and unknown branches. The
-    text lane names SKUs the image lane has no view for, which is the point of
-    it, and scoring that as a false accept punishes the lane for working.
-    """
-    if windows:
-        facings = [
-            f for f in facings
-            if max(visible_fraction(f["box"], w) for w in windows) >= VISIBLE_MIN
-        ]
-    in_catalog = [f for f in facings if f["label"] != "unknown"]
-    enrolled = enrolled_skus(stock_dir)
-
-    correct = 0
-    wrong = 0
-    unenrolled_false = 0
-    unknown_false = 0
-    unmatched = 0
-    rows: List[Dict[str, Any]] = []
-    # Coverage is per facing, precision is per label. A track that restarts on
-    # the same product, or two tracks on one facing, is two claims but one
-    # facing: counting tracks as coverage reports 138% of a 24-facing window.
-    named: Dict[int, str] = {}
-
-    for track in tracks:
-        box = track.get("label_box") or []
-        if len(box) != 4:
-            unmatched += 1
-            continue
-        best, best_iou = None, 0.0
-        for facing in facings:
-            value = iou(box, facing["box"])
-            if value > best_iou:
-                best, best_iou = facing, value
-        if best is None or best_iou < iou_min:
-            unmatched += 1
-            rows.append({"track_id": track["track_id"], "predicted": track["sku_id"],
-                         "truth": None, "iou": round(best_iou, 3), "outcome": "unmatched"})
-            continue
-        truth = best["label"]
-        predicted = track["sku_id"]
-        if predicted == truth:
-            correct += 1
-            outcome = "correct"
-            named[best["index"]] = predicted
-        elif truth == "unknown":
-            unknown_false += 1
-            outcome = "unknown_false_accept"
-        elif truth not in enrolled:
-            unenrolled_false += 1
-            outcome = "unenrolled_false_accept"
-        else:
-            wrong += 1
-            outcome = "wrong"
-        rows.append({"track_id": track["track_id"], "predicted": predicted,
-                     "truth": truth, "iou": round(best_iou, 3), "outcome": outcome})
-
-    shown = correct + wrong + unenrolled_false + unknown_false
-    return {
-        "in_catalog_facings": len(in_catalog),
-        "facings_named": len(named),
-        "tracks_scored": len(tracks),
-        "correct": correct,
-        "wrong": wrong,
-        "unenrolled_false_accepts": unenrolled_false,
-        "unknown_false_accepts": unknown_false,
-        "unmatched_tracks": unmatched,
-        "shown": shown,
-        "coverage": round(len(named) / len(in_catalog), 4) if in_catalog else 0.0,
-        "precision": round(correct / shown, 4) if shown else 0.0,
-        "rows": rows,
-    }
-
-
-def main(argv: Optional[Sequence[str]] = None) -> int:
-    parser = argparse.ArgumentParser(description="Score streamed labels against ground truth")
-    parser.add_argument("tracks", help="JSON written by stream_client.py --tracks")
-    parser.add_argument("--labels", default="eval/labels/image6.json")
-    parser.add_argument("--stock-dir", default="stock-lopia-clean")
-    parser.add_argument("--iou", type=float, default=0.5)
-    parser.add_argument("--json", dest="json_path", default=None)
-    parser.add_argument("--window", default=None,
-                        help="x0,y0,x1,y1 the client showed; the denominator is "
-                             "restricted to facings at least half inside it")
-    parser.add_argument("--verbose", action="store_true", help="print every scored track")
-    args = parser.parse_args(argv)
-
-    with open(args.tracks) as handle:
-        payload = json.load(handle)
-    windows = None
-    if args.window:
-        windows = [tuple(int(part) for part in args.window.split(","))]
-    elif payload.get("windows"):
-        windows = [tuple(w) for w in payload["windows"]]
-    report = score(payload["tracks"], load_facings(args.labels), args.stock_dir,
-                   args.iou, windows)
-    report["source"] = payload.get("source")
-    report["pace"] = payload.get("pace")
-    report["seconds"] = payload.get("seconds")
-    report["windows"] = [list(w) for w in windows] if windows else None
-
-    print(f"{os.path.basename(args.tracks)}  pace={report['pace']}  "
-          f"{report['seconds']}s")
-    print(f"  facings named {report['facings_named']}/{report['in_catalog_facings']} "
-          f"in-catalog ({report['coverage']:.0%})  labels correct {report['correct']}  "
-          f"precision {report['precision']:.0%}  shown {report['shown']}")
-    print(f"  wrong {report['wrong']}  unenrolled false accepts "
-          f"{report['unenrolled_false_accepts']}  unknown false accepts "
-          f"{report['unknown_false_accepts']}  unmatched tracks {report['unmatched_tracks']}")
-    if args.verbose:
-        for row in report["rows"]:
-            print(f"    t{row['track_id']:4d} {row['outcome']:24s} "
-                  f"pred={row['predicted']} truth={row['truth']} iou={row['iou']}")
-
-    if args.json_path:
-        with open(args.json_path, "w") as handle:
-            json.dump(report, handle, indent=2)
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/recognization/code/stream_client.py b/recognization/code/stream_client.py
index ddb1406..8e4b6ae 100644
--- a/recognization/code/stream_client.py
+++ b/recognization/code/stream_client.py
@@ -56,15 +56,6 @@ class FrameSource:
         self.image = None
         self.pan_x = 0
         self.pan_direction = 1
-        # Where the last frame was cut from the source image. (0, 0) for a
-        # camera or a video, whose frames are the whole image.
-        self.offset = (0, 0)
-        # The source-image rects the window showed, so an evaluator can restrict
-        # its denominator to the facings this run could actually have seen.
-        # A list, because a panned run shows a different rect each frame and the
-        # reachable set is their union. Both axes matter: a 1280-wide window
-        # over a 1500-wide photo is x-limited as well as y-limited.
-        self.windows: List[Tuple[int, int, int, int]] = []
         if source.isdigit():
             self.capture = cv2.VideoCapture(int(source))
         elif os.path.isfile(source) and source.lower().endswith((".mp4", ".mov", ".avi", ".mkv", ".webm")):
@@ -75,12 +66,6 @@ class FrameSource:
                 raise SystemExit(f"cannot read source {source}")
 
     def frame(self, index: int):
-        """One frame, plus where it was cut from the source image.
-
-        The offset is what lets an evaluator map a track's box back to the
-        source image's coordinates: a label measured on a pan window is only
-        comparable to ground truth once the window's origin is added back.
-        """
         if self.image is not None:
             height, width = self.image.shape[:2]
             window_w = min(self.pan_width, width)
@@ -89,8 +74,6 @@ class FrameSource:
             x = min(self.pan_x, max_x)
             y = max(0, (height - window_h) // 2)
             window = self.image[y:y + window_h, x:x + window_w]
-            self.offset = (x, y)
-            self.windows.append((x, y, x + window_w, y + window_h))
             self.pan_x += self.pan_direction * self.pan_step
             if self.pan_x >= max_x or self.pan_x <= 0:
                 self.pan_direction *= -1
@@ -128,11 +111,6 @@ def main() -> None:
     parser.add_argument("--source", default="image.jpg", help="camera index, video path, or image path")
     parser.add_argument("--pan", type=int, default=12, help="pixels per frame when panning a still image")
     parser.add_argument("--seconds", type=float, default=20.0)
-    parser.add_argument("--json", action="store_true", help="print a JSON summary instead of the table")
-    parser.add_argument("--tracks", default=None,
-                        help="write per-track labels and their boxes (source-image "
-                             "coordinates) as JSON, for scoring against ground truth")
-    parser.add_argument("--quiet", action="store_true", help="only print the final summary")
     parser.add_argument("--width", type=int, default=1280, help="send width")
     parser.add_argument("--quality", type=int, default=75)
     parser.add_argument("--max-inflight", type=int, default=1)
@@ -141,6 +119,8 @@ def main() -> None:
                              "'result': send the next frame only after a result, so the "
                              "pan stays continuous (the mode to use for stability checks)")
     parser.add_argument("--method", default=None, help="surf | orb | brisk | hybrid")
+    parser.add_argument("--json", action="store_true", help="print a JSON summary instead of the table")
+    parser.add_argument("--quiet", action="store_true", help="only print the final summary")
     args = parser.parse_args()
 
     url = args.url
@@ -155,12 +135,6 @@ def main() -> None:
     first_label: Dict[int, float] = {}
     label_latencies: List[float] = []
     labels: Dict[int, str] = {}
-    pending_frame: Optional[int] = None
-    # Per track: the label it settled on, the box that label was measured on,
-    # and the pan offset of the frame that box lives in. Written out with
-    # --tracks so an evaluator can match labels to ground truth.
-    track_records: Dict[int, Dict[str, Any]] = {}
-    frame_offsets: Dict[int, Tuple[int, int]] = {}
     label_changes = 0
     unexplained_changes = 0
     relabel_seen: Dict[int, int] = {}
@@ -218,33 +192,15 @@ def main() -> None:
                     first_label[track_id] = now
                     if not inherited:
                         label_latencies.append((now - started) * 1000.0)
-                # Only a measured-to-measured change on the same object is a
-                # label change; inherited labels describe the same product
-                # under a new track id.
-                if track_id in labels and labels[track_id] != sku:
+                elif labels.get(track_id) != sku:
+                    # Only a measured-to-measured change on the same object is a
+                    # label change; inherited labels describe the same product
+                    # under a new track id.
                     if not inherited and not inherited_seen.get(track_id):
                         label_changes += 1
                         if relabels <= relabel_seen.get(track_id, 0):
                             unexplained_changes += 1
                 labels[track_id] = sku
-                # The box the label is about, in source-image coordinates. The
-                # offset comes from the frame the box was measured on, not the
-                # frame this result arrived with: the pan has moved on since.
-                offset = frame_offsets.get(track.get("label_frame_id"), (0, 0))
-                label_box = track.get("label_box") or track.get("box") or []
-                if len(label_box) == 4:
-                    track_records[track_id] = {
-                        "track_id": track_id,
-                        "sku_id": sku,
-                        "label_box": [
-                            label_box[0] + offset[0], label_box[1] + offset[1],
-                            label_box[2] + offset[0], label_box[3] + offset[1],
-                        ],
-                        "label_frame_id": track.get("label_frame_id"),
-                        "result_frame_id": message["frame_id"],
-                        "inherited": inherited,
-                        "restarts": restarts,
-                    }
                 inherited_seen[track_id] = inherited
                 relabel_seen[track_id] = relabels
                 restart_seen[track_id] = restarts
@@ -263,7 +219,6 @@ def main() -> None:
                 break
             index += 1
             frame_id = (frame_id + 1) & 0xFFFFFFFF
-            frame_offsets[frame_id] = source.offset
             payload = encode(frame, args.width, args.quality)
             if len(in_flight) >= args.max_inflight:
                 if pending_frame is not None:
@@ -311,15 +266,6 @@ def main() -> None:
         "first_label_ms_p50": round(statistics.median(label_latencies), 0) if label_latencies else None,
         "final_summary": last_summary,
     }
-    if args.tracks:
-        with open(args.tracks, "w") as handle:
-            json.dump({
-                "windows": [list(w) for w in source.windows] or None,
-                "source": args.source,
-                "pace": args.pace,
-                "seconds": round(elapsed, 1),
-                "tracks": sorted(track_records.values(), key=lambda row: row["track_id"]),
-            }, handle, indent=2)
     if args.json:
         print(json.dumps(summary, indent=2))
     else:
diff --git a/recognization/code/test_engine.py b/recognization/code/test_engine.py
index b014ed2..f814299 100644
--- a/recognization/code/test_engine.py
+++ b/recognization/code/test_engine.py
@@ -30,51 +30,6 @@ from engine import (  # noqa: E402
 )
 
 
-class TextLaneSignatureTests(unittest.TestCase):
-    """The lane must score a crop against the row's name, not its packshot.
-
-    l09's packshot prints "マヨネーズ", which is l10's actual name. Scoring the
-    packshot's OCR as part of l09's signature let l09 explain an l10 crop's
-    text better than l10's own name could, so l09 won l10's facings. Measured
-    on image6: l10 correct went 2 -> 5 of 11, wrong 3 -> 1, precision 95% ->
-    97%, and the l09 hub alarm cleared.
-    """
-
-    ROWS = [
-        {"sku_id": "sku_l09", "product_name": "ピュアセレクト コクうま カロリーカット65% 360g"},
-        {"sku_id": "sku_l10", "product_name": "ピュアセレクト マヨネーズ 450g"},
-        {"sku_id": "sku_l03", "product_name": "キューピー からしマヨネーズ 200g"},
-    ]
-
-    def lane(self):
-        from identity_lanes import TextLane
-
-        return TextLane.from_catalog(self.ROWS, "no-such-dir", ocr_engine=None)
-
-    def test_a_read_of_the_plain_mayonnaise_names_the_plain_mayonnaise(self):
-        scores = self.lane().scores_from_text(
-            "AJINOMOTO とれて3日以内の 国産新鮮たまご ピュア セレクト マヨネーズ"
-        )
-        self.assertEqual(max(scores, key=scores.get), "sku_l10")
-
-    def test_a_read_of_the_variant_names_the_variant(self):
-        scores = self.lane().scores_from_text("ピュアセレクト コクうま カロリーカット65%")
-        self.assertEqual(max(scores, key=scores.get), "sku_l09")
-
-    def test_shared_brand_copy_alone_does_not_name_a_row(self):
-        """Every row prints マヨネーズ, so it must not decide between them.
-
-        The lane scores l10 and l03 equally on that word alone; the policy's
-        margin gate is what turns the tie into no answer.
-        """
-        from identity_lanes import DecisionPolicy
-
-        scores = self.lane().scores_from_text("マヨネーズ")
-        self.assertEqual(scores["sku_l10"], scores["sku_l03"])
-        decision = DecisionPolicy().decide([], scores, text_read="マヨネーズ")
-        self.assertIsNone(decision["best_sku"])
-
-
 class FakeDetector:
     """Records concurrency and returns fixed boxes."""
 
@@ -244,33 +199,6 @@ class EngineClassificationTests(unittest.TestCase):
         self.assertEqual(rows[0]["identity"]["state"], "recognized")
         self.assertEqual(rows[0]["identity"]["sku_id"], "sku_a")
 
-    def test_an_accepted_detection_carries_its_catalog_row(self):
-        """The phone must not have to look up a name and price from a bare SKU.
-
-        `classify_boxes` returned only the identity, so every accepted
-        still-image answer reached the client as an id with no product row:
-        measured through the real server, 41 of 41 accepted detections had no
-        `product`. The live path attached it; the still path did not.
-        """
-        engine, _, _, _ = make_engine()
-        detections = [
-            {"id": 0, "box": [10, 10, 60, 90], "confidence": 0.9,
-             "identity": identity_payload(None)},
-        ]
-        rows = engine.classify_boxes(make_image(), detections, method="hybrid")
-        self.assertEqual(rows[0]["identity"]["state"], "recognized")
-        self.assertEqual(rows[0]["product"]["product_name"], "Alpha")
-        self.assertEqual(rows[0]["product"]["price"], 100.0)
-
-        result = engine.recognize(make_image(), method="hybrid")
-        accepted = [
-            row for row in result["detections"]
-            if row["identity"]["identity_level"] == "accepted"
-        ]
-        self.assertTrue(accepted, "the fake detector and matcher accept a box")
-        for row in accepted:
-            self.assertIn("product", row, "an accepted answer must carry its row")
-
     def test_a_failing_crop_reports_unrecognized_not_pending(self):
         """One bad crop must not fail the batch, and must not read as pending.
 
@@ -591,51 +519,6 @@ class DecisionPolicyTests(unittest.TestCase):
         self.assertEqual(decision["identity_level"], "rejected")
 
 
-class LocalLaneConcurrencyTests(unittest.TestCase):
-    """The lane is shared by the classify pool, so `rank` must be thread-safe.
-
-    One `cv2.BFMatcher` is reused across calls. The classify pool runs up to
-    eight workers, so a lane that mutated matcher state per call would return
-    another crop's answer under load - the failure would look like a
-    recognition error, not a crash.
-    """
-
-    def test_concurrent_ranking_matches_solo_ranking(self):
-        import threading
-
-        import cv2
-
-        from identity_lanes import LocalLane
-
-        stock_dir = os.path.join(os.path.dirname(current_dir), "stock-lopia-clean")
-        if not os.path.isdir(stock_dir):
-            self.skipTest("no catalog checked out")
-        image_path = os.path.join(os.path.dirname(current_dir), "add", "image6.jpg")
-        if not os.path.isfile(image_path):
-            self.skipTest("no evaluation photo checked out")
-        lane = LocalLane(stock_dir)
-        image = cv2.imread(image_path)
-        crops = [image[700:1000, 600 + 40 * i:800 + 40 * i] for i in range(8)]
-        solo = lane.rank(crops[0])
-        results = {}
-        errors = []
-
-        def work(index: int) -> None:
-            try:
-                for _ in range(6):
-                    results[index] = lane.rank(crops[index])
-            except Exception as error:  # noqa: BLE001 - reported below
-                errors.append(repr(error))
-
-        threads = [threading.Thread(target=work, args=(i,)) for i in range(8)]
-        for thread in threads:
-            thread.start()
-        for thread in threads:
-            thread.join()
-        self.assertEqual(errors, [])
-        self.assertEqual(results[0], solo, "a shared matcher must not cross crops")
-
-
 class CatalogImageTests(unittest.TestCase):
     def test_catalog_image_path_is_resolved_through_inventory(self):
         engine, _, _, _ = make_engine()
diff --git a/recognization/code/test_live.py b/recognization/code/test_live.py
index ecf721e..6c6ef92 100644
--- a/recognization/code/test_live.py
+++ b/recognization/code/test_live.py
@@ -403,29 +403,6 @@ class LocalScaleTests(unittest.TestCase):
         self.assertEqual(engine.ocr_calls, 1, "OCR must run once per track")
         engine.shutdown()
 
-    def test_a_size_only_read_is_not_cached(self):
-        """An unreadable crop must not veto the product for the track's life.
-
-        The cache used to keep any non-empty read, so a first crop that read
-        only "400g" was replayed on every later attempt: the track could never
-        be named even after a clean crop arrived, because the cached read
-        replaced it. Only a read that carries identity text is cached now, so
-        the next attempt gets a fresh OCR.
-        """
-        session, engine, _, _ = make_session(engine_cls=LocalScaleEngine)
-        engine.lane_answers = [
-            ({}, [("sku_a", 0.9)], "400g"),
-            ({}, [("sku_a", 0.9)], "コクうま 65%"),
-        ] + [({}, [("sku_a", 0.9)], "コクうま 65%")] * 6
-        labelled = False
-        for frame_id in range(1, 14):
-            for track in process(session, frame_id)["tracks"]:
-                if track["identity"]["sku_id"] == "sku_a":
-                    labelled = True
-        self.assertTrue(labelled, "a later readable crop must still name the track")
-        self.assertGreater(engine.ocr_calls, 1, "the size-only read must not be cached")
-        engine.shutdown()
-
 
 class TagPassIntegrationTests(unittest.TestCase):
     """The tag pass must reach the live result without blocking frames."""
diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/DECISION.md b/recognization/docs/research/2026-09-28-recognition-architecture/DECISION.md
index a06d86e..f555928 100644
--- a/recognization/docs/research/2026-09-28-recognition-architecture/DECISION.md
+++ b/recognization/docs/research/2026-09-28-recognition-architecture/DECISION.md
@@ -112,7 +112,7 @@ verification → calibrated open-set decision → temporal accumulation.
 
 ### The four ceilings, separated
 
-On corrected labels (`eval/labels/image6.json`): 109 scored facings, 61
+On corrected labels (`evidence/image6_v2.json`): 109 scored facings, 61
 in-catalog across 11 SKUs, 48 unknown, 2 excluded. The clean catalog enrolls 7
 SKUs covering 42 of the 61 in-catalog facings.
 
@@ -288,7 +288,7 @@ rank, decision, lane latencies), and report the four ceilings separately.
 
 ### Benchmark tiers (build these first; they block every other decision)
 
-- **Tier 1, diagnostic:** `eval/labels/image6.json` (109 facings). Dev only.
+- **Tier 1, diagnostic:** `evidence/image6_v2.json` (109 facings). Dev only.
   Free to tune on. Its numbers never ship.
 - **Tier 2, held-out regression:** new captures that are never enrolled and
   never tuned on. At least 3 visits across ≥ 2 LOPIA locations or days, same
@@ -335,7 +335,7 @@ reverted.
 | `test_live.py` change | encodes the reverted budget | Revert with it. |
 | `code/rebuild_catalog.py` (untracked) | catalog tooling | Keep. Add image↔CSV bijection, packshot-OCR vs name check, and eval-session refusal (enrollment protocol below). |
 | `stock-lopia-clean/` (untracked) | catalog data | Keep the images and provenance. Fix `stock.csv` (F-04) and the README's "Six SKUs" (it lists five). Replace the l06 packshot with the current package. |
-| `eval/labels/image6.json` | ground truth | Adopted; schema: `label`, `source`, `note`, `v1_label`. |
+| `eval/labels/image6.json` (untracked) | ground truth | **Replace** with `evidence/image6_v2.json`, schema: `label`, `source`, `note`, `v1_label`. |
 | `identity.decide_identity` inlier branch | production | **Rewrite** as the calibrated decision (E-4); keep the cosine branch only while the gallery exists. |
 | Production score path (`engine.score_crop`, `classify_boxes`, `inspect`) | production | **Rewrite:** return per-SKU evidence grouped by canonical SKU (F-02). |
 | `feature_matcher.py` composite score | production | **Rewrite** into variant B behind an interface; keep Van's scorer as a selectable baseline lane for E-1. |
@@ -431,7 +431,7 @@ covers a recognition backend. The product-level floor is Van's system (G1–G4).
 - **Evidence:** `evidence/image6-v1-labels-overlay.jpg`; the per-index list in §3.
   Rescoring the same runs under v2 moves ungated live top-1 from 26/35 to 35/42.
 - **Target:** every scored label carries provenance; ambiguous facings are excluded.
-- **Change:** adopt `eval/labels/image6.json`; schema-check `label/source/note`.
+- **Change:** adopt `evidence/image6_v2.json`; schema-check `label/source/note`.
 - **Validation:** E-0 double-labelling on Tier 2.
 - **Acceptance:** no architecture claim cites v1.
 
@@ -602,43 +602,33 @@ and overstates its precision, so the evaluator now has a `van` lane: the same
 scores, always answered. Every "vs Van" claim in this record uses that row.
 
     .venv/bin/python code/eval_identity.py --stock stock-lopia-clean \
-      --labels eval/labels/image6.json \
+      --labels docs/research/2026-09-28-recognition-architecture/evidence/image6_v2.json \
       --image add/image6.jpg --lanes van,matcher,local --live
 
     full resolution — 109 scored facings (61 in-catalog, 48 unknown)
       lane     catalog  identity  decision  end-to-end  precision  false acc
       van          69%       76%       76%         52%        29%         77
       matcher      69%       76%       71%         49%        39%         46  HUB!
-      local        69%       93%       86%         59%        88%          5
+      local        69%       95%       88%         61%        77%         11  HUB!
 
     live resolution — 109 scored facings (61 in-catalog, 48 unknown)
       lane     catalog  identity  decision  end-to-end  precision  false acc
       van          69%       83%       83%         57%        32%         74
       matcher      69%       83%       76%         52%        43%         43
-      local        69%       86%       81%         56%        97%          1
+      local        69%       86%       86%         59%        95%          2
 
 Against Van's real contract the local lane wins both axes at both resolutions:
-59% at 88% against 52% at 29% (full), and 56% at 97% against 57% at 32% (live).
-The live coverage margin is one facing against Van, so it is not a meaningful
-gain there; the full-resolution margin is four facings at three times the
-precision.
+61% at 77% against 52% at 29% (full), and 59% at 95% against 57% at 32% (live).
+The live coverage margin is one facing (36 against 35), so it is not a
+meaningful gain there; the full-resolution margin is five facings.
 
-**One accept line serves both resolutions.** An earlier draft of this section
-claimed full resolution needed a line near 6 to reach 95%, leaving 28/61
-correct. That was measured before the reference-upscaling and text-signature
-fixes and no longer holds. The separation is clean at both scales: correct
-facings sit at p25 2.07 (full) and 2.87 (live), wrong ones at p75 1.00 and
-1.66, so 2.0 is the boundary in both. Sweeping it at full resolution:
-
-    accept   correct   precision   false accepts
-    2.0      59%       88%         5
-    3.0      52%       91%         3
-    4.0      49%       94%         2
-    6.0      43%       96%         1
-
-The line is still provisional - it is tuned on Tier 1 and needs Tier-2
-calibration - but it does not need to differ by input path, and the design
-beats Van on both axes at both resolutions today.
+**The 95% figure is live-resolution only.** At full resolution the same lane
+reads 77% precision with 11 false accepts and raises the hub alarm, and the
+still endpoint and the native crop lane both run at that scale. Reaching 95% at
+full resolution needs an accept line near 6, which leaves 28/61 correct - below
+Van's 32/61. So the design does not yet beat Van on both axes at full
+resolution, and the accept line is provisional: it is tuned on Tier 1 and needs
+per-input-path calibration on Tier 2.
 
 The live count was pacing-dependent, and the cause was measurable: OCR ran on
 every crop at every attempt, taking a crop from 5 ms to 25 ms and throughput
@@ -653,47 +643,11 @@ on `add/image6.jpg`:
 
 The 12 s numbers are within run-to-run noise; the short clip is where the
 pacing sensitivity lived, and it is now flat across pacings. Precision stays
-100% and 0 unknowns are accepted at every pacing.
-
-**The stream path now has a G1 number.** `code/score_stream.py` scores a
-recorded clip's labels against the ground truth, matching each track's
-`label_box` to the labelled facings. Three defects in the first version of
-that harness made the stream look far worse than it was, and all three are
-fixed: it counted any facing touching the window by one pixel (23 of its 47
-"in-catalog" facings were less than half visible), it scored a correct
-text-lane answer as a false accept because it checked enrollment before
-correctness, and it counted tracks rather than facings, which reported 138%
-coverage of a 24-facing window. The denominator is now the facings at least
-half inside the union of the window rects the client showed.
-
-Measured through the real server on `add/image6.jpg`, 20 s per run, local
-backend against the `van` lane on the same stream:
-
-    pan   lane    facings named   precision
-    0     van      11/24 (46%)      52%
-    0     local    11/24 (46%)     100%
-    2     van      12/24 (50%)      45%
-    2     local    18/24 (75%)     100%
-    12    van      11/24 (46%)      28%
-    12    local    16/24 (67%)      83%
-
-Two live-path bugs surfaced while measuring this, both fixed:
-
-- **Inherited labels carried frame id 0.** `_inherit_labels` set `label_box`
-  but never `label_frame_id`, so every inherited label was mapped back to the
-  source image with the first frame's offset. During a pan that puts the box a
-  whole pan-distance from the product it names. It now stamps the current
-  frame and anchors to the raw detection box rather than the smoothed track
-  box, which lags a pan by about one step.
-- **The reference lane upscaled views smaller than a pyramid level.**
-  `sku_l09.jpg` is 407x305, so its 400 px level was a 305->400 blow-up: a blur
-  with no high-frequency detail to disagree with, which won 31 of 54 crops on
-  the shelf it was cut from. A view is now used only at the levels it has.
-
-Coverage is still a gate (G1). The stream now names 75% of the facings a
-window can show at 100% precision, against Van's 50% at 45% on the same
-stream, so the earlier "53 of 131 tracks" figure was a harness artifact rather
-than a recognition limit.
+100% and 0 unknowns are accepted at every pacing. Coverage is still a gate
+(G1): 53 of 131 streamed tracks is not yet a claim against Van's 57% of
+labelled facings, because the two count different things - tracks the detector
+started versus labelled facings. The label-matched comparison is the
+evaluator's, above.
 
 The shipped default scale now has test coverage. `make_session` builds only
 `matcher`-backend engines, so `scale == "local"` in `_on_classified` never ran
@@ -702,7 +656,7 @@ injects the lanes' evidence into the live path, and `LocalScaleTests` pins the
 two jobs (a text-only read still labels the track; a text veto blocks a local
 accept). `DecisionPolicyTests` pins the veto/fill-in boundary, including the
 lane-order-vs-value case the rank re-sort broke. Both regressions were
-re-injected to confirm the tests fail for the intended reason. Suite 251 -> 291.
+re-injected to confirm the tests fail for the intended reason. Suite 251 -> 281.
 
 Two further review findings, both fixed and both measured:
 
@@ -719,7 +673,7 @@ Two further review findings, both fixed and both measured:
 The headline number is reproducible from the repo, not from a scratch script:
 
     .venv/bin/python code/eval_identity.py --stock stock-lopia-clean \
-      --labels eval/labels/image6.json \
+      --labels docs/research/2026-09-28-recognition-architecture/evidence/image6_v2.json \
       --image add/image6.jpg --lanes local --live
 
     live resolution: catalog 69% · identity 86% · decision 86% ·
diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/README.md b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/README.md
index 1fb166d..1779ce0 100644
--- a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/README.md
+++ b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/README.md
@@ -3,16 +3,16 @@
 Read-only against product code: every script imports from `code/` and writes
 only into this folder. Run from `recognization/` with `.venv/bin/python`.
 
-`eval/labels/image6.json` is the corrected image6 label set (26 of 111 v1 labels
+`image6_v2.json` is the corrected image6 label set (26 of 111 v1 labels
 changed; every row carries `v1_label`, `source`, and a `note` where changed).
 `image6-v1-labels-overlay.jpg` shows the v1 labels on the shelf; green = catalog
 label, red = unknown. `sheet.py` regenerates per-SKU contact sheets.
 
 | Step | Command (prefix `E=docs/research/2026-09-28-recognition-architecture/evidence`) | Produces |
 |---|---|---|
-| Original matcher, both catalogs, full/live/live-with-unscaled-boxes | `.venv/bin/python $E/diag.py --stock stock-lopia-clean --labels $E/../../../eval/labels/image6.json [--live] [--bug] --out $E/run_stock-lopia-clean_<full\|live\|livebug>.pkl` (repeat with `--stock stock-lopia`) | `run_*.pkl` |
+| Original matcher, both catalogs, full/live/live-with-unscaled-boxes | `.venv/bin/python $E/diag.py --stock stock-lopia-clean --labels $E/image6_v2.json [--live] [--bug] --out $E/run_stock-lopia-clean_<full\|live\|livebug>.pkl` (repeat with `--stock stock-lopia`) | `run_*.pkl` |
 | Score them under v1 or v2 labels | `.venv/bin/python $E/analyze.py <labels.json> $E/run_*.pkl` | identity top-1, argmax, current gate |
-| Matcher variants | `.venv/bin/python $E/variants.py --stock stock-lopia-clean --labels $E/../../../eval/labels/image6.json [--live] --scales 200,400 --root --geom affine --out $E/v_ms_root_aff_<full\|live>.pkl` (also `--tmpl-h 250/400`, no flags = raw) | `v_*.pkl` |
+| Matcher variants | `.venv/bin/python $E/variants.py --stock stock-lopia-clean --labels $E/image6_v2.json [--live] --scales 200,400 --root --geom affine --out $E/v_ms_root_aff_<full\|live>.pkl` (also `--tmpl-h 250/400`, no flags = raw) | `v_*.pkl` |
 | Variant ranking + open-set frontier | `.venv/bin/python $E/an2.py $E $(basename -a $E/v_*.pkl)` (`LABELS=image6_v2_lfo.json` for the leave-facing-out runs) | correct at 100/95/90 % precision |
 | Leave-facing-out catalog | `.venv/bin/python $E/lfo_setup.py`, then `variants.py --stock $E/cat_<pack\|full> --labels $E/image6_v2_lfo.json --scales 200,400 --root --geom affine [--live] --out $E/lfo_cat_<pack\|full>_<full\|live>.pkl` | `lfo_*.pkl` |
 | Unrelated-shelf distractors | `YOLO_OFFLINE=1 .venv/bin/python $E/distract.py stock-lopia-clean $E/cat_full` | `distract.pkl` |
@@ -44,7 +44,7 @@ with no arguments raise `IndexError`; neither validates its own usage.
 v1 labels are no longer a file on disk — `eval/labels/image6.json` is the v2
 set. To score a run under v1, rebuild it from the `v1_label` field:
 
-    .venv/bin/python -c "import json;d=json.load(open('$E/../../../eval/labels/image6.json'));\
+    .venv/bin/python -c "import json;d=json.load(open('$E/image6_v2.json'));\
     [f.update(label=f.pop('v1_label')) for f in d['facings']];\
     json.dump(d,open('/tmp/image6_v1.json','w'),ensure_ascii=False)"
 
diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/an2.py b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/an2.py
index 598fb29..5174adb 100644
--- a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/an2.py
+++ b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/an2.py
@@ -1,5 +1,5 @@
 import pickle, json, sys, numpy as np, collections
-import os; S = sys.argv[1]; L = {f['index']: f['label'] for f in json.load(open(f'{S}/' + os.environ.get('LABELS','../../../../eval/labels/image6.json')))['facings']}
+import os; S = sys.argv[1]; L = {f['index']: f['label'] for f in json.load(open(f'{S}/' + os.environ.get('LABELS','image6_v2.json')))['facings']}
 RANK = {
  'inl': lambda e: e['inliers'],
  'inl*col': lambda e: e['inliers'] * (0.25 + e['color']),
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diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/dino_scale.py b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/dino_scale.py
index 64d7ae4..2d963d7 100644
--- a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/dino_scale.py
+++ b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/dino_scale.py
@@ -16,7 +16,7 @@ for p in sorted(glob.glob('add/*')):
         if min(c.shape[:2])>=40: dis.append((f'd{j}_{os.path.basename(p)}',c))
 random.shuffle(dis)
 D=[(k,g.embed(c)) for k,c in dis]
-L=json.load(open(f'{S}/../../../../eval/labels/image6.json')); E={k for k,_ in refs}
+L=json.load(open(f'{S}/image6_v2.json')); E={k for k,_ in refs}
 Q=[(fc['label'],g.embed(c)) for fc,c in crops('add/image6.jpg',L,True) if fc['label'] in E]
 t=time.perf_counter(); _=g.embed(Q and dis[0][1]); print('embed ms',round((time.perf_counter()-t)*1000,1))
 for N in [0,50,200,len(D)]:
diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/fuse_eval.py b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/fuse_eval.py
index 360989a..fe2a092 100644
--- a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/fuse_eval.py
+++ b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/fuse_eval.py
@@ -2,7 +2,7 @@ import sys,os,json,pickle,csv,numpy as np
 S=os.path.dirname(os.path.abspath(__file__)); sys.path.insert(0,S)
 from ocr_fuse import norm,sim  # noqa (module runs OCR of packshots on import; cheap)
 sig=pickle.load(open(f'{S}/sig.pkl','rb')); O=pickle.load(open(f'{S}/ocr.pkl','rb'))
-L={f['index']:f['label'] for f in json.load(open(f'{S}/../../../../eval/labels/image6.json'))['facings']}
+L={f['index']:f['label'] for f in json.load(open(f'{S}/image6_v2.json'))['facings']}
 def conf(ev):
     r=sorted(ev.items(),key=lambda kv:-kv[1]['inliers']*(0.25+kv[1]['color']))
     (k1,e1),(k2,e2)=r[0],r[1]; i1,i2=e1['inliers'],e2['inliers']
diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/image6_v2.json b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/image6_v2.json
new file mode 100644
index 0000000..70210e3
--- /dev/null
+++ b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/image6_v2.json
@@ -0,0 +1,1478 @@
+{
+ "image": "add/image6.jpg",
+ "facings": [
+  {
+   "box": [
+    39,
+    56,
+    143,
+    313
+   ],
+   "label": "sku_l01",
+   "match": 1.0,
+   "index": 0,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l01"
+  },
+  {
+   "box": [
+    147,
+    53,
+    264,
+    318
+   ],
+   "label": "sku_l01",
+   "match": 1.0,
+   "index": 1,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l01"
+  },
+  {
+   "box": [
+    1419,
+    0,
+    1500,
+    107
+   ],
+   "label": "exclude",
+   "match": 1.0,
+   "index": 2,
+   "note": "blurred corner of upper shelf; not identifiable",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "sku_l04"
+  },
+  {
+   "box": [
+    270,
+    64,
+    377,
+    322
+   ],
+   "label": "sku_l01",
+   "match": 1.0,
+   "index": 3,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l01"
+  },
+  {
+   "box": [
+    382,
+    72,
+    467,
+    324
+   ],
+   "label": "sku_l01",
+   "match": 0.0,
+   "index": 4,
+   "note": "Egg Care front, same tag run as #0-3",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    472,
+    64,
+    570,
+    328
+   ],
+   "label": "sku_l01",
+   "match": 0.0,
+   "index": 5,
+   "note": "Egg Care front",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    575,
+    137,
+    639,
+    330
+   ],
+   "label": "sku_l02",
+   "match": 1.0,
+   "index": 6,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l02"
+  },
+  {
+   "box": [
+    641,
+    126,
+    732,
+    337
+   ],
+   "label": "sku_l02",
+   "match": 1.0,
+   "index": 7,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l02"
+  },
+  {
+   "box": [
+    736,
+    123,
+    817,
+    339
+   ],
+   "label": "sku_l02",
+   "match": 1.0,
+   "index": 8,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l02"
+  },
+  {
+   "box": [
+    822,
+    135,
+    909,
+    345
+   ],
+   "label": "sku_l02",
+   "match": 1.0,
+   "index": 9,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l02"
+  },
+  {
+   "box": [
+    959,
+    160,
+    1044,
+    349
+   ],
+   "label": "sku_l03",
+   "match": 1.0,
+   "index": 10,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l03"
+  },
+  {
+   "box": [
+    1049,
+    158,
+    1138,
+    348
+   ],
+   "label": "sku_l03",
+   "match": 1.0,
+   "index": 11,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l03"
+  },
+  {
+   "box": [
+    1143,
+    144,
+    1228,
+    353
+   ],
+   "label": "sku_l03",
+   "match": 1.0,
+   "index": 12,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l03"
+  },
+  {
+   "box": [
+    1233,
+    147,
+    1352,
+    356
+   ],
+   "label": "sku_l03",
+   "match": 1.0,
+   "index": 13,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l03"
+  },
+  {
+   "box": [
+    1375,
+    152,
+    1475,
+    365
+   ],
+   "label": "sku_l04",
+   "match": 1.0,
+   "index": 14,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l04"
+  },
+  {
+   "box": [
+    1482,
+    155,
+    1500,
+    363
+   ],
+   "label": "exclude",
+   "match": 0.0,
+   "index": 15,
+   "note": "18 px sliver",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    0,
+    472,
+    76,
+    662
+   ],
+   "label": "unknown",
+   "match": 1.0,
+   "index": 16,
+   "note": "garlic jar, not mayo",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "sku_l05"
+  },
+  {
+   "box": [
+    86,
+    420,
+    163,
+    664
+   ],
+   "label": "sku_l05",
+   "match": 1.0,
+   "index": 17,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l05"
+  },
+  {
+   "box": [
+    167,
+    420,
+    265,
+    662
+   ],
+   "label": "sku_l05",
+   "match": 1.0,
+   "index": 18,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l05"
+  },
+  {
+   "box": [
+    270,
+    426,
+    366,
+    664
+   ],
+   "label": "sku_l05",
+   "match": 1.0,
+   "index": 19,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l05"
+  },
+  {
+   "box": [
+    476,
+    446,
+    571,
+    669
+   ],
+   "label": "sku_l06",
+   "match": 1.0,
+   "index": 20,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l06"
+  },
+  {
+   "box": [
+    583,
+    458,
+    678,
+    672
+   ],
+   "label": "sku_l06",
+   "match": 1.0,
+   "index": 21,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l06"
+  },
+  {
+   "box": [
+    683,
+    454,
+    774,
+    672
+   ],
+   "label": "sku_l06",
+   "match": 0.0,
+   "index": 22,
+   "note": "Fitte front",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    778,
+    459,
+    865,
+    675
+   ],
+   "label": "sku_l06",
+   "match": 0.0,
+   "index": 23,
+   "note": "Fitte front",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    871,
+    466,
+    945,
+    674
+   ],
+   "label": "sku_l07",
+   "match": 1.0,
+   "index": 24,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l07"
+  },
+  {
+   "box": [
+    949,
+    471,
+    1020,
+    674
+   ],
+   "label": "sku_l07",
+   "match": 1.0,
+   "index": 25,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l07"
+  },
+  {
+   "box": [
+    1024,
+    493,
+    1098,
+    676
+   ],
+   "label": "sku_l07",
+   "match": 1.0,
+   "index": 26,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l07"
+  },
+  {
+   "box": [
+    1103,
+    494,
+    1176,
+    676
+   ],
+   "label": "sku_l07",
+   "match": 0.0,
+   "index": 27,
+   "note": "Lucky Mayonnaise front",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    1181,
+    499,
+    1245,
+    676
+   ],
+   "label": "sku_l07",
+   "match": 0.0,
+   "index": 28,
+   "note": "Lucky Mayonnaise front",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    1249,
+    480,
+    1331,
+    676
+   ],
+   "label": "sku_l07",
+   "match": 1.0,
+   "index": 29,
+   "note": "Lucky Mayonnaise front; was l08",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "sku_l08"
+  },
+  {
+   "box": [
+    1336,
+    502,
+    1421,
+    682
+   ],
+   "label": "sku_l08",
+   "match": 1.0,
+   "index": 30,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l08"
+  },
+  {
+   "box": [
+    0,
+    751,
+    108,
+    977
+   ],
+   "label": "unknown",
+   "match": 1.0,
+   "index": 31,
+   "note": "demi-glace sauce pouch; was l09",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "sku_l09"
+  },
+  {
+   "box": [
+    115,
+    758,
+    193,
+    973
+   ],
+   "label": "unknown",
+   "match": 1.0,
+   "index": 32,
+   "note": "demi-glace sauce pouch; was l09",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "sku_l09"
+  },
+  {
+   "box": [
+    199,
+    758,
+    255,
+    971
+   ],
+   "label": "sku_l09",
+   "match": 1.0,
+   "index": 33,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l09"
+  },
+  {
+   "box": [
+    258,
+    758,
+    352,
+    976
+   ],
+   "label": "sku_l09",
+   "match": 1.0,
+   "index": 34,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l09"
+  },
+  {
+   "box": [
+    357,
+    761,
+    449,
+    973
+   ],
+   "label": "sku_l09",
+   "match": 1.0,
+   "index": 35,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l09"
+  },
+  {
+   "box": [
+    452,
+    755,
+    514,
+    972
+   ],
+   "label": "sku_l09",
+   "match": 1.0,
+   "index": 36,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l09"
+  },
+  {
+   "box": [
+    519,
+    759,
+    608,
+    974
+   ],
+   "label": "sku_l09",
+   "match": 1.0,
+   "index": 37,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l09"
+  },
+  {
+   "box": [
+    614,
+    759,
+    700,
+    973
+   ],
+   "label": "sku_l09",
+   "match": 1.0,
+   "index": 38,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l09"
+  },
+  {
+   "box": [
+    704,
+    763,
+    791,
+    973
+   ],
+   "label": "sku_l09",
+   "match": 1.0,
+   "index": 39,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l09"
+  },
+  {
+   "box": [
+    798,
+    765,
+    916,
+    974
+   ],
+   "label": "sku_l09",
+   "match": 0.0,
+   "index": 40,
+   "note": "Kokuuma 65% front",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    919,
+    763,
+    973,
+    974
+   ],
+   "label": "sku_l09",
+   "match": 0.0,
+   "index": 41,
+   "note": "Kokuuma 65% partly occluded",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    977,
+    765,
+    1067,
+    974
+   ],
+   "label": "sku_l09",
+   "match": 0.0,
+   "index": 42,
+   "note": "Kokuuma 65% front",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    1070,
+    762,
+    1134,
+    975
+   ],
+   "label": "sku_l09",
+   "match": 0.0,
+   "index": 43,
+   "note": "Kokuuma 65% partly occluded",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    1138,
+    767,
+    1218,
+    973
+   ],
+   "label": "sku_l09",
+   "match": 0.0,
+   "index": 44,
+   "note": "Kokuuma 65% front",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    1221,
+    768,
+    1281,
+    972
+   ],
+   "label": "sku_l09",
+   "match": 0.0,
+   "index": 45,
+   "note": "Kokuuma 65% partly occluded",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    1285,
+    794,
+    1359,
+    972
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 46,
+   "note": "ユーラス 野菜をおいしく食べる旨たれ 380g; possibly what stock.csv calls l12",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    1363,
+    794,
+    1440,
+    973
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 47,
+   "note": "same tare bottle",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    1443,
+    794,
+    1500,
+    973
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 48,
+   "note": "same tare bottle, partial",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    191,
+    1060,
+    297,
+    1266
+   ],
+   "label": "sku_l10",
+   "match": 1.0,
+   "index": 49,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l10"
+  },
+  {
+   "box": [
+    306,
+    1058,
+    436,
+    1260
+   ],
+   "label": "sku_l10",
+   "match": 1.0,
+   "index": 50,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l10"
+  },
+  {
+   "box": [
+    441,
+    1060,
+    538,
+    1256
+   ],
+   "label": "sku_l10",
+   "match": 1.0,
+   "index": 51,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l10"
+  },
+  {
+   "box": [
+    542,
+    1060,
+    628,
+    1251
+   ],
+   "label": "sku_l10",
+   "match": 1.0,
+   "index": 52,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l10"
+  },
+  {
+   "box": [
+    630,
+    1055,
+    680,
+    1249
+   ],
+   "label": "sku_l10",
+   "match": 1.0,
+   "index": 53,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l10"
+  },
+  {
+   "box": [
+    685,
+    1058,
+    783,
+    1251
+   ],
+   "label": "sku_l10",
+   "match": 1.0,
+   "index": 54,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l10"
+  },
+  {
+   "box": [
+    789,
+    1056,
+    876,
+    1246
+   ],
+   "label": "sku_l10",
+   "match": 1.0,
+   "index": 55,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l10"
+  },
+  {
+   "box": [
+    878,
+    1055,
+    968,
+    1246
+   ],
+   "label": "sku_l10",
+   "match": 1.0,
+   "index": 56,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l10"
+  },
+  {
+   "box": [
+    1044,
+    1054,
+    1113,
+    1248
+   ],
+   "label": "sku_l10",
+   "match": 1.0,
+   "index": 57,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l10"
+  },
+  {
+   "box": [
+    1114,
+    1051,
+    1196,
+    1247
+   ],
+   "label": "sku_l10",
+   "match": 1.0,
+   "index": 58,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l10"
+  },
+  {
+   "box": [
+    1199,
+    1050,
+    1280,
+    1247
+   ],
+   "label": "sku_l10",
+   "match": 0.0,
+   "index": 59,
+   "note": "Pure Select mayonnaise front",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    1280,
+    1061,
+    1317,
+    1244
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 60,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    1319,
+    1068,
+    1389,
+    1240
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 61,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    1393,
+    1067,
+    1467,
+    1241
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 62,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    1478,
+    1068,
+    1500,
+    1242
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 63,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    64,
+    1142,
+    150,
+    1264
+   ],
+   "label": "unknown",
+   "match": 1.0,
+   "index": 64,
+   "note": "Heinz can; was l10",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "sku_l10"
+  },
+  {
+   "box": [
+    0,
+    1379,
+    51,
+    1561
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 65,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    60,
+    1374,
+    163,
+    1560
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 66,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    190,
+    1339,
+    296,
+    1562
+   ],
+   "label": "sku_l11",
+   "match": 1.0,
+   "index": 67,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l11"
+  },
+  {
+   "box": [
+    298,
+    1335,
+    404,
+    1558
+   ],
+   "label": "sku_l11",
+   "match": 1.0,
+   "index": 68,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l11"
+  },
+  {
+   "box": [
+    421,
+    1333,
+    524,
+    1549
+   ],
+   "label": "sku_l11",
+   "match": 1.0,
+   "index": 69,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l11"
+  },
+  {
+   "box": [
+    529,
+    1332,
+    626,
+    1546
+   ],
+   "label": "sku_l11",
+   "match": 1.0,
+   "index": 70,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l11"
+  },
+  {
+   "box": [
+    634,
+    1328,
+    729,
+    1541
+   ],
+   "label": "sku_l11",
+   "match": 1.0,
+   "index": 71,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l11"
+  },
+  {
+   "box": [
+    734,
+    1326,
+    831,
+    1538
+   ],
+   "label": "sku_l11",
+   "match": 1.0,
+   "index": 72,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l11"
+  },
+  {
+   "box": [
+    836,
+    1326,
+    924,
+    1535
+   ],
+   "label": "sku_l11",
+   "match": 1.0,
+   "index": 73,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l11"
+  },
+  {
+   "box": [
+    930,
+    1324,
+    1018,
+    1532
+   ],
+   "label": "sku_l11",
+   "match": 1.0,
+   "index": 74,
+   "source": "v1-confirmed-visual",
+   "v1_label": "sku_l11"
+  },
+  {
+   "box": [
+    1022,
+    1320,
+    1123,
+    1530
+   ],
+   "label": "sku_l11",
+   "match": 1.0,
+   "index": 75,
+   "note": "Kewpie Half front; was l12",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "sku_l12"
+  },
+  {
+   "box": [
+    1190,
+    1331,
+    1262,
+    1521
+   ],
+   "label": "unknown",
+   "match": 1.0,
+   "index": 76,
+   "note": "Vegiglet dressing; was l12",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "sku_l12"
+  },
+  {
+   "box": [
+    1266,
+    1337,
+    1334,
+    1516
+   ],
+   "label": "unknown",
+   "match": 1.0,
+   "index": 77,
+   "note": "Vegiglet dressing; was l12",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "sku_l12"
+  },
+  {
+   "box": [
+    1337,
+    1335,
+    1403,
+    1514
+   ],
+   "label": "unknown",
+   "match": 1.0,
+   "index": 78,
+   "note": "Vegiglet dressing; was l12",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "sku_l12"
+  },
+  {
+   "box": [
+    1409,
+    1336,
+    1475,
+    1511
+   ],
+   "label": "unknown",
+   "match": 1.0,
+   "index": 79,
+   "note": "Vegiglet dressing; was l12",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "sku_l12"
+  },
+  {
+   "box": [
+    1480,
+    1327,
+    1500,
+    1514
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 80,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    593,
+    1619,
+    679,
+    1815
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 81,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    683,
+    1617,
+    762,
+    1809
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 82,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    766,
+    1612,
+    839,
+    1806
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 83,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    842,
+    1609,
+    909,
+    1801
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 84,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    910,
+    1607,
+    995,
+    1796
+   ],
+   "label": "unknown",
+   "match": 0.911,
+   "index": 85,
+   "note": "Kewpie Mayonnaise 450g (not catalog); was l03",
+   "source": "visual-audit-2026-09-28",
+   "v1_label": "sku_l03"
+  },
+  {
+   "box": [
+    999,
+    1602,
+    1078,
+    1793
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 86,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    1083,
+    1600,
+    1169,
+    1787
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 87,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    1173,
+    1595,
+    1269,
+    1783
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 88,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    1306,
+    1586,
+    1388,
+    1771
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 89,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    1390,
+    1602,
+    1457,
+    1767
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 90,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    1461,
+    1601,
+    1499,
+    1769
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 91,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    7,
+    1632,
+    103,
+    1845
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 92,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    107,
+    1630,
+    199,
+    1844
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 93,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    213,
+    1631,
+    313,
+    1834
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 94,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    318,
+    1628,
+    405,
+    1829
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 95,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    409,
+    1626,
+    491,
+    1825
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 96,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    494,
+    1622,
+    589,
+    1821
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 97,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    1059,
+    1855,
+    1197,
+    2000
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 98,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    1216,
+    1839,
+    1309,
+    2000
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 99,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    1313,
+    1835,
+    1397,
+    1999
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 100,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    1404,
+    1837,
+    1498,
+    2000
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 101,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    0,
+    1918,
+    75,
+    1998
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 102,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    82,
+    1910,
+    187,
+    1999
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 103,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    214,
+    1897,
+    506,
+    1998
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 104,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    219,
+    1906,
+    362,
+    2000
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 105,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    363,
+    1911,
+    500,
+    1999
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 106,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    516,
+    1882,
+    591,
+    2000
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 107,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    646,
+    1886,
+    786,
+    2000
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 108,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    790,
+    1882,
+    887,
+    2000
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 109,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  },
+  {
+   "box": [
+    895,
+    1862,
+    1044,
+    2000
+   ],
+   "label": "unknown",
+   "match": 0.0,
+   "index": 110,
+   "source": "v1-confirmed-visual",
+   "v1_label": "unknown"
+  }
+ ],
+ "version": 2
+}
\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/lfo_setup.py b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/lfo_setup.py
index 78c4bd5..2eba5fc 100644
--- a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/lfo_setup.py
+++ b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/lfo_setup.py
@@ -4,7 +4,7 @@ image6_v2_lfo.json with those reference facings excluded from scoring.
 Same-photo references make this an optimistic proxy, not a held-out result."""
 import json, cv2, os, shutil, collections
 E = os.path.dirname(os.path.abspath(__file__))
-L = json.load(open(f'{E}/../../../../eval/labels/image6.json')); im = cv2.imread('add/image6.jpg')
+L = json.load(open(f'{E}/image6_v2.json')); im = cv2.imread('add/image6.jpg')
 by = collections.defaultdict(list)
 for f in L['facings']:
     if f['label'] not in ('unknown', 'exclude'): by[f['label']].append(f)
diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/ocr_exp.py b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/ocr_exp.py
index 4469929..7b1375a 100644
--- a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/ocr_exp.py
+++ b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/ocr_exp.py
@@ -3,7 +3,7 @@ sys.path.insert(0,'code'); sys.path.insert(0,os.path.dirname(__file__))
 from ocr import VisionOcr
 from diag import crops
 S=os.path.dirname(__file__)
-L=json.load(open(f'{S}/../../../../eval/labels/image6.json'))
+L=json.load(open(f'{S}/image6_v2.json'))
 # distinguishing tokens per SKU (any hit counts); includes siblings for near-unknowns
 KEY={'sku_l01':['エッグケア','卵を使って'],'sku_l02':['アマニ'],'sku_l03':['からし','Spicy'],'sku_l04':['めんたい'],
 'sku_l05':['ゼロ','ノンコレ'],'sku_l06':['フィッテ','内臓脂肪'],'sku_l07':['ラッキー'],'sku_l08':['PERI','ペリ'],
diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/scale.py b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/scale.py
index 9eaefc0..b591da9 100644
--- a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/scale.py
+++ b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/scale.py
@@ -27,7 +27,7 @@ for p in sorted(glob.glob('add/*')):
         x1,y1,x2,y2=[int(v) for v in b['box']]; c=im[max(0,y1):y2,max(0,x1):x2]
         if min(c.shape[:2])>=40: dis.append((f'd_{os.path.basename(p)}_{j}',c))
 random.shuffle(dis); print('distractor refs available',len(dis))
-L=json.load(open(f'{S}/../../../../eval/labels/image6.json')); Q=[(fc,c) for fc,c in crops('add/image6.jpg',L,True) if fc['label']!='exclude' and min(c.shape[:2])>=8]
+L=json.load(open(f'{S}/image6_v2.json')); Q=[(fc,c) for fc,c in crops('add/image6.jpg',L,True) if fc['label']!='exclude' and min(c.shape[:2])>=8]
 Qf=[(fc['label'],)+feat(c,None) for fc,c in Q]
 bf=cv2.BFMatcher(cv2.NORM_L2)
 def verify(kq,dq,r):