runtime: let signal enter as geometry, not as prose about signal

No ingest path could carry a vector. engram_node/_full/_layered take text
only, and a node acquired an embedding solely via engram_embed_backfill
DERIVING one from n->content. That made text the mandatory entry medium:
any non-text modality had to be described in prose first, so the geometry
we then reasoned over was the geometry OF THE DESCRIPTION, not of the
signal. Measured: POST /api/nodes accepted an "emb" field, returned 200
with a fresh id, and stored nothing — emb_dim=None, embedded=false.

engram_node_set_emb attaches a vector to an existing node. Off-dimension
vectors are stored but not indexed (the HNSW build loop already filters on
emb_dim), so modality geometry is durable and addressable without
perturbing the canonical index. Setting emb also makes the node ineligible
for embed_backfill, so a realizer's vector is never overwritten by a
text-derived one.

Two reporting fixes ride along, because both are how the drop stayed
invisible: the create response now reports emb_set instead of being
success-shaped regardless, and the node document now always emits emb_dim
and embedded — without which a genuine ingest drop and a mere reporting
gap are indistinguishable.

Verified live: voice node emb_dim=64 embedded=true; text control emb_dim=0
embedded=false; malformed hex, length mismatch and dim<=0 all reject.

KNOWN PLACEMENT DEFECT: this is at the consumer. Ingest is a language
concern, not an engram feature — every el program touching any modality
needs it. The vector also marshals as a hex STRING because el has no
first-class geometry value, which reintroduces text as the transport
medium one layer below the problem being fixed. The durable shape is
geometry as an el value plus declarable realizers, after which the engram
stops having an ingest concept at all. Landing this as the verified probe
that proves the path.

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

@@ -1510,6 +1510,11 @@ fn cg_stmt(stmt: Map<String, Any>, indent: String, declared: [String]) -> [Strin
         if str_eq(ltype, "Int") {
             add_int_name(name)
         }
+        // Same as params: Bool is an int in the value model. Without this a
+        // `let ok: Bool = ...` compared to another Bool lowered to str_eq.
+        if str_eq(ltype, "Bool") {
+            add_int_name(name)
+        }
         if str_eq(ltype, "Float") {
             add_float_name(name)
         }
@@ -2902,6 +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 }
@@ -3127,6 +3133,15 @@ fn build_int_names_for_params(params: [Map<String, Any>]) -> Bool {
         if str_eq(ptype, "Int") {
             add_int_name(pname)
         }
+        // Bool is an integer in the value model (type_to_c maps Bool -> "int";
+        // el_runtime.h: "Bool -> el_val_t (0 = false, nonzero = true)"), but
+        // Bool names were registered nowhere. So `cond == want` between two
+        // Bool params fell through to str_eq and dereferenced 0 or 1 as a
+        // char* — an immediate segfault. Track them as int-like, which is what
+        // they are.
+        if str_eq(ptype, "Bool") {
+            add_int_name(pname)
+        }
         if str_eq(ptype, "Float") {
             add_float_name(pname)
         }

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) */
@@ -18522,6 +18618,26 @@ 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"
+}

lang/tests/runtime/operator_typing_test.el

@@ -0,0 +1,28 @@
+fn getstr(x: String) -> String { return x }
+fn getint(x: Int) -> Int { return x }
+fn ok(label: String) -> Void { println("ok   " + label) }
+fn bad(label: String) -> Void { println("FAIL " + label) }
+
+let s1: String = "hello"
+let s2: String = "hello"
+let s3: String = "world"
+let i1: Int = 5
+let i2: Int = 5
+let i3: Int = 9
+
+if "abc" == "abc" { ok("str literal eq") } else { bad("str literal eq") }
+if "abc" == "xyz" { bad("str literal ne") } else { ok("str literal ne") }
+if s1 == s2 { ok("str var eq") } else { bad("str var eq") }
+if s1 == s3 { bad("str var ne") } else { ok("str var ne") }
+if getstr("hi") == "hi" { ok("str call vs literal") } else { bad("str call vs literal") }
+if s1 == getstr("hello") { ok("str var vs call") } else { bad("str var vs call") }
+if s1 == getstr("nope") { bad("str var vs call ne") } else { ok("str var vs call ne") }
+if i1 == i2 { ok("int var eq") } else { bad("int var eq") }
+if i1 == i3 { bad("int var ne") } else { ok("int var ne") }
+if getint(5) == i1 { ok("int call vs var") } else { bad("int call vs var") }
+if getint(9) == i1 { bad("int call vs var ne") } else { ok("int call vs var ne") }
+if s1 != s3 { ok("str NOTEQ") } else { bad("str NOTEQ") }
+if s1 != s2 { bad("str NOTEQ same") } else { ok("str NOTEQ same") }
+if i1 != i3 { ok("int NOTEQ") } else { bad("int NOTEQ") }
+if getint(9) != i1 { ok("int call NOTEQ") } else { bad("int call NOTEQ") }
+println("done")