runtime: anchor the think read, so Neuron can think at all

engram_think_json passed NULL as the anchor. NULL is not "no opinion":
engram_think re-origins at `anchor ? anchor : region->centroid`, so NULL
means "read from the centroid" — and the centroid is the one point where
the gradient is zero by construction. r = x - centroid = 0, so every axis
projection is 0, grad is 0, and direction takes the "at rest" branch at
engram_cognition.c:137.

Measured consequence: EVERY faculty returned an identical null result,
differing only in its label —
  {"direction":[0,0,0,0,0,0,0,0],"spread":0,"magnitude":1,"confidence":0.5}
magnitude 1 is membership evaluated at the centroid, spread 0 is its
distance to itself, confidence 0.5 is the stance fallback. The geometry was
never at fault: /api/drift computes real values (centroid_sep 0.104,
core_disp 0.045) over the very same 87 members. Neuron could not think
because the read was always taken from the region's own centre.

The seeds choose WHICH region; they must also supply the VANTAGE. Anchor at
the first resolvable embedded seed — the same seed eg_geo_build_desc infers
dim from, so the two can never disagree. One seed still yields a real
gradient because the descriptor expands to that seed's neighbourhood, so
the seed's position is distinct from the neighbourhood centroid.

The vector is COPIED, never borrowed: g->nodes is realloc'd in place on
append, so a borrowed EngramNode* dangles across any concurrent write.

Verified against a clone of the production store (13,616 nodes / 37,865
edges):
  self anchor   n_support 87  magnitude 0.00282  spread 18.79
  values hub    n_support 28  magnitude 0.00318  spread 17.72
with distinct unit direction vectors. Previously both returned the zero
vector with magnitude 1 and spread 0.

STILL OPEN, now isolated by this fix: all five faculties return identical
numbers and confidence stays 0.5, because cog_stance_init is passed NULL
for the stance and the faculty enters the computation only through the
stance's axis_gain[] and bias_dir. The faculty label is inert until a
stance is loaded — which is what learn()'s correspondence-beat calibrates.
Same shape as this bug: a neutral parameter collapsing a capability to a
constant.

DESIGN.md

@@ -61,6 +61,13 @@ Per test file, current build model:
 | `cc` test .c → .o | 0.02s |
 | link | 0.02s |
 
+> **STALE as of el #132 — re-measured 2026-08-16.** The `test_compiler` figure below was
+> *entirely* the `strlen`-per-character quadratic, now fixed. Re-measured on the same host:
+> **3.58s → 0.03s (119x)**, and the 422 KB compiler concatenation likewise compiles in 0.03s.
+> The table is retained only as the historical record that motivated the gate. The remaining
+> per-file cost is the redundant `el_runtime.c` rebuild, which §9's compile-once architecture
+> addresses.
+
 Per-file `elc` time across the existing suite:
 
 | File | Bytes | elc time |
@@ -416,6 +423,48 @@ Wall-clock needs statistics. **Allocation counts do not.** They are perfectly de
 > a level. That is why the gate fits a curve across a sweep instead of comparing one number to a
 > threshold.
 
+> **Second correction, same day — THE ALLOCATION GATE ALONE WOULD HAVE MISSED THE REAL BUG.**
+>
+> el #132 found the actual elc quadratic: `strlen()` called inside `str_char_code()` and
+> `str_slice()`, so the lexer rescanned the remaining input on every character. Pure CPU.
+> **Zero allocation.** `str_char_code` is a bounds check and an index — it allocates nothing.
+>
+> Measured on three controlled specimens (`lang/.work/fitprobe.el`), growth ratio per doubling of
+> n across n = 200/400/800/1600:
+>
+> | specimen | allocs | bytes | time | what it proves |
+> |---|---|---|---|---|
+> | `linear` — one alloc per item | 2.00 2.00 2.00 → **O(n)** | 2.16 2.07 2.23 → **O(n)** | 0.83 2.00 2.05 → **O(n)** | clean baseline |
+> | `accum` — rebuilds accumulator | 2.00 2.00 2.00 → **O(n)** | 3.97 3.99 3.99 → **O(n²)** | noisy | count misses, **bytes catches** |
+> | `compute` — n scans over n chars | 0 → **FLAT** | 0 → **FLAT** | 3.93 4.01 3.96 → **O(n²)** | **both alloc signals blind; only time catches** |
+>
+> `compute` is el #132's shape exactly. A gate fitting only allocation count and bytes classifies
+> it as FLAT and passes it. **The gate as originally specified would not have caught the defect it
+> was created for.**
+>
+> Therefore the gate fits **THREE** signals and fails if ANY exceeds its declared curve:
+>
+> ```
+> bench "elc_compile" over n in [...] expect time O(n) allocs O(n) bytes O(n) { ... }
+> ```
+>
+> - **allocs (count)** — deterministic, zero-noise. Catches per-item allocation growth.
+> - **allocs (bytes)** — deterministic, zero-noise. Catches accumulator-rebuild quadratics that
+>   count cannot see.
+> - **time** — noisy, needs the sweep and statistics. The ONLY signal that sees pure-compute
+>   complexity regressions. Gate on the fitted *exponent*, never on absolute duration, so CI
+>   hardware variance scales the coefficient and leaves the classification intact.
+>
+> The deterministic signals remain preferable where they apply — they need no statistics and are
+> correct on the first run. They are simply not sufficient.
+>
+> **`black_box` is mandatory, and consuming the result is NOT enough.** The first version of
+> `compute` accumulated `total + 1` in a nested loop and reported **0 µs at every n** while
+> returning a numerically correct n². Clang recognised the idiom and closed the loop to a
+> multiply. Feeding the result into output did not prevent it. Only making the inner operation an
+> opaque external call restored the real curve. A benchmark harness that trusts the user to defeat
+> the optimiser will silently measure nothing — and report success while doing it.
+
 Instrument the runtime with allocation counters and fit *those* against n instead of time:
 
 ```el

engram/src/server.el

@@ -288,6 +288,27 @@ fn route_create_node(method: String, path: String, body: String) -> String {
         salience, importance, confidence,
         tier, tags
     )
+    // GEOMETRY INGEST (2026-08-16 self-review): this route accepted an "emb"
+    // field, returned 200 with a fresh id, and stored NOTHING — engram_node_full
+    // has no vector parameter, so the caller's geometry was silently discarded
+    // and the node came back emb_dim=None / embedded:false. Measured live while
+    // trying to admit a voice signal. The consequence was structural, not
+    // cosmetic: text was the only entry medium, so any non-text modality had to
+    // be DESCRIBED in prose and what we then reasoned over was the geometry of
+    // the description, not of the signal.
+    //
+    // "emb" is little-endian float32 hex (dim*8 chars) — the encoding the
+    // perception vessel's /voice/embed already emits, so a realizer's output
+    // moves in with no float-array round trip. "dim" defaults to the vector's
+    // implied width. Off-dimension vectors are stored but not inserted into the
+    // resident index (its build loop filters on emb_dim), so a modality vector
+    // is durable and addressable without perturbing the canonical index.
+    let emb_hex: String = json_get_string(body, "emb")
+    let emb_set: Int = if str_eq(emb_hex, "") { 0 } else {
+        let dim_raw: String = json_get_raw(body, "dim")
+        let dim: Int = if str_eq(dim_raw, "") { str_len(emb_hex) / 8 } else { json_get_int(body, "dim") }
+        engram_node_set_emb(id, emb_hex, dim)
+    }
     let saved: Int = persist_node(id)
     // ORPHAN PREVENTION (ENGRAM_AUTOCONNECT): connect the fresh node to its
     // nearest embedded neighbors so it never enters the graph edgeless.
@@ -298,7 +319,11 @@ fn route_create_node(method: String, path: String, body: String) -> String {
         if added > 0 { let sv2: Int = persist_edges_since(ec0) }
         added
     } else { 0 }
-    "{\"id\":\"" + id + "\",\"content\":\"" + content + "\",\"node_type\":\"" + node_type + "\",\"connected\":" + int_to_str(connected) + "}"
+    // Report whether the supplied geometry actually landed. The old response
+    // was success-shaped regardless — 200 with an id while the vector was
+    // discarded — which is how the drop went unnoticed. A caller can now
+    // assert on emb_set instead of trusting the status code.
+    "{\"id\":\"" + id + "\",\"content\":\"" + content + "\",\"node_type\":\"" + node_type + "\",\"connected\":" + int_to_str(connected) + ",\"emb_set\":" + int_to_str(emb_set) + "}"
 }
 
 fn route_get_node(method: String, path: String, body: String) -> String {

lang/el-compiler/src/codegen.el

@@ -2907,6 +2907,7 @@ fn builtin_arity(name: String) -> Int {
     if str_eq(name, "el_alloc_count") { return 0 }
     if str_eq(name, "el_alloc_bytes") { return 0 }
     if str_eq(name, "el_peak_rss") { return 0 }
+    if str_eq(name, "el_black_box") { return 1 }
     if str_eq(name, "engram_neighbors_json") { return 3 }
     if str_eq(name, "engram_activate_json") { return 2 }
     if str_eq(name, "engram_stats_json") { return 0 }

lang/runtime/el_runtime.c

@@ -5168,10 +5168,23 @@ el_val_t state_get(el_val_t key) {
     if (!k) return el_wrap_str(el_strdup(""));
     pthread_mutex_lock(&_state_mu);
     StateEntry* e = state_find(k);
-    char* result = el_strdup_persist(e ? e->value : "");
+    /* ONE arena-tracked copy, taken under the lock.
+     *
+     * This used to make TWO copies: an el_strdup_persist temporary, then an
+     * arena-tracked copy of that temporary. The persistent one was never
+     * returned and never freed — el_strdup_persist bypasses the arena by
+     * design ("state_set, engram internals"), so arena-pop could not reclaim
+     * it. Every state_get therefore leaked its full value string, permanently.
+     *
+     * The soul's awareness loop has 68 state_get call sites and ticks every
+     * 200ms; measured leak was ~1.1 MB per tick, about 19 GB/hour. It went
+     * unnoticed for as long as the soul restarted often enough to mask it.
+     *
+     * el_strdup tracks into the thread-local arena, which touches no shared
+     * state, so doing it under _state_mu is safe and removes the need for the
+     * temporary entirely. */
+    char* copy = el_strdup(e ? e->value : "");
     pthread_mutex_unlock(&_state_mu);
-    /* wrap in arena-tracked copy for the caller's request lifetime */
-    char* copy = el_strdup(result);
     return el_wrap_str(copy);
 }
 
@@ -8494,6 +8507,80 @@ el_val_t engram_node_count(void) {
     return (el_val_t)engram_get()->node_count;
 }
 
+/* engram_node_set_emb — attach GEOMETRY to an existing node.
+ *
+ * WHY THIS EXISTS (2026-08-16). Until now no ingest path could carry a
+ * vector. engram_node / engram_node_full / engram_node_layered take text
+ * only, and the sole way a node acquired an embedding was
+ * engram_embed_backfill DERIVING one from n->content. That made text the
+ * mandatory entry medium: any non-text modality (audio, image, sensor)
+ * had to be described in prose first, and the geometry we then reasoned
+ * over was the geometry OF THE DESCRIPTION, not of the signal. Measured
+ * consequence: POST /api/nodes accepted an "emb" field, returned 200 with
+ * a fresh id, and stored emb_dim=None / embedded:false — the vector was
+ * silently discarded because no parameter existed to receive it.
+ *
+ * `hex` is little-endian float32, the encoding the perception vessel's
+ * /voice/embed already emits, so a realizer's output moves in without a
+ * JSON float-array round trip. Length must be exactly dim*8 hex chars.
+ *
+ * DIMENSION POLICY: dim need NOT equal the canonical text-embedding dim.
+ * A modality vector of a different width is stored and is simply not
+ * inserted into the resident HNSW index, whose build loop already filters
+ * on `n->emb_dim == dim`. So off-dimension geometry is durable and
+ * addressable without perturbing the canonical index.
+ *
+ * Setting emb also makes the node ineligible for embed_backfill (which
+ * only fills nodes with no emb), so a realizer's vector is never
+ * overwritten by a text-derived one.
+ *
+ * Returns 1 on success, 0 on unknown id / malformed hex / bad dim. */
+el_val_t engram_node_set_emb(el_val_t id, el_val_t hex, el_val_t dim) {
+    const char* sid = EL_CSTR(id);
+    const char* sh  = EL_CSTR(hex);
+    int32_t d = (int32_t)(int64_t)dim;
+    /* Bound the allocation. No max-dim constant existed because no caller
+     * could supply a dim before this function; 8192 is generous for any
+     * realizer (canonical text embeddings are 768, MFCC voice stats 64)
+     * while keeping a malformed `dim` from requesting an unbounded malloc. */
+    if (!sid || !*sid || !sh || d <= 0 || d > 8192) return (el_val_t)0;
+
+    size_t need = (size_t)d * 8u;           /* 4 bytes → 8 hex chars per float */
+    if (strlen(sh) != need) return (el_val_t)0;
+
+    EngramNode* n = engram_find_node(sid);
+    if (!n) return (el_val_t)0;
+
+    float* v = (float*)malloc(sizeof(float) * (size_t)d);
+    if (!v) return (el_val_t)0;
+
+    for (int32_t i = 0; i < d; i++) {
+        uint32_t w = 0;
+        for (int k = 0; k < 8; k++) {
+            char c = sh[(size_t)i * 8u + (size_t)k];
+            uint32_t nib;
+            if      (c >= '0' && c <= '9') nib = (uint32_t)(c - '0');
+            else if (c >= 'a' && c <= 'f') nib = (uint32_t)(c - 'a' + 10);
+            else if (c >= 'A' && c <= 'F') nib = (uint32_t)(c - 'A' + 10);
+            else { free(v); return (el_val_t)0; }
+            w = (w << 4) | nib;
+        }
+        /* Hex is emitted little-endian byte order; rebuild the word. */
+        uint32_t le = ((w & 0x000000FFu) << 24) | ((w & 0x0000FF00u) << 8) |
+                      ((w & 0x00FF0000u) >> 8)  | ((w & 0xFF000000u) >> 24);
+        float f;
+        memcpy(&f, &le, sizeof(f));
+        v[i] = f;
+    }
+
+    free(n->emb);
+    n->emb     = v;
+    n->emb_dim = d;
+    n->updated_at = engram_now_ms();
+    if (engram_store_enabled()) eg_store_put_node(n);
+    return (el_val_t)1;
+}
+
 /* ── Telemetry retention ────────────────────────────────────────────────────
  * (2026-07-16 self-review) InternalStateEvent nodes are append-only telemetry
  * (heartbeat, curiosity_scan, engram_sync) written ~3/min by the awareness
@@ -11289,6 +11376,15 @@ static void engram_emit_node_json(JsonBuf* b, const EngramNode* n, int include_e
     snprintf(tmp, sizeof(tmp), ",\"wm_anchor\":%g",             n->wm_anchor);             jb_puts(b, tmp);
     snprintf(tmp, sizeof(tmp), ",\"base_level\":%g",
              engram_bll_base_level(n, engram_now_ms()));                                   jb_puts(b, tmp);
+    /* GEOMETRY VISIBILITY (2026-08-16 self-review): the node document never
+     * said whether the node carried a vector, so a read-back could not tell
+     * "has geometry" from "text only". Not cosmetic — it is exactly how a
+     * real ingest drop and a mere reporting gap became indistinguishable,
+     * and I misdiagnosed one as the other for an hour. Always emit the width
+     * and the boolean; the vector itself stays behind include_emb since it
+     * is large and most callers do not want it inline. */
+    snprintf(tmp, sizeof(tmp), ",\"emb_dim\":%d,\"embedded\":%s",
+             (int)n->emb_dim, (n->emb && n->emb_dim > 0) ? "true" : "false");              jb_puts(b, tmp);
     /* Base-level access history: chronological (oldest→newest) compact
      * string. Loaders replay it through engram_bll_record_access; absent
      * field = empty ring (optimized-form fallback). (2026-07-22) */
@@ -13679,7 +13775,65 @@ el_val_t engram_think_json(el_val_t seeds, el_val_t faculty) {
     if (!g) return eg_geo_err("geometry unavailable");
     CogStance st; cog_stance_init(&st, NULL, EL_CSTR(faculty), g->hub_id, NULL, g);
     GeoGradient grad;
-    if (engram_think(g, NULL, &st, &grad) != 0) { cog_stance_free(&st); engram_geo_free(g); return eg_geo_err("think failed"); }
+
+    /* ANCHOR THE READ (2026-08-16 self-review). This passed NULL, and NULL is
+     * not "no opinion" — engram_think re-origins at `anchor ? anchor :
+     * region->centroid`, so NULL means "read from the centroid", and the
+     * centroid is the ONE point where the gradient is zero by construction:
+     * r = x - centroid = 0, so every axis projection is 0, grad is 0, and
+     * direction takes the "at rest" branch. Measured consequence: EVERY
+     * faculty — reason, abduce, induce, plan, analogize — returned an
+     * identical null result, differing only in its label:
+     *     {"direction":[0,0,...],"spread":0,"magnitude":1,"confidence":0.5}
+     * magnitude 1 is membership evaluated at the centroid, spread 0 is its
+     * distance to itself, and confidence 0.5 is the stance fallback. The
+     * geometry was never the problem — /api/drift computes real values
+     * (centroid_sep 0.104, core_disp 0.045) over the very same 87 members.
+     * Neuron could not think because the read was always taken from the
+     * region's own centre.
+     *
+     * The seeds choose WHICH region; they must also supply the VANTAGE it is
+     * read from. Anchor at the first resolvable embedded seed — the same seed
+     * eg_geo_build_desc infers `dim` from, so the two never disagree. A single
+     * seed still yields a real gradient because the descriptor expands to the
+     * seed's neighbourhood (87 members for the self anchor), so the seed's own
+     * position is distinct from the neighbourhood centroid.
+     *
+     * COPY the vector, never borrow it: g->nodes is realloc'd in place on
+     * append, so a borrowed EngramNode* is a dangling pointer across any
+     * concurrent write. 768 floats is 3 KB. */
+    float* anchor = NULL;
+    {
+        EngramStore* eg = engram_get();
+        const char* csv = EL_CSTR(seeds);
+        if (eg && csv) {
+            const char* p = csv;
+            while (*p && !anchor) {
+                while (*p == ' ' || *p == ',') p++;
+                const char* s = p;
+                while (*p && *p != ',') p++;
+                const char* e = p; while (e > s && e[-1] == ' ') e--;
+                if (e > s) {
+                    char* id = strndup(s, (size_t)(e - s));
+                    if (id) {
+                        int64_t idx = engram_find_node_index(id);
+                        if (idx >= 0 && idx < eg->node_count) {
+                            EngramNode* n = &eg->nodes[idx];
+                            if (n->emb && n->emb_dim == g->dim) {
+                                anchor = malloc(sizeof(float) * (size_t)g->dim);
+                                if (anchor) memcpy(anchor, n->emb,
+                                                   sizeof(float) * (size_t)g->dim);
+                            }
+                        }
+                        free(id);
+                    }
+                }
+            }
+        }
+    }
+
+    if (engram_think(g, anchor, &st, &grad) != 0) { free(anchor); cog_stance_free(&st); engram_geo_free(g); return eg_geo_err("think failed"); }
+    free(anchor);
     JsonBuf b; jb_init(&b); char t[256];
     snprintf(t, sizeof t, "{\"faculty\":\"%s\",\"n_support\":%d,\"magnitude\":%.6g,\"spread\":%.6g,\"confidence\":%.6g,\"dim\":%d",
              EL_CSTR(faculty), grad.n_support, grad.magnitude, grad.spread, grad.confidence, grad.dim);
@@ -18522,6 +18676,26 @@ el_val_t engram_pool_stats_json(void) {
 el_val_t el_alloc_count(void) { return (el_val_t)(int64_t)_el_alloc_count; }
 el_val_t el_alloc_bytes(void) { return (el_val_t)(int64_t)_el_alloc_bytes; }
 
+/* el_black_box — optimisation barrier for benchmark bodies.
+ *
+ * WHY THIS IS NOT OPTIONAL. A benchmark whose result is unused is dead code,
+ * and CONSUMING THE RESULT IS NOT SUFFICIENT: clang recognises loop idioms and
+ * closes them to arithmetic. A nested `total = total + 1` loop measured at
+ * 0 microseconds for every n while returning a numerically correct n*n --
+ * the answer was right and the work never happened.
+ *
+ * That is the same failure shape as a test that never ran reporting pass. The
+ * harness must own the barrier rather than trusting the benchmark author to
+ * defeat the optimiser.
+ *
+ * The constraint "+r" forces the value through a register the compiler must
+ * treat as both read and written by opaque code; the "memory" clobber stops
+ * loads and stores being reordered across it or elided. Emits no instructions. */
+el_val_t el_black_box(el_val_t v) {
+    __asm__ __volatile__("" : "+r"(v) : : "memory");
+    return v;
+}
+
 el_val_t el_peak_rss(void) {
     struct rusage ru;
     if (getrusage(RUSAGE_SELF, &ru) != 0) return (el_val_t)0;

lang/runtime/el_runtime.h

@@ -613,6 +613,11 @@ void      engram_strengthen(el_val_t node_id);
 void      engram_forget(el_val_t node_id);
 el_val_t  engram_prune_telemetry(el_val_t older_than_ms);
 el_val_t  engram_node_count(void);
+/* Attach geometry to an existing node. `hex` is little-endian float32,
+ * exactly dim*8 hex chars — the encoding realizers already emit. Lets a
+ * non-text modality enter as geometry instead of being described in prose
+ * and embedded as its description. Returns 1 on success, 0 otherwise. */
+el_val_t  engram_node_set_emb(el_val_t id, el_val_t hex, el_val_t dim);
 el_val_t  engram_search(el_val_t query, el_val_t limit);
 el_val_t  engram_scan_nodes(el_val_t limit, el_val_t offset);
 void      engram_connect(el_val_t from_id, el_val_t to_id, el_val_t weight, el_val_t relation);
@@ -1022,6 +1027,7 @@ el_val_t  el_mem_check(void);
 el_val_t  el_alloc_count(void);
 el_val_t  el_alloc_bytes(void);
 el_val_t  el_peak_rss(void);
+el_val_t  el_black_box(el_val_t v);
 
 /* Semantic retrieval surface. NOT interchangeable with engram_search_json,
  * which is lexical by design — see the note at the definition. */

lang/runtime/elbench.el

@@ -0,0 +1,256 @@
+// runtime/elbench.el — growth-curve classifier and complexity gate.
+//
+// Given a geometric sweep of input sizes and the measurements taken at each,
+// classify the growth curve and decide whether it violates a declared bound.
+//
+// ── Why this exists ──────────────────────────────────────────────────────────
+//
+// Constant-factor regressions are annoying. Complexity regressions are outages.
+// An O(n) lookup inside an O(n) loop is invisible at n=100 in a unit test and
+// catastrophic at n=100000 in production. el #132 was exactly that: a strlen()
+// inside a per-character accessor, quadratic, shipped for months.
+//
+// ── THREE signals, not one ───────────────────────────────────────────────────
+//
+// The gate fits time AND allocation-count AND allocation-bytes, and fails if
+// ANY of them exceeds its declared curve. This is not belt-and-braces; each
+// signal is blind to a real defect class the others catch:
+//
+//   * A copy-on-write accumulator rebuilding its buffer allocates ONCE per
+//     iteration — count is exactly linear — while bytes go quadratic.
+//     Count alone passes it.
+//   * el #132's strlen-per-character is pure CPU and allocates NOTHING.
+//     Both allocation signals read FLAT. Only time catches it.
+//
+// The deterministic signals (count, bytes) are preferable where they apply:
+// no statistics, correct on the first run, machine-independent. They are
+// simply not sufficient.
+//
+// ── SCOPE LIMIT — read this before trusting a flat curve ─────────────────────
+//
+// The allocation counters track EL-LEVEL allocation only: strings, ElList and
+// ElMap bodies, their backing arrays, copy-on-write clones, and the realloc
+// growth path. malloc inside engram_*.c and inside libcurl is NOT counted.
+//
+// A flat allocation curve over a workload dominated by engram or HTTP calls is
+// therefore NOT evidence of anything. It means "no El-level allocation growth",
+// not "no allocation growth". Gate El-level complexity with this; do not read
+// third-party memory behaviour into it.
+//
+// ── Classification method ────────────────────────────────────────────────────
+//
+// Sizes must form a geometric sweep (each n double the last). On such a sweep
+// the ratio between consecutive measurements IS the growth exponent, directly:
+//
+//     O(1)  -> 1.0     O(log n) -> ~1.1     O(n) -> 2.0
+//     O(n log n) -> ~2.2     O(n^2) -> 4.0     O(n^3) -> 8.0
+//
+// DEVIATION FROM DESIGN.md 6.2, stated plainly: that section specified Google
+// Benchmark's one-parameter least-squares fit over candidate curves. This uses
+// consecutive ratios instead. The sweep is mandated geometric either way, and
+// on a geometric sweep ratios are directly interpretable and need no floating
+// point. The cost is weaker separation between O(n) and O(n log n), which is
+// reported honestly as an ambiguous band rather than guessed at. Least-squares
+// remains the better answer if that band ever needs to be resolved.
+//
+// All arithmetic is fixed-point, scaled by 1000 ("milli-ratio"), so a ratio of
+// 2.0 is 2000. El values are int64; this avoids float-in-list handling.
+
+// Curve identifiers. Ordered by growth — the ordering IS the comparison used
+// by the gate, so an index comparison decides "worse than declared".
+//   0 = O(1)   1 = O(log n)   2 = O(n)   3 = O(n log n)   4 = O(n^2)   5 = O(n^3)
+
+fn elb_curve_name(c: Int) -> String {
+    if c == 0 { return "O(1)" }
+    if c == 1 { return "O(log n)" }
+    if c == 2 { return "O(n)" }
+    if c == 3 { return "O(n log n)" }
+    if c == 4 { return "O(n^2)" }
+    if c == 5 { return "O(n^3)" }
+    return "O(?)"
+}
+
+fn elb_curve_from_name(s: String) -> Int {
+    if str_eq(s, "O(1)") { return 0 }
+    if str_eq(s, "O(log n)") { return 1 }
+    if str_eq(s, "O(n)") { return 2 }
+    if str_eq(s, "O(n log n)") { return 3 }
+    if str_eq(s, "O(n^2)") { return 4 }
+    if str_eq(s, "O(n^3)") { return 5 }
+    return -1
+}
+
+// elb_classify_ratio — map a milli-ratio-per-doubling onto a curve.
+//
+// Bands are deliberately wide at the top (a quadratic measured at 3.4x is
+// still a quadratic) and deliberately overlap-averse at the bottom, where a
+// misclassification between O(1) and O(log n) matters least.
+fn elb_classify_ratio(milli: Int) -> Int {
+    if milli < 1300 { return 0 }
+    if milli < 1700 { return 1 }
+    if milli < 2400 { return 2 }
+    if milli < 3200 { return 3 }
+    if milli < 6000 { return 4 }
+    return 5
+}
+
+// elb_ratio — milli-ratio between two consecutive measurements.
+// Returns -1 when the earlier measurement is zero (ratio undefined).
+fn elb_ratio(prev: Int, cur: Int) -> Int {
+    if prev <= 0 { return -1 }
+    return (cur * 1000) / prev
+}
+
+// ── The measurement floor ────────────────────────────────────────────────────
+//
+// A benchmark whose largest measurement is at or near zero has not been
+// measured. Reporting it as O(1) would be a confident answer with nothing
+// behind it — the same failure as a test that never ran reporting pass, and
+// exactly what happened when clang closed a nested loop to a multiply and the
+// harness read 0 microseconds at every n.
+//
+// So: REFUSE. Never classify below the floor.
+fn elb_below_floor(vals: [Int], floor: Int) -> Bool {
+    let n: Int = native_list_len(vals)
+    let i: Int = 0
+    let mx: Int = 0
+    while i < n {
+        let v: Int = native_list_get(vals, i)
+        if v > mx { let mx = v }
+        let i = i + 1
+    }
+    if mx < floor { return true }
+    return false
+}
+
+// elb_implausibly_flat — a measurement that does not move across a sweep whose
+// input grew by 8x or more is not a flat curve, it is a broken measurement.
+// Genuine O(1) work still shows noise; a hard-flat series means the work was
+// optimised away, the timer has insufficient resolution, or the benchmark body
+// never executed.
+fn elb_implausibly_flat(vals: [Int]) -> Bool {
+    let n: Int = native_list_len(vals)
+    if n < 3 { return false }
+    let first: Int = native_list_get(vals, 0)
+    let last: Int = native_list_get(vals, n - 1)
+    if first == 0 {
+        if last == 0 { return true }
+        return false
+    }
+    let r: Int = (last * 1000) / first
+    if r < 1100 { return true }
+    return false
+}
+
+// elb_spread_ok — do the consecutive ratios agree with each other?
+//
+// This is the ratio-method analogue of a normalised-RMS threshold. If the
+// doublings disagree wildly the data is noise, a cache cliff, or a phase
+// change, and the honest report is INDETERMINATE rather than a classification.
+// Applies to the ASYMPTOTIC TAIL only — the last three ratios.
+//
+// The small-n end of any sweep is dominated by fixed overhead, cold caches and
+// branch predictors that have not warmed. Measured on a genuinely linear
+// character scan, the ratios ran 3.37, 2.92, 1.76, 1.65: the head looks
+// quadratic, the tail is the truth. Checking spread across the whole sweep
+// therefore rejects correct data. A complexity bound is an asymptotic claim, so
+// it is judged on the asymptotic region — the same reason a benchmark harness
+// discards warmup rather than averaging it in.
+fn elb_spread_ok(ratios: [Int]) -> Bool {
+    let total: Int = native_list_len(ratios)
+    if total < 2 { return true }
+    let start: Int = total - 3
+    if start < 0 { let start = 0 }
+    let n: Int = total
+    let lo: Int = 999999
+    let hi: Int = 0
+    let i: Int = start
+    while i < n {
+        let r: Int = native_list_get(ratios, i)
+        if r >= 0 {
+            if r < lo { let lo = r }
+            if r > hi { let hi = r }
+        }
+        let i = i + 1
+    }
+    if lo <= 0 { return false }
+    // Reject when the widest ratio is more than 2.2x the narrowest. That is
+    // enough slack for real timing noise and tight enough to separate a clean
+    // 2.0 series from a clean 4.0 series.
+    if (hi * 1000) / lo > 2200 { return false }
+    return true
+}
+
+// elb_ratios — consecutive milli-ratios across the sweep.
+fn elb_ratios(vals: [Int]) -> [Int] {
+    let out: [Int] = native_list_empty()
+    let n: Int = native_list_len(vals)
+    let i: Int = 1
+    while i < n {
+        let out = native_list_append(out,
+            elb_ratio(native_list_get(vals, i - 1), native_list_get(vals, i)))
+        let i = i + 1
+    }
+    return out
+}
+
+// elb_mean_tail_ratio — mean of the LAST TWO ratios.
+//
+// The tail is used deliberately: asymptotic behaviour is what a complexity
+// bound claims, and the small-n end of any sweep is dominated by fixed
+// overhead. This is the same reason a benchmark harness discards warmup.
+fn elb_mean_tail_ratio(ratios: [Int]) -> Int {
+    let n: Int = native_list_len(ratios)
+    if n == 0 { return -1 }
+    if n == 1 { return native_list_get(ratios, 0) }
+    let a: Int = native_list_get(ratios, n - 1)
+    let b: Int = native_list_get(ratios, n - 2)
+    if a < 0 { return b }
+    if b < 0 { return a }
+    return (a + b) / 2
+}
+
+// ── Verdicts ─────────────────────────────────────────────────────────────────
+//
+//   0 PASS           measured curve is at or below the declared bound
+//   1 FAIL           measured curve is strictly worse than declared
+//   2 INDETERMINATE  ratios disagree; data is noise or a phase change
+//   3 REFUSED        below the measurement floor, or implausibly flat
+//   4 BETTER         measured strictly better than declared (warn, not fail)
+
+fn elb_verdict_name(v: Int) -> String {
+    if v == 0 { return "PASS" }
+    if v == 1 { return "FAIL" }
+    if v == 2 { return "INDETERMINATE" }
+    if v == 3 { return "REFUSED" }
+    if v == 4 { return "BETTER" }
+    return "?"
+}
+
+// elb_gate — classify one signal against its declared bound.
+//
+//   vals     measurements, one per sweep point, in sweep order
+//   expect   declared curve index (see elb_curve_name)
+//   floor    minimum largest-measurement below which we refuse to classify
+fn elb_gate(vals: [Int], expect: Int, floor: Int) -> Int {
+    if elb_below_floor(vals, floor) { return 3 }
+    if elb_implausibly_flat(vals) { return 3 }
+    let ratios: [Int] = elb_ratios(vals)
+    if !elb_spread_ok(ratios) { return 2 }
+    let m: Int = elb_mean_tail_ratio(ratios)
+    if m < 0 { return 2 }
+    let got: Int = elb_classify_ratio(m)
+    if got > expect { return 1 }
+    if got < expect { return 4 }
+    return 0
+}
+
+// elb_measured_curve — the classified curve for a signal, or -1 if unclassifiable.
+fn elb_measured_curve(vals: [Int], floor: Int) -> Int {
+    if elb_below_floor(vals, floor) { return -1 }
+    if elb_implausibly_flat(vals) { return -1 }
+    let ratios: [Int] = elb_ratios(vals)
+    let m: Int = elb_mean_tail_ratio(ratios)
+    if m < 0 { return -1 }
+    return elb_classify_ratio(m)
+}

lang/tests/bench/fitprobe.el

@@ -0,0 +1,91 @@
+// fitprobe.el — controlled growth-curve specimens for validating the complexity fitter.
+//
+// Three deliberately-shaped workloads. None depends on a real defect existing,
+// which is the point: the fitter must be provable against KNOWN curves.
+//
+//   linear   — one allocation per item.        count O(n),  bytes O(n),   time O(n)
+//   accum    — rebuilds its accumulator.       count O(n),  bytes O(n^2), time O(n^2)
+//   compute  — nested arithmetic, no alloc.    count O(1),  bytes O(1),   time O(n^2)
+//
+// `compute` is the specimen that matters. It is the shape of el #132
+// (strlen-per-character inside str_char_code): pure CPU, zero allocation.
+// An allocation-only gate is structurally blind to it.
+//
+// No imports — uses runtime builtins directly so nothing collides.
+
+fn work_linear(n: Int) -> Int {
+    let parts: [String] = native_list_empty()
+    let i: Int = 0
+    while i < n {
+        let parts = native_list_append(parts, int_to_str(i))
+        let i = i + 1
+    }
+    return native_list_len(parts)
+}
+
+fn work_accum(n: Int) -> Int {
+    let acc: String = ""
+    let i: Int = 0
+    while i < n {
+        let acc = acc + "x"
+        let i = i + 1
+    }
+    return str_len(acc)
+}
+
+fn work_compute(n: Int) -> Int {
+    // str_char_code is an opaque external call, so the C optimiser cannot
+    // reduce this nest to a closed form the way it does with `total + 1`.
+    // This is the exact shape of el #132: n scans over n characters, pure
+    // CPU, ZERO allocation.
+    let s: String = "abcdefghij"
+    let total: Int = 0
+    let i: Int = 0
+    while i < n {
+        let j: Int = 0
+        while j < n {
+            let total = total + str_char_code(s, 0)
+            let j = j + 1
+        }
+        let i = i + 1
+    }
+    return total
+}
+
+fn run_one(mode: String, n: Int) {
+    let c0: Int = el_alloc_count()
+    let b0: Int = el_alloc_bytes()
+    let t0: Int = el_now_instant()
+
+    let r: Int = 0
+    if str_eq(mode, "linear")  { let r = work_linear(n) }
+    if str_eq(mode, "accum")   { let r = work_accum(n) }
+    if str_eq(mode, "compute") { let r = work_compute(n) }
+
+    let t1: Int = el_now_instant()
+    let c1: Int = el_alloc_count()
+    let b1: Int = el_alloc_bytes()
+
+    println(mode + "\t" + int_to_str(n)
+        + "\t" + int_to_str(c1 - c0)
+        + "\t" + int_to_str(b1 - b0)
+        + "\t" + int_to_str((t1 - t0) / 1000)
+        + "\t" + int_to_str(r))
+    return
+}
+
+fn sweep(mode: String) {
+    run_one(mode, 200)
+    run_one(mode, 400)
+    run_one(mode, 800)
+    run_one(mode, 1600)
+    return
+}
+
+fn main() -> Int {
+    println("mode\tn\tallocs\tbytes\tusec\tsink")
+    sweep("linear")
+    sweep("accum")
+    sweep("compute")
+    return 0
+}

lang/tests/native/test_elbench.el

@@ -0,0 +1,111 @@
+import "../../runtime/eltest.el"
+import "../../runtime/elbench.el"
+
+// test_elbench.el — proves the growth-curve classifier against KNOWN curves.
+//
+// Every series below is real measured data from lang/tests/bench/fitprobe.el
+// on a geometric sweep n = 200/400/800/1600. The classifier must be provable
+// without depending on a live defect existing, which is the whole point of
+// keeping controlled specimens.
+
+fn _s4(a: Int, b: Int, c: Int, d: Int) -> [Int] {
+    let l: [Int] = native_list_empty()
+    let l = native_list_append(l, a)
+    let l = native_list_append(l, b)
+    let l = native_list_append(l, c)
+    let l = native_list_append(l, d)
+    return l
+}
+
+test "classifies a linear allocation series as O(n)" {
+    // fitprobe `linear`, allocation count
+    let v = _s4(208, 409, 810, 1611)
+    assert elb_measured_curve(v, 10) == 2, "linear allocs should classify O(n)"
+}
+
+test "classifies a linear byte series as O(n)" {
+    // fitprobe `linear`, allocation bytes
+    let v = _s4(4786, 9682, 19474, 39658)
+    assert elb_measured_curve(v, 10) == 2, "linear bytes should classify O(n)"
+}
+
+test "classifies a quadratic byte series as O(n^2)" {
+    // fitprobe `accum`, allocation bytes -- the accumulator-rebuild shape
+    let v = _s4(20300, 80600, 321200, 1282400)
+    assert elb_measured_curve(v, 10) == 4, "accum bytes should classify O(n^2)"
+}
+
+test "accumulator count is linear -- proves count alone misses it" {
+    // Same run as above. The COUNT is exactly linear while bytes are
+    // quadratic. A count-only gate passes this defect clean.
+    let v = _s4(200, 400, 800, 1600)
+    assert elb_measured_curve(v, 10) == 2, "accum count classifies O(n)"
+    assert elb_gate(v, 2, 10) == 0, "count-only gate PASSES the quadratic"
+}
+
+test "classifies a quadratic time series as O(n^2)" {
+    // fitprobe `compute` -- el #132's shape: n scans over n characters
+    let v = _s4(67, 205, 818, 3268)
+    assert elb_measured_curve(v, 10) == 4, "compute time should classify O(n^2)"
+}
+
+test "REFUSES an all-zero series instead of calling it O(1)" {
+    // fitprobe `compute` allocation count. Pure CPU, allocates nothing.
+    // Reporting O(1) here would be a confident answer with nothing behind it.
+    let v = _s4(0, 0, 0, 0)
+    assert elb_gate(v, 2, 10) == 3, "all-zero series must be REFUSED"
+    assert elb_measured_curve(v, 10) < 0, "unclassifiable returns -1"
+}
+
+test "REFUSES an implausibly flat series" {
+    // The shape produced when clang closes a loop to a multiply: a real
+    // answer, no work done, no movement across an 8x input range.
+    let v = _s4(1000, 1001, 1002, 1003)
+    assert elb_gate(v, 2, 10) == 3, "hard-flat series must be REFUSED"
+}
+
+test "gate FAILS a quadratic declared as linear" {
+    let v = _s4(20300, 80600, 321200, 1282400)
+    assert elb_gate(v, 2, 10) == 1, "O(n^2) measured vs O(n) declared must FAIL"
+}
+
+test "gate PASSES a linear series declared as linear" {
+    let v = _s4(208, 409, 810, 1611)
+    assert elb_gate(v, 2, 10) == 0, "O(n) measured vs O(n) declared must PASS"
+}
+
+test "gate reports BETTER when measured beats the declared bound" {
+    let v = _s4(208, 409, 810, 1611)
+    assert elb_gate(v, 4, 10) == 4, "O(n) measured vs O(n^2) declared is BETTER"
+}
+
+test "gate reports INDETERMINATE on disagreeing ratios" {
+    // fitprobe `linear` WALL TIME at these sizes: 26/19/43/78 microseconds.
+    // Ratios 0.73, 2.26, 1.81 disagree well past the noise threshold. The
+    // honest answer is "cannot tell", not a classification -- this is exactly
+    // why benchmarks need auto-scaled iteration counts rather than one shot.
+    let v = _s4(26, 19, 43, 78)
+    assert elb_gate(v, 2, 10) == 2, "disagreeing ratios must be INDETERMINATE"
+}
+
+test "black_box is a real barrier and returns its input" {
+    assert el_black_box(42) == 42, "black_box is value-preserving"
+    let s: Int = 0
+    let i: Int = 0
+    while i < 100 {
+        // Bind the call before using it in arithmetic: `x + call(...)`
+        // lowers to el_str_concat() on integers. Same inference defect
+        // as `call(...) == y` lowering to str_eq().
+        let bx: Int = el_black_box(1)
+        let s = s + bx
+        let i = i + 1
+    }
+    assert s == 100, "black_box does not disturb the computation"
+}
+
+test "curve names round-trip" {
+    assert elb_curve_from_name("O(n)") == 2, "O(n) parses"
+    assert elb_curve_from_name("O(n^2)") == 4, "O(n^2) parses"
+    assert str_eq(elb_curve_name(4), "O(n^2)"), "O(n^2) renders"
+    assert elb_curve_from_name("O(nonsense)") < 0, "unknown curve is -1"
+}

lang/tests/native/test_lexer_scaling.el

@@ -0,0 +1,178 @@
+import "../../runtime/eltest.el"
+import "../../runtime/elbench.el"
+
+// test_lexer_scaling.el — THE ARMED GATE.
+//
+// This is the regression test that would have caught el #132.
+//
+// #132 was a strlen() inside str_char_code() and str_slice(). The lexer walks
+// source one character at a time, so every character access rescanned the whole
+// remaining input: O(n) per character over n characters = O(n^2). It shipped for
+// months. It was found by a geometric sweep, not by reading code.
+//
+// So this test IS a geometric sweep. It scans a string of length n, character by
+// character, at four doubling sizes, and asserts the cost is linear. If anyone
+// reintroduces a per-character rescan — in str_char_code, in str_slice, in any
+// accessor the lexer leans on — the measured curve becomes O(n^2) and this fails.
+//
+// The value is in it being ARMED, not in it currently failing. It passes today
+// because #132 is fixed. That is the correct state for a regression gate.
+//
+// Note the deliberate `let c: Int = str_char_code(...)` binding in the scan loop.
+// Inlining it as `total + str_char_code(s, i)` lowers to el_str_concat() on
+// integers — the Plus arm of the operator-typing family, still open at the time
+// of writing. Binding first is the safe form.
+
+// _mk_string — build a string of length >= n by DOUBLING.
+//
+// Deliberately not `s = s + "x"` n times: that is itself quadratic in bytes and
+// would contaminate the very measurement this test exists to take. Doubling
+// allocates ~2n total.
+fn _mk_string(n: Int) -> String {
+    let s: String = "abcdefgh"
+    while str_len(s) < n {
+        let s = s + s
+    }
+    return s
+}
+
+// _scan — walk the string one character at a time, REPS times.
+//
+// This is the lexer's access pattern reduced to its essential shape. The
+// repetitions lift the measurement clear of timer resolution; without them the
+// smaller sizes land in noise and the classifier correctly reports
+// INDETERMINATE rather than guessing.
+fn _scan(s: String, n: Int, reps: Int) -> Int {
+    let total: Int = 0
+    let r: Int = 0
+    while r < reps {
+        let i: Int = 0
+        while i < n {
+            let c: Int = str_char_code(s, i)
+            let total = total + c
+            let i = i + 1
+        }
+        let r = r + 1
+    }
+    return total
+}
+
+// _measure_scan — microseconds for a full scan sweep point.
+fn _measure_scan(n: Int, reps: Int) -> Int {
+    let s: String = _mk_string(n)
+    // WARMUP, discarded. Without it the small-n end of the sweep is dominated
+    // by cold caches and reads as superlinear on genuinely linear work --
+    // measured ratios 3.37 2.92 1.76 1.65 on exactly this workload.
+    let w: Int = _scan(s, n, 2)
+    let wj: Int = el_black_box(w)
+    let t0: Int = el_now_instant()
+    let got: Int = _scan(s, n, reps)
+    let t1: Int = el_now_instant()
+    // Feed the result through the barrier so the scan cannot be elided.
+    let sink: Int = el_black_box(got)
+    if sink == 0 { println("") }
+    return (t1 - t0) / 1000
+}
+
+fn _series4(a: Int, b: Int, c: Int, d: Int) -> [Int] {
+    let l: [Int] = native_list_empty()
+    let l = native_list_append(l, a)
+    let l = native_list_append(l, b)
+    let l = native_list_append(l, c)
+    let l = native_list_append(l, d)
+    return l
+}
+
+test "character scan is LINEAR in time -- regression gate for el #132" {
+    let reps: Int = 40
+    let t1: Int = _measure_scan(16384, reps)
+    let t2: Int = _measure_scan(32768, reps)
+    let t3: Int = _measure_scan(65536, reps)
+    let t4: Int = _measure_scan(131072, reps)
+    let series: [Int] = _series4(t1, t2, t3, t4)
+
+    let verdict: Int = elb_gate(series, 2, 50)
+    let measured: Int = elb_measured_curve(series, 50)
+
+    // Report the actual numbers regardless of outcome. A gate that fires
+    // without showing its evidence is just an assertion.
+    println("      scan us: " + int_to_str(t1) + " " + int_to_str(t2) + " "
+        + int_to_str(t3) + " " + int_to_str(t4)
+        + "  -> " + elb_curve_name(measured) + " [" + elb_verdict_name(verdict) + "]")
+
+    // PASS (0) or BETTER (4) are both acceptable. FAIL (1) means someone
+    // reintroduced superlinear per-character cost. REFUSED (3) or
+    // INDETERMINATE (2) mean the measurement is untrustworthy -- which is
+    // also a failure of this test, deliberately: a gate that cannot measure
+    // must not report success.
+    assert verdict == 0 || verdict == 4, "character scan must measure O(n) or better"
+}
+
+test "string building by doubling stays linear in allocated bytes" {
+    let b1: Int = el_alloc_bytes()
+    let s1: String = _mk_string(8192)
+    let b2: Int = el_alloc_bytes()
+    let s2: String = _mk_string(16384)
+    let b3: Int = el_alloc_bytes()
+    let s3: String = _mk_string(32768)
+    let b4: Int = el_alloc_bytes()
+    let s4: String = _mk_string(65536)
+    let b5: Int = el_alloc_bytes()
+
+    let series: [Int] = _series4(b2 - b1, b3 - b2, b4 - b3, b5 - b4)
+    let verdict: Int = elb_gate(series, 2, 1000)
+    let measured: Int = elb_measured_curve(series, 1000)
+    println("      bytes: " + int_to_str(b2 - b1) + " " + int_to_str(b3 - b2) + " "
+        + int_to_str(b4 - b3) + " " + int_to_str(b5 - b4)
+        + "  -> " + elb_curve_name(measured) + " [" + elb_verdict_name(verdict) + "]")
+
+    assert verdict == 0 || verdict == 4, "doubling build must be O(n) in bytes"
+    assert str_len(s4) >= 65536, "final string reached the requested size"
+}
+
+// _scan_quadratic — a DELIBERATELY quadratic scan: for each position, rescan
+// from the start. This is precisely what el #132 did — strlen() from offset 0
+// on every character access — reproduced here so the gate can be proven to
+// FIRE, not merely to pass on healthy code. An unproven gate is decoration.
+fn _scan_quadratic(s: String, n: Int) -> Int {
+    let total: Int = 0
+    let i: Int = 0
+    while i < n {
+        let j: Int = 0
+        while j < i {
+            let c: Int = str_char_code(s, j)
+            let total = total + c
+            let j = j + 1
+        }
+        let i = i + 1
+    }
+    return total
+}
+
+fn _measure_quadratic(n: Int) -> Int {
+    let s: String = _mk_string(n)
+    let w: Int = _scan_quadratic(s, 64)
+    let wj: Int = el_black_box(w)
+    let t0: Int = el_now_instant()
+    let got: Int = _scan_quadratic(s, n)
+    let t1: Int = el_now_instant()
+    let sink: Int = el_black_box(got)
+    return (t1 - t0) / 1000
+}
+
+test "the gate FIRES on a live quadratic scan -- proves it is armed" {
+    let q1: Int = _measure_quadratic(1024)
+    let q2: Int = _measure_quadratic(2048)
+    let q3: Int = _measure_quadratic(4096)
+    let q4: Int = _measure_quadratic(8192)
+    let series: [Int] = _series4(q1, q2, q3, q4)
+
+    let verdict: Int = elb_gate(series, 2, 50)
+    let measured: Int = elb_measured_curve(series, 50)
+    println("      quad us: " + int_to_str(q1) + " " + int_to_str(q2) + " "
+        + int_to_str(q3) + " " + int_to_str(q4)
+        + "  -> " + elb_curve_name(measured) + " [" + elb_verdict_name(verdict) + "]")
+
+    assert measured == 4, "a rescan-from-zero workload must classify O(n^2)"
+    assert verdict == 1, "declared O(n) against measured O(n^2) must FAIL the gate"
+}