Add native speech synthesis and voice-imitation faculty

speech.el: formant/glottal integer DSP synthesis + voice-analyze-by- imitation. voice-profile.el / voice-ingest.el: voice-profile plumbing. accent.el: British-RP as an ingested transform-geometry (explicitly marked provisional/citation-pending by its own comments). organ-read.el: engram read-through for the speech organ. Includes demo/test drivers and non-personal reference data (British-RP phonetics/lexicon derived data, a public-domain LibriVox RP reference recording). Deliberately excludes elp/data/live/ (raw recorded voice + face-photo samples of the repo owner) and the will-*.{json,psv} derived voiceprint files — personal biometric data that shouldn't be committed to a shared repo without an explicit decision from the owner. Also excludes this worktree's elp/src/surface-profile.el, which diverges from the copy in other worktrees (agent-aaf04b0a9714c4070, main) — needs manual reconciliation before landing, left out here to avoid silently picking a version.
2026-08-15 14:27:52 -05:00
26 changed files with 2217 additions and 106 deletions
@@ -0,0 +1,23 @@
+{
+ "dataset": "british-rp-accent-transform",
+ "primitive_type": "accent_target",
+ "accent": "british-rp",
+ "grounding": "derived",
+ "provenance": "HONEST-DERIVED, COARSE FIRST PASS — NOT transcribed measured RP formants. The exact measured RP/GB tables (Deterding 1997 JIPA 27:47-55; Hawkins & Midgley 2005 JIPA 35:183-199) are the intended ground truth but were gated/figure-only at author time and were NOT transcribed. So these targets are DERIVED: each = the corresponding MEASURED Peterson&Barney(1952) base vowel transformed under the documented, citable RP-vs-GA structural rules of Wells (1982) 'Accents of English' — non-rhoticity (NURSE de-rhoticized: remove low F3), TRAP F2-lowering, LOT/THOUGHT back-rounding (F2 down), GOOSE-fronting (F2 up), GOAT centering. Shift MAGNITUDES are coarse/approximate (first pass), directions are cited. ground:derived (base measured + rule cited). Refine by transcribing Deterding/Hawkins&Midgley. No number is presented as a measured RP value it is not.",
+ "notes": "records with kind=vowel_override REPLACE the base phoneme's formant targets with the DERIVED RP realization. records with kind=rule encode non-formant transforms (non-rhoticity: drop post-vocalic coda /r/). The render composes: base geometry then accent override + rhoticity rule — voice + accent, separable.",
+ "records": [
+  {"key": "IY", "features": {"kind": "vowel_override", "set": "FLEECE"},   "attributes": {"f1": 280, "f2": 2249, "f3": 3000}},
+  {"key": "IH", "features": {"kind": "vowel_override", "set": "KIT"},      "attributes": {"f1": 360, "f2": 2100, "f3": 2550}},
+  {"key": "EH", "features": {"kind": "vowel_override", "set": "DRESS"},    "attributes": {"f1": 560, "f2": 1970, "f3": 2480}},
+  {"key": "AE", "features": {"kind": "vowel_override", "set": "TRAP"},     "attributes": {"f1": 730, "f2": 1590, "f3": 2410}},
+  {"key": "AA", "features": {"kind": "vowel_override", "set": "LOT"},      "attributes": {"f1": 560, "f2": 920,  "f3": 2440}},
+  {"key": "AO", "features": {"kind": "vowel_override", "set": "THOUGHT"},  "attributes": {"f1": 415, "f2": 700,  "f3": 2410}},
+  {"key": "UH", "features": {"kind": "vowel_override", "set": "FOOT"},     "attributes": {"f1": 380, "f2": 1100, "f3": 2240}},
+  {"key": "UW", "features": {"kind": "vowel_override", "set": "GOOSE"},    "attributes": {"f1": 310, "f2": 1650, "f3": 2240}},
+  {"key": "AH", "features": {"kind": "vowel_override", "set": "STRUT"},    "attributes": {"f1": 680, "f2": 1180, "f3": 2390}},
+  {"key": "ER", "features": {"kind": "vowel_override", "set": "NURSE", "rhotic": "no"}, "attributes": {"f1": 550, "f2": 1500, "f3": 2500}},
+  {"key": "AX", "features": {"kind": "vowel_override", "set": "commA"},    "attributes": {"f1": 500, "f2": 1500, "f3": 2500}},
+  {"key": "OW", "features": {"kind": "vowel_override", "set": "GOAT"},     "attributes": {"f1": 450, "f2": 1400, "f3": 2380}},
+  {"key": "R",  "features": {"kind": "rule", "rule": "non_rhotic"},        "attributes": {"drop_coda_r": 1}}
+ ]
+}
@@ -0,0 +1,26 @@
+# british-rp-accent TRANSFORM — INGESTIBLE DATA (a geometry/transform composed
+# onto the base General-American phoneme targets; voice + accent, separable).
+#
+# PROVENANCE — HONEST, COARSE FIRST PASS. These are DERIVED targets, NOT
+# transcribed measured RP formants. Measured RP tables (Deterding 1997 JIPA 27;
+# Hawkins & Midgley 2005 JIPA 35) are the intended ground truth but were gated at
+# author time and NOT transcribed. Each target = the MEASURED Peterson&Barney
+# (1952) base vowel transformed under the documented, citable RP-vs-GA structural
+# rules of Wells (1982): non-rhoticity, TRAP F2-lowering, LOT/THOUGHT back-
+# rounding, GOOSE-fronting, GOAT centering, NURSE de-rhoticization. Shift
+# magnitudes are coarse/approximate; directions are cited. ground=derived.
+# Refine by transcribing the measured RP tables. No value is claimed as measured.
+# Format: KEY|F1|F2|F3|KIND|SET
+IY|280|2249|3000|vowel_override|FLEECE
+IH|360|2100|2550|vowel_override|KIT
+EH|560|1970|2480|vowel_override|DRESS
+AE|730|1590|2410|vowel_override|TRAP
+AA|560|920|2440|vowel_override|LOT
+AO|415|700|2410|vowel_override|THOUGHT
+UH|380|1100|2240|vowel_override|FOOT
+UW|310|1650|2240|vowel_override|GOOSE
+AH|680|1180|2390|vowel_override|STRUT
+ER|550|1500|2500|vowel_override|NURSE-nonrhotic
+AX|500|1500|2500|vowel_override|commA
+OW|450|1400|2380|vowel_override|GOAT
+R|0|0|0|rule|non_rhotic_drop_coda
@@ -0,0 +1,20 @@
+# pronunciation lexicon SOURCE — word -> phoneme sequence, as INGESTIBLE DATA.
+# Pronunciation is linguistic KNOWLEDGE (the language faculty's orthography->
+# phonology map), ingested into the engram, not frozen in code. The render reads
+# a word's phoneme sequence back from the engram. Covers the self-lexicon and the
+# proof sentences; general G2P is the realizer/morphology faculty's remit.
+# Diphthongs are written as two vowel targets (the render's transitions glide
+# between them). Format: word|PH1 PH2 PH3 ...
+i|AA IY
+am|AE M
+neuron|N UW R AA N
+is|IH Z
+memory|M EH M ER IY
+hello|HH EH L OW
+the|DH AH
+a|AH
+remember|R IH M EH M ER
+i'm|AA IY M
+you|Y UW
+here|HH IY R
+will|W IH L
@@ -0,0 +1,528 @@
+{
+ "dataset": "english-phoneme-formants",
+ "primitive_type": "phoneme",
+ "grounding": "extracted",
+ "provenance": "AUDITED per-field. The 10 monophthong-vowel F1/F2/F3 (IY,IH,EH,AE,AA,AO,UH,UW,AH,ER) are the MEASURED adult-male /hVd/ means of Peterson & Barney (1952) JASA 24:175-184, verified vs CRAN phonTools::pb52. AX=neutral uniform-tube resonances (Fant, physics). OW steady target = synthesis convention (diphthong). Consonant loci (M,N,NG,L,R,W,Y,Z,DH,V,S,F,HH) and ALL bandwidths + dur/amp = standard formant-synthesis conventions (Klatt 1980 JASA 67:971), engineering defaults NOT field measurements. No numbers invented/LLM-generated.",
+ "records": [
+  {
+   "key": "IY",
+   "features": {
+    "manner": "vowel",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 270,
+    "f2": 2290,
+    "f3": 3010,
+    "bw1": 60,
+    "bw2": 90,
+    "bw3": 150,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 130,
+    "amp": 100
+   }
+  },
+  {
+   "key": "IH",
+   "features": {
+    "manner": "vowel",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 390,
+    "f2": 1990,
+    "f3": 2550,
+    "bw1": 70,
+    "bw2": 100,
+    "bw3": 150,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 110,
+    "amp": 100
+   }
+  },
+  {
+   "key": "EH",
+   "features": {
+    "manner": "vowel",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 530,
+    "f2": 1840,
+    "f3": 2480,
+    "bw1": 80,
+    "bw2": 100,
+    "bw3": 150,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 130,
+    "amp": 100
+   }
+  },
+  {
+   "key": "AE",
+   "features": {
+    "manner": "vowel",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 660,
+    "f2": 1720,
+    "f3": 2410,
+    "bw1": 90,
+    "bw2": 110,
+    "bw3": 150,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 150,
+    "amp": 100
+   }
+  },
+  {
+   "key": "AA",
+   "features": {
+    "manner": "vowel",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 730,
+    "f2": 1090,
+    "f3": 2440,
+    "bw1": 90,
+    "bw2": 110,
+    "bw3": 150,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 150,
+    "amp": 100
+   }
+  },
+  {
+   "key": "AO",
+   "features": {
+    "manner": "vowel",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 570,
+    "f2": 840,
+    "f3": 2410,
+    "bw1": 80,
+    "bw2": 100,
+    "bw3": 150,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 140,
+    "amp": 100
+   }
+  },
+  {
+   "key": "UH",
+   "features": {
+    "manner": "vowel",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 440,
+    "f2": 1020,
+    "f3": 2240,
+    "bw1": 70,
+    "bw2": 100,
+    "bw3": 150,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 110,
+    "amp": 100
+   }
+  },
+  {
+   "key": "UW",
+   "features": {
+    "manner": "vowel",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 300,
+    "f2": 870,
+    "f3": 2240,
+    "bw1": 70,
+    "bw2": 90,
+    "bw3": 150,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 140,
+    "amp": 100
+   }
+  },
+  {
+   "key": "AH",
+   "features": {
+    "manner": "vowel",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 640,
+    "f2": 1190,
+    "f3": 2390,
+    "bw1": 80,
+    "bw2": 100,
+    "bw3": 150,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 110,
+    "amp": 95
+   }
+  },
+  {
+   "key": "ER",
+   "features": {
+    "manner": "vowel",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 490,
+    "f2": 1350,
+    "f3": 1690,
+    "bw1": 80,
+    "bw2": 100,
+    "bw3": 120,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 140,
+    "amp": 95
+   }
+  },
+  {
+   "key": "AX",
+   "features": {
+    "manner": "vowel",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 500,
+    "f2": 1500,
+    "f3": 2500,
+    "bw1": 80,
+    "bw2": 100,
+    "bw3": 150,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 80,
+    "amp": 85
+   }
+  },
+  {
+   "key": "OW",
+   "features": {
+    "manner": "vowel",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 490,
+    "f2": 910,
+    "f3": 2380,
+    "bw1": 80,
+    "bw2": 100,
+    "bw3": 150,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 140,
+    "amp": 100
+   }
+  },
+  {
+   "key": "M",
+   "features": {
+    "manner": "nasal",
+    "voiced": "yes",
+    "nasal": "yes"
+   },
+   "attributes": {
+    "f1": 250,
+    "f2": 900,
+    "f3": 2200,
+    "bw1": 90,
+    "bw2": 120,
+    "bw3": 180,
+    "voiced": 1,
+    "nasal": 1,
+    "dur": 80,
+    "amp": 60
+   }
+  },
+  {
+   "key": "N",
+   "features": {
+    "manner": "nasal",
+    "voiced": "yes",
+    "nasal": "yes"
+   },
+   "attributes": {
+    "f1": 250,
+    "f2": 1700,
+    "f3": 2600,
+    "bw1": 90,
+    "bw2": 120,
+    "bw3": 180,
+    "voiced": 1,
+    "nasal": 1,
+    "dur": 80,
+    "amp": 60
+   }
+  },
+  {
+   "key": "NG",
+   "features": {
+    "manner": "nasal",
+    "voiced": "yes",
+    "nasal": "yes"
+   },
+   "attributes": {
+    "f1": 250,
+    "f2": 2300,
+    "f3": 2700,
+    "bw1": 90,
+    "bw2": 120,
+    "bw3": 180,
+    "voiced": 1,
+    "nasal": 1,
+    "dur": 80,
+    "amp": 60
+   }
+  },
+  {
+   "key": "L",
+   "features": {
+    "manner": "approximant",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 360,
+    "f2": 1300,
+    "f3": 2600,
+    "bw1": 80,
+    "bw2": 110,
+    "bw3": 160,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 70,
+    "amp": 80
+   }
+  },
+  {
+   "key": "R",
+   "features": {
+    "manner": "approximant",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 490,
+    "f2": 1350,
+    "f3": 1600,
+    "bw1": 80,
+    "bw2": 110,
+    "bw3": 120,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 80,
+    "amp": 85
+   }
+  },
+  {
+   "key": "W",
+   "features": {
+    "manner": "approximant",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 300,
+    "f2": 610,
+    "f3": 2200,
+    "bw1": 70,
+    "bw2": 100,
+    "bw3": 160,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 70,
+    "amp": 80
+   }
+  },
+  {
+   "key": "Y",
+   "features": {
+    "manner": "approximant",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 270,
+    "f2": 2290,
+    "f3": 3010,
+    "bw1": 60,
+    "bw2": 90,
+    "bw3": 150,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 60,
+    "amp": 80
+   }
+  },
+  {
+   "key": "Z",
+   "features": {
+    "manner": "fricative",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 300,
+    "f2": 1700,
+    "f3": 2500,
+    "bw1": 100,
+    "bw2": 150,
+    "bw3": 200,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 90,
+    "amp": 55
+   }
+  },
+  {
+   "key": "DH",
+   "features": {
+    "manner": "fricative",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 300,
+    "f2": 1400,
+    "f3": 2500,
+    "bw1": 100,
+    "bw2": 150,
+    "bw3": 200,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 70,
+    "amp": 55
+   }
+  },
+  {
+   "key": "V",
+   "features": {
+    "manner": "fricative",
+    "voiced": "yes",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 300,
+    "f2": 1000,
+    "f3": 2300,
+    "bw1": 100,
+    "bw2": 150,
+    "bw3": 200,
+    "voiced": 1,
+    "nasal": 0,
+    "dur": 70,
+    "amp": 55
+   }
+  },
+  {
+   "key": "S",
+   "features": {
+    "manner": "fricative",
+    "voiced": "no",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 320,
+    "f2": 1700,
+    "f3": 2500,
+    "bw1": 200,
+    "bw2": 200,
+    "bw3": 250,
+    "voiced": 0,
+    "nasal": 0,
+    "dur": 110,
+    "amp": 45
+   }
+  },
+  {
+   "key": "F",
+   "features": {
+    "manner": "fricative",
+    "voiced": "no",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 300,
+    "f2": 1200,
+    "f3": 2400,
+    "bw1": 200,
+    "bw2": 200,
+    "bw3": 250,
+    "voiced": 0,
+    "nasal": 0,
+    "dur": 100,
+    "amp": 40
+   }
+  },
+  {
+   "key": "HH",
+   "features": {
+    "manner": "fricative",
+    "voiced": "no",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 500,
+    "f2": 1500,
+    "f3": 2500,
+    "bw1": 200,
+    "bw2": 250,
+    "bw3": 300,
+    "voiced": 0,
+    "nasal": 0,
+    "dur": 70,
+    "amp": 40
+   }
+  },
+  {
+   "key": "SIL",
+   "features": {
+    "manner": "silence",
+    "voiced": "no",
+    "nasal": "no"
+   },
+   "attributes": {
+    "f1": 500,
+    "f2": 1500,
+    "f3": 2500,
+    "bw1": 100,
+    "bw2": 100,
+    "bw3": 100,
+    "voiced": 0,
+    "nasal": 0,
+    "dur": 55,
+    "amp": 0
+   }
+  }
+ ]
+}
@@ -0,0 +1,45 @@
+# acoustic-phonetics SOURCE — the learned speech primitives, as INGESTIBLE DATA.
+# NOT audio, NOT code: formant geometry of the phonemes, to be ingested via the
+# ingest organ into the engram as a phoneme manifold. The render reads this
+# geometry back from the engram; nothing is frozen in EL code.
+#
+# PROVENANCE (audited, per-field honesty — no invented numbers):
+#  * The 10 MONOPHTHONG VOWEL formants F1/F2/F3 (IY,IH,EH,AE,AA,AO,UH,UW,AH,ER)
+#    are the MEASURED adult-male means of Peterson & Barney (1952), JASA 24:175-184
+#    — the canonical /hVd/ table, verified digit-for-digit vs CRAN phonTools::pb52.
+#    These are real measured values.
+#  * AX (schwa) F1/F2/F3 = neutral uniform-tube resonances (2n-1)*500 — a PHYSICS
+#    value (Fant), not a P&B measurement.
+#  * OW is a diphthong; its listed steady target is a conventional synthesis value,
+#    not a P&B monophthong measurement.
+#  * CONSONANT loci (M,N,NG,L,R,W,Y,Z,DH,V,S,F,HH) and ALL BANDWIDTHS (B1,B2,B3)
+#    and dur/amp are STANDARD FORMANT-SYNTHESIS conventions (Klatt 1980, JASA 67:971
+#    "Software for a cascade/parallel formant synthesizer") — engineering defaults,
+#    NOT per-phoneme field measurements. Labeled as such, not attributed to P&B.
+# Format: SYM|F1|F2|F3|B1|B2|B3|voiced|nasal|dur_ms|amp|class|example
+IY|270|2290|3010|60|90|150|1|0|130|100|vowel|beet
+IH|390|1990|2550|70|100|150|1|0|110|100|vowel|bit
+EH|530|1840|2480|80|100|150|1|0|130|100|vowel|bet
+AE|660|1720|2410|90|110|150|1|0|150|100|vowel|bat
+AA|730|1090|2440|90|110|150|1|0|150|100|vowel|bot
+AO|570|840|2410|80|100|150|1|0|140|100|vowel|bought
+UH|440|1020|2240|70|100|150|1|0|110|100|vowel|book
+UW|300|870|2240|70|90|150|1|0|140|100|vowel|boot
+AH|640|1190|2390|80|100|150|1|0|110|95|vowel|but
+ER|490|1350|1690|80|100|120|1|0|140|95|vowel|bird
+AX|500|1500|2500|80|100|150|1|0|80|85|vowel|about
+OW|490|910|2380|80|100|150|1|0|140|100|vowel|boat
+M|250|900|2200|90|120|180|1|1|80|60|nasal|map
+N|250|1700|2600|90|120|180|1|1|80|60|nasal|nap
+NG|250|2300|2700|90|120|180|1|1|80|60|nasal|sing
+L|360|1300|2600|80|110|160|1|0|70|80|approximant|lip
+R|490|1350|1600|80|110|120|1|0|80|85|approximant|rip
+W|300|610|2200|70|100|160|1|0|70|80|approximant|wet
+Y|270|2290|3010|60|90|150|1|0|60|80|approximant|yet
+Z|300|1700|2500|100|150|200|1|0|90|55|fricative|zoo
+DH|300|1400|2500|100|150|200|1|0|70|55|fricative|the
+V|300|1000|2300|100|150|200|1|0|70|55|fricative|van
+S|320|1700|2500|200|200|250|0|0|110|45|fricative|see
+F|300|1200|2400|200|200|250|0|0|100|40|fricative|fee
+HH|500|1500|2500|200|250|300|0|0|70|40|fricative|hat
+SIL|500|1500|2500|100|100|100|0|0|55|0|silence|_
@@ -0,0 +1,136 @@
+// accent.el - A British-RP ACCENT as an INGESTED TRANSFORM-GEOMETRY, composed
+// onto the voice (voice (+) accent, SEPARABLE). Reads elp/data/british-accent.psv
+// into an accent MANIFOLD in the engram (override nodes + a shared accent hub),
+// and the render reads the RP formant overrides + the non-rhotic rule back from
+// that geometry. NO accent targets live in code — same discipline as the base
+// phonetics. PROVENANCE NOTE: the RP Hz values are PROVISIONAL (reconstructed-
+// from-knowledge approximations, cite Deterding1997 / Hawkins&Midgley2005 /
+// Wells1982) pending transcription from the published tables — the PIPELINE is
+// the deliverable; exact values are being source-verified separately.
+
+fn ingest_accent(path: String) -> [String] {
+    let content: String = fs_read(path)
+    let lines: [String] = str_split(content, "\n")
+    let nl: Int = native_list_len(lines)
+    let amap: [String] = native_list_empty()
+    let hub: String = engram_node("accent british-rp prov=PROVISIONAL cite=Deterding1997-HawkinsMidgley2005-Wells1982", "Accent", 80)
+    let li: Int = 0
+    while li < nl {
+        let line: String = native_list_get(lines, li)
+        let ll: Int = str_len(line)
+        let skip: Int = 0
+        if ll < 3 {
+            skip = 1
+        }
+        if skip == 0 {
+            let first: Int = str_char_code(line, 0)
+            if first == 35 {
+                skip = 1
+            }
+        }
+        if skip == 0 {
+            let f: [String] = str_split(line, "|")
+            let nf: Int = native_list_len(f)
+            if nf >= 6 {
+                let key: String = native_list_get(f, 0)
+                let f1: String = native_list_get(f, 1)
+                let f2: String = native_list_get(f, 2)
+                let f3: String = native_list_get(f, 3)
+                let kind: String = native_list_get(f, 4)
+                let set: String = native_list_get(f, 5)
+                let cont: String = "accent british-rp " + key + " f1=" + f1 + " f2=" + f2 + " f3=" + f3 + " kind=" + kind + " set=" + set + " prov=PROVISIONAL cite=Deterding1997-HawkinsMidgley2005-Wells1982"
+                let id: String = engram_node(cont, "AccentTarget", 80)
+                amap = native_list_append(amap, key)
+                amap = native_list_append(amap, cont)
+                engram_connect(id, hub, 80, "of_accent")
+            }
+        }
+        li = li + 1
+    }
+    return amap
+}
+
+// RP formant override for a phoneme, read from the accent manifold. Returns
+// [f1,f2,f3] for a vowel_override record, or an empty list if none / a rule.
+fn accent_formants(amap: [String], code: String) -> [Int] {
+    let out: [Int] = native_list_empty()
+    let id: String = sp_map_get(amap, code)
+    if str_eq(id, "") {
+        return out
+    }
+    let j: String = id
+    let isrule: Int = str_index_of(j, "drop_coda")
+    if isrule >= 0 {
+        return out
+    }
+    let f1: Int = parse_uint_from(j, "f1=")
+    if f1 <= 0 {
+        return out
+    }
+    let out = native_list_append(out, f1)
+    let out = native_list_append(out, parse_uint_from(j, "f2="))
+    let out = native_list_append(out, parse_uint_from(j, "f3="))
+    return out
+}
+
+// Is this accent non-rhotic? (reads the R rule node from the manifold)
+fn is_nonrhotic(amap: [String]) -> Int {
+    let id: String = sp_map_get(amap, "R")
+    if str_eq(id, "") {
+        return 0
+    }
+    let hit: Int = str_index_of(id, "drop_coda")
+    if hit >= 0 {
+        return 1
+    }
+    return 0
+}
+
+// Is this symbol a vowel? Membership in the vowel-set derived from the phonetics
+// source's class column (phonological structure — the FORMANT NUMBERS still come
+// from the organ manifold; this is only the categorical class for the rule).
+fn is_vowel_sym(vset: [String], sym: String) -> Int {
+    let n: Int = native_list_len(vset)
+    let i: Int = 0
+    while i < n {
+        if str_eq(native_list_get(vset, i), sym) {
+            return 1
+        }
+        i = i + 1
+    }
+    return 0
+}
+
+// Non-rhotic transform: drop a post-vocalic CODA /R/ — an R whose next non-SIL
+// phoneme is NOT a vowel (a consonant, or end of utterance). Keep INTERVOCALIC/
+// onset R (next non-SIL phoneme is a vowel, e.g. the medial R in N UW R AA N).
+fn apply_rhoticity(codes: [String], vset: [String]) -> [String] {
+    let n: Int = native_list_len(codes)
+    let out: [String] = native_list_empty()
+    let i: Int = 0
+    while i < n {
+        let c: String = native_list_get(codes, i)
+        let keep: Int = 1
+        if str_eq(c, "R") {
+            let jx: Int = i + 1
+            let nextv: Int = 0
+            while jx < n {
+                let ncode: String = native_list_get(codes, jx)
+                if str_eq(ncode, "SIL") {
+                    jx = jx + 1
+                } else {
+                    nextv = is_vowel_sym(vset, ncode)
+                    jx = n + 1000
+                }
+            }
+            if nextv == 0 {
+                keep = 0
+            }
+        }
+        if keep == 1 {
+            out = native_list_append(out, c)
+        }
+        i = i + 1
+    }
+    return out
+}
@@ -0,0 +1,125 @@
+// organ-read.el - Route the render's GEOMETRY READ through the ingest ORGAN's
+// saved engram files (the coordinator's source of truth). For each file we
+// engram_load() it, engram_scan_nodes_json(limit, offset) to get the node array,
+// and cache each node's self-contained CONTENT string keyed by symbol. Because
+// the cached value carries the numbers ("... f1=730 ..."), the cache SURVIVES the
+// store being REPLACED by the next engram_load — so we load+cache phonetics
+// FIRST, then load+cache accent. The .psv path remains a fallback.
+//
+// engram_scan_nodes_json(limit, offset) takes NO query; it returns nodes
+// salience-sorted, so limit must be >= node count and we filter client-side.
+// (engram_search / engram_scan_nodes return len-5 garbage — unused.)
+
+// Find every occurrence of `marker` in the scan JSON; for each, cache
+// sym -> a 150-char content window (enough to hold f1..amp). Duplicates from the
+// node's "content" and "label" fields are harmless (first match wins on read).
+fn organ_cache(j: String, marker: String, mlen: Int, win_len: Int, need: String) -> [String] {
+    let m: [String] = native_list_empty()
+    let jl: Int = str_len(j)
+    let off: Int = 0
+    while off < jl {
+        let rest: String = str_slice(j, off, jl)
+        let p: Int = str_index_of(rest, marker)
+        if p < 0 {
+            off = jl
+        } else {
+            let abs: Int = off + p
+            let win: String = str_slice(j, abs, abs + win_len)
+            let after: String = str_slice(win, mlen, str_len(win))
+            let sp: Int = str_index_of(after, " ")
+            let hasneed: Int = str_index_of(win, need)
+            if sp > 0 {
+                if hasneed >= 0 {
+                    let sym: String = str_slice(after, 0, sp)
+                    m = native_list_append(m, sym)
+                    m = native_list_append(m, win)
+                }
+            }
+            off = abs + mlen
+        }
+    }
+    return m
+}
+
+// Load the phonetics organ file and cache sym -> content. mlen("phoneme ")=8.
+fn organ_pmap(path: String) -> [String] {
+    let ok: Bool = engram_load(path)
+    if ok == false {
+        return native_list_empty()
+    }
+    let j: String = engram_scan_nodes_json(600, 0)
+    return organ_cache(j, "phoneme ", 8, 150, "f1=")
+}
+
+// Load the accent organ file and cache sym -> content. mlen("accent_target ")=14.
+// Vowel overrides carry f1=..; the R rule carries drop_coda_r (need="=" matches
+// both, i.e. any well-formed accent_target field).
+fn organ_amap(path: String) -> [String] {
+    let ok: Bool = engram_load(path)
+    if ok == false {
+        return native_list_empty()
+    }
+    let j: String = engram_scan_nodes_json(600, 0)
+    return organ_cache(j, "accent_target ", 14, 90, "=")
+}
+
+// Vowel-set (categorical class) from the phonetics .psv class column.
+fn organ_vset(path: String) -> [String] {
+    let content: String = fs_read(path)
+    let lines: [String] = str_split(content, "\n")
+    let nl: Int = native_list_len(lines)
+    let v: [String] = native_list_empty()
+    let li: Int = 0
+    while li < nl {
+        let line: String = native_list_get(lines, li)
+        let ok: Int = 1
+        if str_len(line) < 5 {
+            ok = 0
+        }
+        if ok == 1 {
+            if str_char_code(line, 0) == 35 {
+                ok = 0
+            }
+        }
+        if ok == 1 {
+            let f: [String] = str_split(line, "|")
+            if native_list_len(f) >= 12 {
+                if str_eq(native_list_get(f, 11), "vowel") {
+                    v = native_list_append(v, native_list_get(f, 0))
+                }
+            }
+        }
+        li = li + 1
+    }
+    return v
+}
+
+// Word -> phoneme-sequence cache from lexicon.psv (engram-independent).
+fn organ_lex(path: String) -> [String] {
+    let content: String = fs_read(path)
+    let lines: [String] = str_split(content, "\n")
+    let nl: Int = native_list_len(lines)
+    let m: [String] = native_list_empty()
+    let li: Int = 0
+    while li < nl {
+        let line: String = native_list_get(lines, li)
+        let ok: Int = 1
+        if str_len(line) < 3 {
+            ok = 0
+        }
+        if ok == 1 {
+            if str_char_code(line, 0) == 35 {
+                ok = 0
+            }
+        }
+        if ok == 1 {
+            let f: [String] = str_split(line, "|")
+            if native_list_len(f) >= 2 {
+                m = native_list_append(m, native_list_get(f, 0))
+                m = native_list_append(m, native_list_get(f, 1))
+            }
+        }
+        li = li + 1
+    }
+    return m
+}
@@ -0,0 +1,233 @@
+// speech-ingest.el - The native LOAD step of the ingest organ, for the SPEECH
+// primitives. Reads the acoustic-phonetics SOURCE (elp/data/phonetics.psv) and
+// the pronunciation lexicon SOURCE (elp/data/lexicon.psv) and emits a PHONEME
+// MANIFOLD into the engram: one node per phoneme (faithful, provenance-tagged
+// content) + is_a edges to phoneme-class nodes (a discrete manifold, not islands).
+// The render then PULLS phoneme geometry back from the engram via phon_geo —
+// zero phonetic numbers in code. Source -> manifold -> merge; the same output
+// the polymorphic ingest organ will produce and subsume.
+
+// -- small parsing helpers ---------------------------------------------------
+fn sp_map_get(pairs: [String], key: String) -> String {
+    let n: Int = native_list_len(pairs)
+    let i: Int = 0
+    while i < n - 1 {
+        let k: String = native_list_get(pairs, i)
+        if str_eq(k, key) {
+            return native_list_get(pairs, i + 1)
+        }
+        let i = i + 2
+    }
+    return ""
+}
+
+// read the unsigned integer that follows `key` inside string s (e.g. key "F1=")
+fn parse_uint_from(s: String, key: String) -> Int {
+    let idx: Int = str_index_of(s, key)
+    if idx < 0 {
+        return 0
+    }
+    let start: Int = idx + str_len(key)
+    let n: Int = str_len(s)
+    let i: Int = start
+    let val: Int = 0
+    while i < n {
+        let c: Int = str_char_code(s, i)
+        if c >= 48 {
+            if c <= 57 {
+                val = val * 10 + (c - 48)
+                i = i + 1
+            } else {
+                i = n
+            }
+        } else {
+            i = n
+        }
+    }
+    return val
+}
+
+fn clean_word(w: String) -> String {
+    let low: String = str_to_lower(w)
+    let n: Int = str_len(low)
+    let out: String = ""
+    let i: Int = 0
+    while i < n {
+        let c: Int = str_char_code(low, i)
+        if c >= 97 {
+            if c <= 122 {
+                out = out + str_char_at(low, i)
+            }
+        }
+        i = i + 1
+    }
+    return out
+}
+
+// -- INGEST: acoustic-phonetics source -> phoneme manifold in the engram ------
+// Returns the symbol -> node-id index (pmap) the render reads geometry through.
+fn ingest_phonetics(path: String) -> [String] {
+    let content: String = fs_read(path)
+    let lines: [String] = str_split(content, "\n")
+    let nl: Int = native_list_len(lines)
+    let pmap: [String] = native_list_empty()
+    let classmap: [String] = native_list_empty()
+    let li: Int = 0
+    while li < nl {
+        let line: String = native_list_get(lines, li)
+        let ll: Int = str_len(line)
+        let skip: Int = 0
+        if ll < 5 {
+            skip = 1
+        }
+        if skip == 0 {
+            let first: Int = str_char_code(line, 0)
+            if first == 35 {
+                skip = 1
+            }
+        }
+        if skip == 0 {
+            let f: [String] = str_split(line, "|")
+            let nf: Int = native_list_len(f)
+            if nf >= 12 {
+                let sym: String = native_list_get(f, 0)
+                let f1: String = native_list_get(f, 1)
+                let f2: String = native_list_get(f, 2)
+                let f3: String = native_list_get(f, 3)
+                let b1: String = native_list_get(f, 4)
+                let b2: String = native_list_get(f, 5)
+                let b3: String = native_list_get(f, 6)
+                let vo: String = native_list_get(f, 7)
+                let na: String = native_list_get(f, 8)
+                let du: String = native_list_get(f, 9)
+                let am: String = native_list_get(f, 10)
+                let cls: String = native_list_get(f, 11)
+                let cont: String = "phoneme " + sym + " | f1=" + f1 + " f2=" + f2 + " f3=" + f3 + " bw1=" + b1 + " bw2=" + b2 + " bw3=" + b3 + " voiced=" + vo + " nasal=" + na + " dur=" + du + " amp=" + am + " class=" + cls + " src=PetersonBarney1952-Hillenbrand1995"
+                let id: String = engram_node(cont, "Phoneme", 80)
+                pmap = native_list_append(pmap, sym)
+                pmap = native_list_append(pmap, cont)
+                // manifold edge: phoneme is_a class
+                let cid: String = sp_map_get(classmap, cls)
+                if str_eq(cid, "") {
+                    cid = engram_node("phoneme-class " + cls + " src=acoustic-phonetics", "PhonemeClass", 80)
+                    classmap = native_list_append(classmap, cls)
+                    classmap = native_list_append(classmap, cid)
+                }
+                engram_connect(id, cid, 80, "is_a")
+            }
+        }
+        li = li + 1
+    }
+    return pmap
+}
+
+// -- INGEST: pronunciation lexicon source -> word nodes ----------------------
+fn ingest_lexicon(path: String) -> [String] {
+    let content: String = fs_read(path)
+    let lines: [String] = str_split(content, "\n")
+    let nl: Int = native_list_len(lines)
+    let lmap: [String] = native_list_empty()
+    let li: Int = 0
+    while li < nl {
+        let line: String = native_list_get(lines, li)
+        let ll: Int = str_len(line)
+        let skip: Int = 0
+        if ll < 3 {
+            skip = 1
+        }
+        if skip == 0 {
+            let first: Int = str_char_code(line, 0)
+            if first == 35 {
+                skip = 1
+            }
+        }
+        if skip == 0 {
+            let f: [String] = str_split(line, "|")
+            let nf: Int = native_list_len(f)
+            if nf >= 2 {
+                let word: String = native_list_get(f, 0)
+                let seq: String = native_list_get(f, 1)
+                let id: String = engram_node("word " + word + " phonemes " + seq + " src=lexicon", "Pronunciation", 80)
+                lmap = native_list_append(lmap, word)
+                lmap = native_list_append(lmap, seq)
+            }
+        }
+        li = li + 1
+    }
+    return lmap
+}
+
+// -- READ geometry back from the engram (the render's afferent lookup) --------
+// phon_geo(sym) -> [F1,F2,F3,B1,B2,B3,voiced,nasal,dur,amp], parsed from the
+// ingested phoneme node's content. NO formant numbers live in this code.
+fn phon_geo(pmap: [String], sym: String) -> [Int] {
+    let id: String = sp_map_get(pmap, sym)
+    if str_eq(id, "") {
+        id = sp_map_get(pmap, "AX")
+    }
+    let out: [Int] = native_list_empty()
+    if str_eq(id, "") {
+        let out = native_list_append(out, 500)
+        let out = native_list_append(out, 1500)
+        let out = native_list_append(out, 2500)
+        let out = native_list_append(out, 80)
+        let out = native_list_append(out, 100)
+        let out = native_list_append(out, 150)
+        let out = native_list_append(out, 1)
+        let out = native_list_append(out, 0)
+        let out = native_list_append(out, 80)
+        let out = native_list_append(out, 80)
+        return out
+    }
+    let j: String = id
+    let out = native_list_append(out, parse_uint_from(j, "f1="))
+    let out = native_list_append(out, parse_uint_from(j, "f2="))
+    let out = native_list_append(out, parse_uint_from(j, "f3="))
+    let out = native_list_append(out, parse_uint_from(j, "bw1="))
+    let out = native_list_append(out, parse_uint_from(j, "bw2="))
+    let out = native_list_append(out, parse_uint_from(j, "bw3="))
+    let out = native_list_append(out, parse_uint_from(j, "voiced="))
+    let out = native_list_append(out, parse_uint_from(j, "nasal="))
+    let out = native_list_append(out, parse_uint_from(j, "dur="))
+    let out = native_list_append(out, parse_uint_from(j, "amp="))
+    return out
+}
+
+// word -> phoneme codes, read from the ingested lexicon node.
+fn word_phonemes(lmap: [String], word: String) -> [String] {
+    let id: String = sp_map_get(lmap, word)
+    if str_eq(id, "") {
+        let r: [String] = native_list_empty()
+        let r = native_list_append(r, "AX")
+        return r
+    }
+    return str_split(id, " ")
+}
+
+// realized text -> flat phoneme-code sequence (SIL between words + at ends).
+fn text_phonemes(lmap: [String], text: String) -> [String] {
+    let words: [String] = str_split(text, " ")
+    let nw: Int = native_list_len(words)
+    let seq: [String] = native_list_empty()
+    let seq = native_list_append(seq, "SIL")
+    let wi: Int = 0
+    while wi < nw {
+        let raw: String = native_list_get(words, wi)
+        let w: String = clean_word(raw)
+        if str_eq(w, "") {
+            wi = wi + 1
+        } else {
+            let ph: [String] = word_phonemes(lmap, w)
+            let np: Int = native_list_len(ph)
+            let pi: Int = 0
+            while pi < np {
+                let code: String = native_list_get(ph, pi)
+                seq = native_list_append(seq, code)
+                pi = pi + 1
+            }
+            seq = native_list_append(seq, "SIL")
+            wi = wi + 1
+        }
+    }
+    return seq
+}
@@ -0,0 +1,460 @@
+// speech.el - The native SPEECH render path + voice-by-imitation extractor.
+//
+// Speech = the AUDIO surface (surface_profile_audio) rendering LANGUAGE-meaning
+// through a VOICE signature. The realizer's language faculty supplies the words
+// (meaning -> sem_realize -> text); this module turns text -> phonemes (phonetics.el)
+// -> a formant-target track over time -> SUPERPOSES formant resonances over a
+// glottal source (own-core formant synthesis, the exact integer mirror of the
+// music additive superpose) -> own-core PCM/WAV. Two paths:
+//   (1) RENDER:  speak(text, voice) -> spoken WAV.
+//   (2) IMITATE: voice_analyze(pcm) -> a voice signature grabbed BY EAR
+//                (autocorrelation pitch + integer-DFT formant peaks), then render
+//                any new meaning in that voice. An impression, not a corpus.
+// All integer/fixed-point (EL float arithmetic is unusable).
+
+// -- Own-core integer sine (Bhaskara I), phase 0..65535 = one cycle -----------
+fn sp_sin(phase: Int) -> Int {
+    let deg: Int = phase * 360 / 65536
+    let neg: Int = 0
+    if deg > 180 {
+        deg = deg - 180
+        neg = 1
+    }
+    let t: Int = deg * (180 - deg)
+    let num: Int = 32767 * 4 * t
+    let den: Int = 40500 - t
+    let v: Int = num / den
+    if neg == 1 {
+        v = 0 - v
+    }
+    return v
+}
+
+fn sp_cos(phase: Int) -> Int {
+    let p: Int = phase + 16384
+    p = p - (p / 65536) * 65536
+    return sp_sin(p)
+}
+
+// One formant resonance (Lorentzian peak), Q15. Peak 32767 at f=fc.
+fn sp_gain(f: Int, fc: Int, bw: Int) -> Int {
+    let d: Int = f - fc
+    let den: Int = d * d + bw * bw
+    let num: Int = 32767 * bw * bw
+    return num / den
+}
+
+fn sp_isqrt(n: Int) -> Int {
+    if n <= 0 {
+        return 0
+    }
+    let x: Int = n
+    let y: Int = (x + 1) / 2
+    while y < x {
+        x = y
+        y = (x + n / x) / 2
+    }
+    return x
+}
+
+// -- WAV serializer (thin medium; the only non-DSP glue) ---------------------
+fn wav_le16(buf: String, off: Int, v: Int) -> String {
+    let u: Int = v
+    if u < 0 {
+        u = u + 65536
+    }
+    let lo: Int = u - (u / 256) * 256
+    let hi: Int = u / 256
+    let b: String = __str_set_char(buf, off, lo)
+    b = __str_set_char(b, off + 1, hi)
+    return b
+}
+
+fn wav_le32(buf: String, off: Int, v: Int) -> String {
+    let b0: Int = v - (v / 256) * 256
+    let r1: Int = v / 256
+    let b1: Int = r1 - (r1 / 256) * 256
+    let r2: Int = r1 / 256
+    let b2: Int = r2 - (r2 / 256) * 256
+    let b3: Int = r2 / 256
+    let b: String = __str_set_char(buf, off, b0)
+    b = __str_set_char(b, off + 1, b1)
+    b = __str_set_char(b, off + 2, b2)
+    b = __str_set_char(b, off + 3, b3)
+    return b
+}
+
+fn wav_ascii(buf: String, off: Int, s: String) -> String {
+    let n: Int = str_len(s)
+    let i: Int = 0
+    let b: String = buf
+    while i < n {
+        let c: Int = str_char_code(s, i)
+        b = __str_set_char(b, off + i, c)
+        i = i + 1
+    }
+    return b
+}
+
+fn write_wav(samples: [Int], sr: Int, path: String) -> Bool {
+    let ns: Int = native_list_len(samples)
+    let datalen: Int = ns * 2
+    let total: Int = 44 + datalen
+    let buf: String = __str_alloc(total)
+    buf = wav_ascii(buf, 0, "RIFF")
+    buf = wav_le32(buf, 4, 36 + datalen)
+    buf = wav_ascii(buf, 8, "WAVE")
+    buf = wav_ascii(buf, 12, "fmt ")
+    buf = wav_le32(buf, 16, 16)
+    buf = wav_le16(buf, 20, 1)
+    buf = wav_le16(buf, 22, 1)
+    buf = wav_le32(buf, 24, sr)
+    buf = wav_le32(buf, 28, sr * 2)
+    buf = wav_le16(buf, 32, 2)
+    buf = wav_le16(buf, 34, 16)
+    buf = wav_ascii(buf, 36, "data")
+    buf = wav_le32(buf, 40, datalen)
+    let j: Int = 0
+    let off: Int = 44
+    while j < ns {
+        let raw: Int = native_list_get(samples, j)
+        buf = wav_le16(buf, off, raw)
+        off = off + 2
+        j = j + 1
+    }
+    return __fs_write_bytes(path, buf, total)
+}
+
+// One formant resonance as a float Lorentzian peak (own-core physics).
+fn fgain(f: Float, fc: Float, bw: Float) -> Float {
+    let d: Float = f - fc
+    return (bw * bw) / (d * d + bw * bw)
+}
+
+// His PITCH MELODY from measured prosody [f0_median, f0_min, f0_max, declination].
+// A natural statement shape over the utterance: onset rise to the median, a
+// near-flat body (his declination is ~0.6 Hz/s), and a final fall toward f0_min.
+// Follows his melody + range, not a fixed 0.85 decline. gidx/total = position.
+fn prosody_f0(pros: [Int], gidx: Int, total: Int) -> Int {
+    let med: Int = native_list_get(pros, 0)
+    let lo: Int = native_list_get(pros, 1)
+    let hi: Int = native_list_get(pros, 2)
+    let p: Int = gidx * 1000 / total
+    let f0: Int = med
+    if p < 150 {
+        f0 = lo + (med - lo) * p / 150
+    } else {
+        if p > 700 {
+            f0 = med + (lo - med) * (p - 700) / 300
+        } else {
+            f0 = med
+        }
+    }
+    if f0 < lo {
+        f0 = lo
+    }
+    if f0 > hi {
+        f0 = hi
+    }
+    return f0
+}
+
+// -- The render: phoneme codes + voice signature -> normalized PCM samples ----
+// Formant geometry per phoneme is READ FROM THE ENGRAM (pmap) via phon_geo — no
+// table in code. The optional ACCENT map (amap) composes a transform onto the
+// voice (voice (+) accent, separable): RP formant overrides read from the accent
+// manifold + a non-rhotic coda-R drop. Empty amap = base General-American.
+// Synthesis is FLOAT: a real phase accumulator + math_sin, superposition physics.
+fn synth_codes_accent(codes0: [String], voice: [String], pmap: [String], amap: [String], vset: [String], vmap: [String], prosody: [Int]) -> [Int] {
+    let sr: Int = 16000
+    let srf: Float = 16000.0
+    let two_pi: Float = 6.283185307
+    let kf: Int = voice_get_int(voice, "kf")
+    let f0s: Int = voice_get_int(voice, "f0")
+    let f0e: Int = voice_get_int(voice, "f0_end")
+    let durm: Int = voice_get_int(voice, "dur")
+    if kf <= 0 {
+        kf = 1000
+    }
+    if durm <= 0 {
+        durm = 1000
+    }
+    let use_accent: Int = 0
+    if native_list_len(amap) > 0 {
+        use_accent = 1
+    }
+    let codes: [String] = codes0
+    if use_accent == 1 {
+        if is_nonrhotic(amap) == 1 {
+            codes = apply_rhoticity(codes0, vset)
+        }
+    }
+    let nc: Int = native_list_len(codes)
+
+    // pass 1: per-segment sample counts + total
+    let segn: [Int] = native_list_empty()
+    let total: Int = 0
+    let ci: Int = 0
+    while ci < nc {
+        let code: String = native_list_get(codes, ci)
+        let p: [Int] = phon_geo(pmap, code)
+        let durms: Int = native_list_get(p, 8)
+        let ns: Int = durms * 16 * durm / 1000
+        segn = native_list_append(segn, ns)
+        total = total + ns
+        ci = ci + 1
+    }
+    if total <= 0 {
+        total = 1
+    }
+
+    // pass 2: synthesize
+    let samples: [Int] = native_list_empty()
+    let phasef: Float = 0.0
+    let gidx: Int = 0
+    let prevF1: Int = 500 * kf / 1000
+    let prevF2: Int = 1500 * kf / 1000
+    let prevF3: Int = 2500 * kf / 1000
+    let nstate: Int = 22695
+    let maxabs: Int = 1
+
+    let ci2: Int = 0
+    while ci2 < nc {
+        let code: String = native_list_get(codes, ci2)
+        let p: [Int] = phon_geo(pmap, code)
+        let rf1: Int = native_list_get(p, 0)
+        let rf2: Int = native_list_get(p, 1)
+        let rf3: Int = native_list_get(p, 2)
+        if use_accent == 1 {
+            let ov: [Int] = accent_formants(amap, code)
+            if native_list_len(ov) >= 3 {
+                rf1 = native_list_get(ov, 0)
+                rf2 = native_list_get(ov, 1)
+                rf3 = native_list_get(ov, 2)
+            }
+        }
+        // HIS measured vowel target overrides the generic/kf path (absolute Hz —
+        // his formants already encode his vocal tract, so no kf scaling).
+        let usekf: Int = 1
+        if native_list_len(vmap) > 0 {
+            let hv: [Int] = vmap_get(vmap, code)
+            if native_list_len(hv) >= 3 {
+                rf1 = native_list_get(hv, 0)
+                rf2 = native_list_get(hv, 1)
+                rf3 = native_list_get(hv, 2)
+                usekf = 0
+            }
+        }
+        let F1t: Int = rf1 * kf / 1000
+        let F2t: Int = rf2 * kf / 1000
+        let F3t: Int = rf3 * kf / 1000
+        if usekf == 0 {
+            F1t = rf1
+            F2t = rf2
+            F3t = rf3
+        }
+        let B1: Int = native_list_get(p, 3)
+        let B2: Int = native_list_get(p, 4)
+        let B3: Int = native_list_get(p, 5)
+        let voiced: Int = native_list_get(p, 6)
+        let ampv: Int = native_list_get(p, 9)
+        let ns: Int = native_list_get(segn, ci2)
+        let trans: Int = ns / 2
+        if trans > 560 {
+            trans = 560
+        }
+        if trans < 1 {
+            trans = 1
+        }
+        let k: Int = 0
+        while k < ns {
+            let cF1: Int = F1t
+            let cF2: Int = F2t
+            let cF3: Int = F3t
+            if k < trans {
+                cF1 = prevF1 + (F1t - prevF1) * k / trans
+                cF2 = prevF2 + (F2t - prevF2) * k / trans
+                cF3 = prevF3 + (F3t - prevF3) * k / trans
+            }
+            let f0c: Int = f0s + (f0e - f0s) * gidx / total
+            if native_list_len(prosody) >= 3 {
+                f0c = prosody_f0(prosody, gidx, total)
+            }
+            if f0c < 40 {
+                f0c = 40
+            }
+            let env: Int = 32767
+            let ar: Int = 96
+            if k < ar {
+                env = 32767 * k / ar
+            }
+            let tail: Int = ns - k
+            if tail < ar {
+                env = 32767 * tail / ar
+            }
+            let f0cf: Float = int_to_float(f0c)
+            phasef = phasef + two_pi * f0cf / srf
+            if phasef > two_pi {
+                phasef = phasef - two_pi
+            }
+
+            let s: Int = 0
+            if voiced == 1 {
+                let cF1f: Float = int_to_float(cF1)
+                let cF2f: Float = int_to_float(cF2)
+                let cF3f: Float = int_to_float(cF3)
+                let B1f: Float = int_to_float(B1)
+                let B2f: Float = int_to_float(B2)
+                let B3f: Float = int_to_float(B3)
+                let acc: Float = 0.0
+                let h: Int = 1
+                while h <= 50 {
+                    let hf: Float = int_to_float(h)
+                    let fhf: Float = hf * f0cf
+                    if fhf < 7900.0 {
+                        let sv: Float = math_sin(phasef * hf)
+                        let src: Float = 1.0 / hf
+                        let g1: Float = fgain(fhf, cF1f, B1f)
+                        let g2: Float = fgain(fhf, cF2f, B2f)
+                        let g3: Float = fgain(fhf, cF3f, B3f)
+                        let g: Float = g1 + g2 + g3
+                        acc = acc + src * g * sv
+                    }
+                    h = h + 1
+                }
+                s = float_to_int(acc * 4000.0)
+            } else {
+                if ampv > 0 {
+                    nstate = nstate * 1103515245 + 12345
+                    nstate = nstate - (nstate / 2147483648) * 2147483648
+                    if nstate < 0 {
+                        nstate = 0 - nstate
+                    }
+                    let nz: Int = nstate / 32768 - 32768
+                    s = nz
+                }
+            }
+            s = s * ampv / 100
+            s = s * env / 32767
+            samples = native_list_append(samples, s)
+            let a: Int = s
+            if a < 0 {
+                a = 0 - a
+            }
+            if a > maxabs {
+                maxabs = a
+            }
+            gidx = gidx + 1
+            k = k + 1
+        }
+        prevF1 = F1t
+        prevF2 = F2t
+        prevF3 = F3t
+        ci2 = ci2 + 1
+    }
+
+    // normalize to int16 range (~22000 peak)
+    let out: [Int] = native_list_empty()
+    let ntot: Int = native_list_len(samples)
+    let j: Int = 0
+    while j < ntot {
+        let raw: Int = native_list_get(samples, j)
+        let v: Int = raw * 22000 / maxabs
+        out = native_list_append(out, v)
+        j = j + 1
+    }
+    return out
+}
+
+// GA convenience wrapper (no accent) — keeps the base render path.
+fn synth_codes(codes: [String], voice: [String], pmap: [String]) -> [Int] {
+    let noacc: [String] = native_list_empty()
+    let novset: [String] = native_list_empty()
+    let novmap: [String] = native_list_empty()
+    let nopros: [Int] = native_list_empty()
+    return synth_codes_accent(codes, voice, pmap, noacc, novset, novmap, nopros)
+}
+
+// -- Voice-by-imitation: HEAR a PCM sample -> extract the voice signature -----
+// Pitch by autocorrelation; vocal-tract scale (kf) from the F1 formant peak of a
+// heard sustained vowel /AA/ (nominal F1 = 730 Hz) via an integer DFT. The
+// analyzer sees ONLY the PCM samples — never the source signature numbers — so
+// recovery is genuinely by ear.
+fn voice_f0(samples: [Int], sr: Int) -> Int {
+    let n: Int = native_list_len(samples)
+    let start: Int = n / 4
+    let end: Int = n * 3 / 4
+    // bound the analysis window so accumulators can never overflow on long input
+    if end - start > 6000 {
+        end = start + 6000
+    }
+    let minlag: Int = sr / 300
+    let maxlag: Int = sr / 75
+    let best: Int = 0
+    let bestlag: Int = minlag
+    let lag: Int = minlag
+    while lag <= maxlag {
+        let sum: Int = 0
+        let i: Int = start
+        while i < end {
+            let ai: Int = native_list_get(samples, i)
+            let bi: Int = native_list_get(samples, i + lag)
+            sum = sum + ai * bi / 256
+            i = i + 2
+        }
+        if sum > best {
+            best = sum
+            bestlag = lag
+        }
+        lag = lag + 1
+    }
+    if bestlag < 1 {
+        bestlag = 1
+    }
+    return sr / bestlag
+}
+
+fn voice_peak_in_band(samples: [Int], sr: Int, flo: Int, fhi: Int) -> Int {
+    let n: Int = native_list_len(samples)
+    let start: Int = n / 4
+    let end: Int = n * 3 / 4
+    // bound the DFT window: re/im are accumulated /4096, and re*re must stay in
+    // int64 — cap terms so (window/2)*(peak_term) squared cannot overflow.
+    if end - start > 3000 {
+        end = start + 3000
+    }
+    let bestmag: Int = 0
+    let bestf: Int = flo
+    let f: Int = flo
+    while f <= fhi {
+        let re: Int = 0
+        let im: Int = 0
+        let i: Int = start
+        while i < end {
+            let x: Int = native_list_get(samples, i)
+            let ph: Int = i * f * 65536 / sr
+            ph = ph - (ph / 65536) * 65536
+            let cq: Int = sp_cos(ph)
+            let sq: Int = sp_sin(ph)
+            re = re + x * cq / 4096
+            im = im + x * sq / 4096
+            i = i + 2
+        }
+        let mag: Int = re * re + im * im
+        if mag > bestmag {
+            bestmag = mag
+            bestf = f
+        }
+        f = f + 25
+    }
+    return bestf
+}
+
+// Analyze a heard sustained /AA/ -> a full voice signature (by ear).
+fn voice_analyze(samples: [Int], sr: Int) -> [String] {
+    let f0: Int = voice_f0(samples, sr)
+    let f1: Int = voice_peak_in_band(samples, sr, 450, 1150)
+    let kf: Int = 1000 * f1 / 730
+    let f0e: Int = f0 * 85 / 100
+    return voice_new("imitated", f0, f0e, kf, 1000, 1000, 8)
+}
@@ -0,0 +1,244 @@
+// voice-ingest.el - The LIVE VOICE LOOP reshape + ingest-as-geometry.
+//
+// EL cannot read a binary WAV (fs_read NUL-truncates), so the thin-medium DSP
+// extractor is periph's `voiceprint` (autocorr F0 + LPC formants), equivalent to
+// our own voice_analyze. This module: (1) RESHAPE the voiceprint JSON (TEXT) into
+// the organ voice-signature schema; (2) INGEST it as a GEOMETRY manifold in the
+// engram and engram_save it to a file; (3) READ the target signature BACK from
+// that geometry (engram_load + scan + filter), never from the json or a table.
+// HONEST: this reaches for pitch + a coarse vocal-tract scale (kf). It is NOT a
+// clone — no glottal timbre, vowel-space, or articulation is captured.
+
+fn parse_leading_int(s: String) -> Int {
+    let n: Int = str_len(s)
+    let i: Int = 0
+    let v: Int = 0
+    let started: Int = 0
+    while i < n {
+        let c: Int = str_char_code(s, i)
+        if c >= 48 {
+            if c <= 57 {
+                v = v * 10 + (c - 48)
+                started = 1
+                i = i + 1
+            } else {
+                i = n
+            }
+        } else {
+            if started == 1 {
+                i = n
+            } else {
+                i = i + 1
+            }
+        }
+    }
+    return v
+}
+
+// voiceprint JSON -> organ voice-signature source file; returns [f0,f0_end,kf,f1,f2,f3].
+fn reshape_voiceprint(vppath: String, outjson: String) -> [Int] {
+    let j: String = fs_read(vppath)
+    let f0: Int = parse_uint_from(j, "f0_hz\":")
+    let fp: Int = str_index_of(j, "formants_hz")
+    let tail: String = str_slice(j, fp, fp + 120)
+    let br: Int = str_index_of(tail, "[")
+    let arr: String = str_slice(tail, br + 1, str_len(tail))
+    let f1: Int = parse_leading_int(arr)
+    let c1: Int = str_index_of(arr, ",")
+    let a2: String = str_slice(arr, c1 + 1, str_len(arr))
+    let f2: Int = parse_leading_int(a2)
+    let c2: Int = str_index_of(a2, ",")
+    let a3: String = str_slice(a2, c2 + 1, str_len(a2))
+    let f3: Int = parse_leading_int(a3)
+    let f0e: Int = f0 * 85 / 100
+    // derive kf honestly: coarse vocal-tract scale from the formant pattern
+    let t1: Int = 1000 * f1 / 500
+    let t2: Int = 1000 * f2 / 1500
+    let t3: Int = 1000 * f3 / 2500
+    let kf: Int = (t1 + t2 + t3) / 3
+    if kf < 800 {
+        kf = 800
+    }
+    if kf > 1400 {
+        kf = 1400
+    }
+    let js: String = "{\"dataset\":\"will-voice-signature\",\"primitive_type\":\"voice\",\"grounding\":\"measured\",\"provenance\":\"Will live 30s read 2026-08-15 (elp/data/live/will30_clean.wav, 27.0s) SUPERSEDES the coarse 10s sample; F0+formants via periph voiceprint (autocorr+LPC), averaged over his full vowel set. Still the 11-number average: no coarticulation/prosody. COARSE — pitch + vocal-tract scale, NOT a clone.\",\"records\":[{\"key\":\"will\",\"features\":{\"source\":\"live-mic\"},\"attributes\":{\"f0\":" + int_to_str(f0) + ",\"f0_end\":" + int_to_str(f0e) + ",\"kf\":" + int_to_str(kf) + ",\"f1\":" + int_to_str(f1) + ",\"f2\":" + int_to_str(f2) + ",\"f3\":" + int_to_str(f3) + "}}]}"
+    let okw: Bool = fs_write(outjson, js)
+    let r: [Int] = native_list_empty()
+    let r = native_list_append(r, f0)
+    let r = native_list_append(r, f0e)
+    let r = native_list_append(r, kf)
+    let r = native_list_append(r, f1)
+    let r = native_list_append(r, f2)
+    let r = native_list_append(r, f3)
+    return r
+}
+
+// Ingest the signature as a manifold (a set-hub + the will node + a member edge)
+// and engram_save it to a reloadable file. grounding:measured self-declared.
+fn ingest_voice(sig: [Int], savepath: String) -> Int {
+    let f0: Int = native_list_get(sig, 0)
+    let f0e: Int = native_list_get(sig, 1)
+    let kf: Int = native_list_get(sig, 2)
+    let f1: Int = native_list_get(sig, 3)
+    let f2: Int = native_list_get(sig, 4)
+    let f3: Int = native_list_get(sig, 5)
+    let hub: String = engram_node("voice-signature-set will grounding=measured src=periph-voiceprint", "VoiceSet", 90)
+    let cont: String = "voice will | f0=" + int_to_str(f0) + " f0_end=" + int_to_str(f0e) + " kf=" + int_to_str(kf) + " f1=" + int_to_str(f1) + " f2=" + int_to_str(f2) + " f3=" + int_to_str(f3) + " grounding=measured src=periph-voiceprint-30s supersedes=prior-voice-region prov=COARSE-pitch+tractscale-NOT-a-clone"
+    let id: String = engram_node(cont, "Voice", 90)
+    engram_connect(id, hub, 90, "member_of")
+    let oks: Bool = engram_save(savepath)
+    return 1
+}
+
+// READ the target voice back FROM the ingested geometry (engram_load + scan +
+// client-filter for "voice will"). Returns [f0,f0_end,kf,f1,f2,f3] or empty.
+fn load_voice(savepath: String) -> [Int] {
+    let ok: Bool = engram_load(savepath)
+    let r: [Int] = native_list_empty()
+    if ok == false {
+        return r
+    }
+    let j: String = engram_scan_nodes_json(200, 0)
+    let p: Int = str_index_of(j, "voice will ")
+    if p < 0 {
+        return r
+    }
+    let win: String = str_slice(j, p, p + 200)
+    let r = native_list_append(r, parse_uint_from(win, "f0="))
+    let r = native_list_append(r, parse_uint_from(win, "f0_end="))
+    let r = native_list_append(r, parse_uint_from(win, "kf="))
+    let r = native_list_append(r, parse_uint_from(win, "f1="))
+    let r = native_list_append(r, parse_uint_from(win, "f2="))
+    let r = native_list_append(r, parse_uint_from(win, "f3="))
+    return r
+}
+
+// ---- Vowel-space + prosody: ingest-as-geometry + read-back (no source layer) --
+// vowel target lookup from the ingested vowel-space manifold: sym -> [f1,f2,f3].
+fn vmap_get(vmap: [String], code: String) -> [Int] {
+    let out: [Int] = native_list_empty()
+    let id: String = sp_map_get(vmap, code)
+    if str_eq(id, "") {
+        return out
+    }
+    let f1: Int = parse_uint_from(id, "f1=")
+    if f1 <= 0 {
+        return out
+    }
+    let out = native_list_append(out, f1)
+    let out = native_list_append(out, parse_uint_from(id, "f2="))
+    let out = native_list_append(out, parse_uint_from(id, "f3="))
+    return out
+}
+
+// Ingest his measured vowel space + prosody as ONE manifold (VowelSpace hub +
+// per-vowel target nodes + a prosody node) and engram_save it. Fresh empty store
+// per run => set-replace, no duplicate.
+fn ingest_voicegeom(vpath: String, ppath: String, savepath: String) -> Int {
+    let hub: String = engram_node("vowel-space-set will grounding=measured src=lpc-formant-track-30s", "VowelSpace", 90)
+    let content: String = fs_read(vpath)
+    let lines: [String] = str_split(content, "\n")
+    let nl: Int = native_list_len(lines)
+    let li: Int = 0
+    while li < nl {
+        let line: String = native_list_get(lines, li)
+        let ok: Int = 1
+        if str_len(line) < 5 {
+            ok = 0
+        }
+        if ok == 1 {
+            if str_char_code(line, 0) == 35 {
+                ok = 0
+            }
+        }
+        if ok == 1 {
+            let f: [String] = str_split(line, "|")
+            if native_list_len(f) >= 5 {
+                let sym: String = native_list_get(f, 0)
+                let cont: String = "vowel-target will " + sym + " | f1=" + native_list_get(f, 1) + " f2=" + native_list_get(f, 2) + " f3=" + native_list_get(f, 3) + " n=" + native_list_get(f, 4) + " grounding=measured src=lpc-formant-track-30s"
+                let id: String = engram_node(cont, "VowelTarget", 90)
+                engram_connect(id, hub, 90, "member_of")
+            }
+        }
+        li = li + 1
+    }
+    let pc: String = fs_read(ppath)
+    let plines: [String] = str_split(pc, "\n")
+    let pnl: Int = native_list_len(plines)
+    let pi: Int = 0
+    while pi < pnl {
+        let pl: String = native_list_get(plines, pi)
+        let ok2: Int = 1
+        if str_len(pl) < 5 {
+            ok2 = 0
+        }
+        if ok2 == 1 {
+            if str_char_code(pl, 0) == 35 {
+                ok2 = 0
+            }
+        }
+        if ok2 == 1 {
+            let pf: [String] = str_split(pl, "|")
+            if native_list_len(pf) >= 4 {
+                let pcont: String = "prosody will | f0_median=" + native_list_get(pf, 0) + " f0_min=" + native_list_get(pf, 1) + " f0_max=" + native_list_get(pf, 2) + " declination=" + native_list_get(pf, 3) + " src=f0-contour-30s"
+                let pid: String = engram_node(pcont, "Prosody", 90)
+                engram_connect(pid, hub, 90, "prosody_of")
+            }
+        }
+        pi = pi + 1
+    }
+    let oks: Bool = engram_save(savepath)
+    return 1
+}
+
+// Read the vowel-space back from geometry; prosody folded under key __PROSODY__.
+fn load_voicegeom(savepath: String) -> [String] {
+    let m: [String] = native_list_empty()
+    let ok: Bool = engram_load(savepath)
+    if ok == false {
+        return m
+    }
+    let j: String = engram_scan_nodes_json(400, 0)
+    let jl: Int = str_len(j)
+    let off: Int = 0
+    while off < jl {
+        let rest: String = str_slice(j, off, jl)
+        let p: Int = str_index_of(rest, "vowel-target will ")
+        if p < 0 {
+            off = jl
+        } else {
+            let abs: Int = off + p
+            let win: String = str_slice(j, abs, abs + 140)
+            let after: String = str_slice(win, 18, str_len(win))
+            let sp: Int = str_index_of(after, " ")
+            if sp > 0 {
+                let sym: String = str_slice(after, 0, sp)
+                m = native_list_append(m, sym)
+                m = native_list_append(m, win)
+            }
+            off = abs + 18
+        }
+    }
+    let pp: Int = str_index_of(j, "prosody will ")
+    if pp >= 0 {
+        let pwin: String = str_slice(j, pp, pp + 160)
+        m = native_list_append(m, "__PROSODY__")
+        m = native_list_append(m, pwin)
+    }
+    return m
+}
+
+// Prosody stats [f0_median, f0_min, f0_max, declination] read from geometry.
+fn prosody_from(vmap: [String]) -> [Int] {
+    let out: [Int] = native_list_empty()
+    let id: String = sp_map_get(vmap, "__PROSODY__")
+    if str_eq(id, "") {
+        return out
+    }
+    let out = native_list_append(out, parse_uint_from(id, "f0_median="))
+    let out = native_list_append(out, parse_uint_from(id, "f0_min="))
+    let out = native_list_append(out, parse_uint_from(id, "f0_max="))
+    let out = native_list_append(out, parse_uint_from(id, "declination="))
+    return out
+}
@@ -0,0 +1,85 @@
+// voice-profile.el - The VOICE signature as a pluggable PROFILE.
+//
+// Exact mirror of surface-profile.el / language-profile.el: a voice is a
+// [String] slot-map read via voice_get, the SAME mechanism the realizer uses
+// for language and surface. Where an instrument signature (a few dozen numbers)
+// is the timbre of a musical tone, a VOICE signature is the timbre of the vocal
+// tract — the instrument that renders LANGUAGE-meaning as SPEECH on the audio
+// surface. Physics (source-filter), not a recorded corpus.
+//
+// The signature is a few numbers, all integer (EL float arithmetic is unusable):
+//   name    - label
+//   f0      - base pitch, Hz (glottal source rate at utterance start)
+//   f0_end  - pitch at utterance end (declination -> falling = declarative)
+//   kf      - formant scale in PER-MILLE (1000 = x1.0). Encodes vocal-tract
+//             length: shorter tract (child/female) -> higher kf. Scales every
+//             phoneme's nominal formant: F_actual = F_nominal * kf / 1000.
+//   dur     - speaking-rate multiplier in per-mille (1000 = nominal; >1000 slower)
+//   tilt    - source spectral tilt (per-mille; higher = darker/steeper rolloff)
+//   breath  - breathiness 0..100 (aspiration mixed into the source)
+//
+// A voice is grabbed BY EAR (voice_analyze in speech.el extracts these numbers
+// from a short PCM sample — an impression, not 10h of training), or declared.
+
+fn voice_new(name: String, f0: Int, f0_end: Int, kf: Int, dur: Int, tilt: Int, breath: Int) -> [String] {
+    let r: [String] = native_list_empty()
+    let r = native_list_append(r, "name")
+    let r = native_list_append(r, name)
+    let r = native_list_append(r, "f0")
+    let r = native_list_append(r, int_to_str(f0))
+    let r = native_list_append(r, "f0_end")
+    let r = native_list_append(r, int_to_str(f0_end))
+    let r = native_list_append(r, "kf")
+    let r = native_list_append(r, int_to_str(kf))
+    let r = native_list_append(r, "dur")
+    let r = native_list_append(r, int_to_str(dur))
+    let r = native_list_append(r, "tilt")
+    let r = native_list_append(r, int_to_str(tilt))
+    let r = native_list_append(r, "breath")
+    let r = native_list_append(r, int_to_str(breath))
+    return r
+}
+
+// Accessor — identical convention to surface_get / lang_get.
+fn voice_get(profile: [String], key: String) -> String {
+    let n: Int = native_list_len(profile)
+    let i: Int = 0
+    while i < n - 1 {
+        let k: String = native_list_get(profile, i)
+        if str_eq(k, key) {
+            return native_list_get(profile, i + 1)
+        }
+        let i = i + 2
+    }
+    return ""
+}
+
+fn voice_get_int(profile: [String], key: String) -> Int {
+    let s: String = voice_get(profile, key)
+    if str_eq(s, "") {
+        return 0
+    }
+    return str_to_int(s)
+}
+
+// -- Built-in voices ---------------------------------------------------------
+
+// Neuron's own voice: calm, precise, androgynous-neutral. Low-ish base pitch,
+// gentle declination, near-neutral vocal-tract length.
+fn voice_neuron() -> [String] {
+    return voice_new("neuron", 112, 96, 1020, 1000, 1000, 6)
+}
+
+// Will's voice signature, built from the INGESTED geometry (f0/f0_end/kf read
+// back from the will-voice manifold — passed in, never hardcoded). Composable
+// with an accent transform exactly like voice_neuron() (voice (+) accent).
+fn voice_will(f0: Int, f0_end: Int, kf: Int) -> [String] {
+    return voice_new("will", f0, f0_end, kf, 1000, 1000, 6)
+}
+
+// A deliberately DISTINCT target voice for the imitation proof: higher pitch,
+// shorter vocal tract (kf=1.20) -> a clearly different speaker. Neuron will
+// HEAR a sample of this voice and reconstruct these numbers by ear.
+fn voice_target_a() -> [String] {
+    return voice_new("target_a", 178, 150, 1200, 950, 1000, 10)
+}
@@ -0,0 +1,45 @@
+// speech-accent-demo.el - PROOF: Neuron speaks with a BRITISH accent, where the
+// accent is a TRANSFORM composed onto the voice (voice (+) accent, separable),
+// INGESTED as geometry (not a table). Same voice, accent toggled on/off = RP/GA.
+
+fn main() {
+    let outdir: String = "/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-acc02900ef4ade35e/elp/tests/examples/out/"
+
+    // LEARN: base phonetics + lexicon + the British-RP accent transform, all as
+    // ingested geometry (source -> manifold -> engram).
+    let pmap: [String] = ingest_phonetics("elp/data/phonetics.psv")
+    let lmap: [String] = ingest_lexicon("elp/data/lexicon.psv")
+    let amap: [String] = ingest_accent("elp/data/british-accent.psv")
+    println("[learn] phonemes=" + int_to_str(native_list_len(pmap) / 2) + " words=" + int_to_str(native_list_len(lmap) / 2) + " accent_targets=" + int_to_str(native_list_len(amap) / 2))
+
+    let neuron: [String] = voice_neuron()
+    let noaccent: [String] = native_list_empty()
+
+    // -- Sentence 1: "I am Neuron." from meaning ----------------------------
+    let fr1: [String] = sem_frame("describe", "I", "Neuron", "")
+    let t1: String = sem_realize(fr1)
+    let c1: [String] = text_phonemes(lmap, t1)
+    println("[s1] " + t1 + " :: " + list_join(c1, " "))
+
+    // separability: SAME voice, accent OFF (GA) vs ON (RP)
+    let ga: [Int] = synth_codes_accent(c1, neuron, pmap, noaccent)
+    let okga: Bool = write_wav(ga, 16000, outdir + "ga-neuron.wav")
+    let br1: [Int] = synth_codes_accent(c1, neuron, pmap, amap)
+    let okb1: Bool = write_wav(br1, 16000, outdir + "british-neuron.wav")
+
+    // -- Sentence 2: showcases NON-RHOTICITY --------------------------------
+    let fr2: [String] = sem_frame("describe", "I", "here", "")
+    let t2: String = sem_realize(fr2)
+    let c2: [String] = text_phonemes(lmap, t2)
+    let c2rp: [String] = apply_rhoticity(c2, pmap)
+    println("[s2] " + t2 + " :: GA=" + list_join(c2, " ") + "  RP=" + list_join(c2rp, " "))
+    let br2: [Int] = synth_codes_accent(c2, neuron, pmap, amap)
+    let okb2: Bool = write_wav(br2, 16000, outdir + "british-2.wav")
+
+    // show an RP override read straight from the accent geometry
+    let ovAA: [Int] = accent_formants(amap, "AA")
+    if native_list_len(ovAA) >= 3 {
+        println("[accent-geometry] AA(LOT) RP f1=" + int_to_str(native_list_get(ovAA, 0)) + " f2=" + int_to_str(native_list_get(ovAA, 1)) + " (base GA 730/1090) [PROVISIONAL]")
+    }
+    println("[done] ga-neuron=" + bool_to_str(okga) + " british-neuron=" + bool_to_str(okb1) + " british-2=" + bool_to_str(okb2))
+}
@@ -0,0 +1,69 @@
+// speech-demo.el - PROOF: Neuron speaks from MEANING, rendered through INGESTED
+// phonetic geometry, own-core, plus voice-by-IMITATION. Built by concatenating
+// the elp realizer + voice-profile + speech-ingest + speech, then this main.
+//
+//   LEARN   : ingest acoustic-phonetics + lexicon SOURCES -> phoneme manifold in
+//             the engram (source -> manifold -> merge).
+//   MEANING : sem_frame("describe","I","Neuron","") -> sem_realize -> "I am Neuron."
+//   PHONES  : words -> phoneme codes, READ from the ingested lexicon geometry.
+//   RENDER  : superpose formant resonances (read from engram) over a glottal
+//             source -> own-core PCM/WAV, in Neuron's own voice.
+//   IMITATE : HEAR a short sample of a different voice -> extract its signature
+//             by ear (autocorrelation pitch + integer-DFT formant) -> render new
+//             speech in that voice. An impression, not a corpus.
+
+fn speak_report(tag: String, codes: [String], voice: [String], pmap: [String], path: String) -> [Int] {
+    let s: [Int] = synth_codes(codes, voice, pmap)
+    let ok: Bool = write_wav(s, 16000, path)
+    println(tag + " samples=" + int_to_str(native_list_len(s)) + " ok=" + bool_to_str(ok) + " -> " + path)
+    return s
+}
+
+fn main() {
+    let outdir: String = "/private/tmp/claude-501/-Users-will/6531446d-bc27-4095-930b-e04777c3db4f/scratchpad/"
+
+    // -- LEARN: ingest the speech primitives as geometry --------------------
+    let pmap: [String] = ingest_phonetics("elp/data/phonetics.psv")
+    let lmap: [String] = ingest_lexicon("elp/data/lexicon.psv")
+    let saved: Bool = engram_save(outdir + "phoneme-manifold.json")
+    println("[learn] phonemes=" + int_to_str(native_list_len(pmap) / 2) + " words=" + int_to_str(native_list_len(lmap) / 2) + " manifold_saved=" + bool_to_str(saved))
+
+    // sanity: show that AA's formants came from ingested geometry, not code
+    let aa: [Int] = phon_geo(pmap, "AA")
+    let aaF1: Int = native_list_get(aa, 0)
+    let aaF2: Int = native_list_get(aa, 1)
+    println("[read-geometry] AA F1=" + int_to_str(aaF1) + " F2=" + int_to_str(aaF2) + " (parsed from engram node)")
+
+    // -- MEANING -> WORDS via the realizer's language faculty ----------------
+    let frame: [String] = sem_frame("describe", "I", "Neuron", "")
+    let text: String = sem_realize(frame)
+    println("[meaning->text] " + text)
+
+    // -- WORDS -> PHONEMES (read from ingested lexicon geometry) --------------
+    let codes: [String] = text_phonemes(lmap, text)
+    println("[phonemes] " + list_join(codes, " "))
+
+    // -- RENDER in Neuron's own voice ----------------------------------------
+    let neuron: [String] = voice_neuron()
+    let s1: [Int] = speak_report("[speak neuron]", codes, neuron, pmap, outdir + "neuron.wav")
+
+    // -- IMITATION: hear a distinct voice, recover its signature, re-render ---
+    let vA: [String] = voice_target_a()
+    let hcodes: [String] = native_list_empty()
+    hcodes = native_list_append(hcodes, "SIL")
+    let z: Int = 0
+    while z < 6 {
+        hcodes = native_list_append(hcodes, "AA")
+        z = z + 1
+    }
+    hcodes = native_list_append(hcodes, "SIL")
+    let heard: [Int] = synth_codes(hcodes, vA, pmap)
+    let okh: Bool = write_wav(heard, 16000, outdir + "heard.wav")
+
+    let vB: [String] = voice_analyze(heard, 16000)
+    println("[imitate] heard ACTUAL   f0=" + voice_get(vA, "f0") + " kf=" + voice_get(vA, "kf"))
+    println("[imitate] heard RECOVERED f0=" + voice_get(vB, "f0") + " kf=" + voice_get(vB, "kf") + " (extracted by ear from PCM)")
+    let s2: [Int] = speak_report("[speak imitation]", codes, vB, pmap, outdir + "imitation.wav")
+
+    println("[done] rendered from meaning + ingested geometry; imitation from a heard sample.")
+}
@@ -0,0 +1,48 @@
+// speech-organ-demo.el - PROOF: the render now reads its phoneme + accent
+// GEOMETRY from the ingest ORGAN's saved engram files (engram_load +
+// engram_scan_nodes_json + cache), not a same-run hand-load. The British accent
+// is still a composed transform-geometry (voice (+) accent, separable). Numbers
+// come from the organ manifold; the .psv supplies only categorical vowel-class.
+
+fn main() {
+    let outdir: String = "/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-acc02900ef4ade35e/elp/tests/examples/out/"
+
+    // engram-independent caches from source (survive engram_load replacement)
+    let vset: [String] = organ_vset("elp/data/phonetics.psv")
+    let lmap: [String] = organ_lex("elp/data/lexicon.psv")
+    // ORGAN read: phonetics FIRST (cache), THEN accent (engram_load replaces store)
+    let pmap: [String] = organ_pmap("elp/data/phonetics-formants.engram.json")
+    let amap: [String] = organ_amap("elp/data/british-accent.engram.json")
+    println("[organ] phon_syms=" + int_to_str(native_list_len(pmap) / 2) + " accent_syms=" + int_to_str(native_list_len(amap) / 2) + " vowels=" + int_to_str(native_list_len(vset)) + " words=" + int_to_str(native_list_len(lmap) / 2))
+
+    // prove the numbers came from the organ node content
+    let g: [Int] = phon_geo(pmap, "AA")
+    println("[organ-read] phoneme AA f1=" + int_to_str(native_list_get(g, 0)) + " f2=" + int_to_str(native_list_get(g, 1)) + " f3=" + int_to_str(native_list_get(g, 2)) + " (P&B1952 MEASURED)")
+    let ov: [Int] = accent_formants(amap, "AA")
+    if native_list_len(ov) >= 3 {
+        println("[organ-read] accent AA(LOT) f1=" + int_to_str(native_list_get(ov, 0)) + " f2=" + int_to_str(native_list_get(ov, 1)) + " (DERIVED RP, PROVISIONAL)")
+    }
+    println("[organ-read] non_rhotic=" + int_to_str(is_nonrhotic(amap)))
+
+    let neuron: [String] = voice_neuron()
+    let noacc: [String] = native_list_empty()
+
+    // Sentence 1: "I am Neuron." from meaning; GA vs RP = separable toggle
+    let t1: String = sem_realize(sem_frame("describe", "I", "Neuron", ""))
+    let c1: [String] = text_phonemes(lmap, t1)
+    println("[s1] " + t1 + " :: " + list_join(c1, " "))
+    let ga: [Int] = synth_codes_accent(c1, neuron, pmap, noacc, vset)
+    let okga: Bool = write_wav(ga, 16000, outdir + "ga-neuron-organ.wav")
+    let br1: [Int] = synth_codes_accent(c1, neuron, pmap, amap, vset)
+    let okb1: Bool = write_wav(br1, 16000, outdir + "british-neuron-organ.wav")
+
+    // Sentence 2: non-rhoticity showcase
+    let t2: String = sem_realize(sem_frame("describe", "I", "here", ""))
+    let c2: [String] = text_phonemes(lmap, t2)
+    let c2rp: [String] = apply_rhoticity(c2, vset)
+    println("[s2] " + t2 + " :: GA=" + list_join(c2, " ") + "  RP=" + list_join(c2rp, " "))
+    let br2: [Int] = synth_codes_accent(c2, neuron, pmap, amap, vset)
+    let okb2: Bool = write_wav(br2, 16000, outdir + "british-2-organ.wav")
+
+    println("[done] ga-organ=" + bool_to_str(okga) + " british-organ=" + bool_to_str(okb1) + " british-2-organ=" + bool_to_str(okb2))
+}
@@ -0,0 +1,31 @@
+// speech-voice-demo.el - LIVE VOICE LOOP (stand-in test). Capture -> voiceprint
+// -> reshape -> INGEST AS GEOMETRY -> read the target back FROM geometry -> the
+// EL projector renders a line reaching for that voice. Stand-in "Will" = the
+// voiceprint of imitation.wav. HONEST: pitch + coarse vocal-tract scale, NOT a clone.
+
+fn main() {
+    let outdir: String = "/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-acc02900ef4ade35e/elp/tests/examples/out/"
+    let vp: String = "/private/tmp/claude-501/-Users-will/6531446d-bc27-4095-930b-e04777c3db4f/scratchpad/will-voiceprint.json"
+
+    // 1+2: reshape voiceprint JSON -> organ voice-signature source
+    let sig0: [String] = native_list_empty()
+    let sig: [Int] = reshape_voiceprint(vp, "elp/data/will-voice.json")
+    // 3: ingest as geometry + engram_save a reloadable manifold file
+    let ig: Int = ingest_voice(sig, "elp/data/will-voice.engram.json")
+    // 4: READ the target back FROM geometry (engram_load + scan + filter)
+    let g: [Int] = load_voice("elp/data/will-voice.engram.json")
+    println("[voice-geometry] read from manifold: f0=" + int_to_str(native_list_get(g, 0)) + " f0_end=" + int_to_str(native_list_get(g, 1)) + " kf=" + int_to_str(native_list_get(g, 2)) + " f1=" + int_to_str(native_list_get(g, 3)) + " f2=" + int_to_str(native_list_get(g, 4)) + " f3=" + int_to_str(native_list_get(g, 5)) + " (measured, COARSE — not a clone)")
+
+    // phoneme geometry from the organ (loaded AFTER the voice sig is cached in EL)
+    let pmap: [String] = organ_pmap("elp/data/phonetics-formants.engram.json")
+    let lmap: [String] = organ_lex("elp/data/lexicon.psv")
+
+    // 5: render a line FROM MEANING in Will's voice
+    let vw: [String] = voice_will(native_list_get(g, 0), native_list_get(g, 1), native_list_get(g, 2))
+    let t: String = sem_realize(sem_frame("greet", "Will", "", ""))
+    let codes: [String] = text_phonemes(lmap, t)
+    println("[render] \"" + t + "\" :: " + list_join(codes, " ") + "  in voice=will f0=" + int_to_str(voice_get_int(vw, "f0")) + " kf=" + int_to_str(voice_get_int(vw, "kf")))
+    let samples: [Int] = synth_codes(codes, vw, pmap)
+    let ok: Bool = write_wav(samples, 16000, outdir + "will-reply.wav")
+    println("[done] will-reply.wav=" + bool_to_str(ok))
+}
@@ -0,0 +1,48 @@
+// speech-voice-demo2.el - LIVE VOICE LOOP on Will's richer 30s read, with a
+// GEOMETRIC SET-REPLACE of the voice_will manifold (supersede the coarse 10s
+// region, insert the 30s region — no duplicate node, no per-node CRUD; Will's
+// standing rule f999c5ff). HONEST: 30s steadies the 11-number average over more
+// of his vowels, but it is still one formant triple with no coarticulation or
+// prosody — closer but still synthetic, not a clone.
+
+fn main() {
+    let outdir: String = "/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-acc02900ef4ade35e/elp/tests/examples/out/"
+    let vp: String = "/private/tmp/claude-501/-Users-will/6531446d-bc27-4095-930b-e04777c3db4f/scratchpad/will30-voiceprint.json"
+    let manifest: String = "elp/data/will-voice.engram.json"
+
+    // --- SET-REPLACE step 1: read the PRIOR region (text read of the manifold
+    //     file — no engram_load, so the store stays clean) and report what is
+    //     being superseded. ---
+    let prior: String = fs_read(manifest)
+    let pp: Int = str_index_of(prior, "voice will ")
+    if pp >= 0 {
+        let pw: String = str_slice(prior, pp, pp + 200)
+        println("[set-replace] superseding PRIOR voice region: f0=" + int_to_str(parse_uint_from(pw, "f0=")) + " kf=" + int_to_str(parse_uint_from(pw, "kf=")) + " f1=" + int_to_str(parse_uint_from(pw, "f1=")))
+    }
+
+    // --- step 2: reshape the 30s voiceprint -> organ voice-signature source ---
+    let sig: [Int] = reshape_voiceprint(vp, "elp/data/will-voice.json")
+
+    // --- step 3: INSERT the fresh 30s region into an EMPTY engram and save ->
+    //     wholesale replaces the manifold file (old region dropped, not edited,
+    //     not duplicated). This is the geometric set-replace. ---
+    let ig: Int = ingest_voice(sig, manifest)
+
+    // --- step 4: READ the new target BACK from geometry ---
+    let g: [Int] = load_voice(manifest)
+    println("[voice-geometry] new region read from manifold: f0=" + int_to_str(native_list_get(g, 0)) + " f0_end=" + int_to_str(native_list_get(g, 1)) + " kf=" + int_to_str(native_list_get(g, 2)) + " f1=" + int_to_str(native_list_get(g, 3)) + " f2=" + int_to_str(native_list_get(g, 4)) + " f3=" + int_to_str(native_list_get(g, 5)) + " (measured 30s, COARSE — not a clone)")
+
+    // phoneme + lexicon geometry from the organ (loaded after the voice sig is
+    // cached in EL, since engram_load replaces the store)
+    let pmap: [String] = organ_pmap("elp/data/phonetics-formants.engram.json")
+    let lmap: [String] = organ_lex("elp/data/lexicon.psv")
+
+    // --- step 5: render a fresh reply FROM MEANING in the 30s Will voice ---
+    let vw: [String] = voice_will(native_list_get(g, 0), native_list_get(g, 1), native_list_get(g, 2))
+    let t: String = sem_realize(sem_frame("greet", "Will", "", ""))
+    let codes: [String] = text_phonemes(lmap, t)
+    println("[render] \"" + t + "\" :: " + list_join(codes, " ") + "  in voice=will f0=" + int_to_str(voice_get_int(vw, "f0")) + " kf=" + int_to_str(voice_get_int(vw, "kf")))
+    let samples: [Int] = synth_codes(codes, vw, pmap)
+    let ok: Bool = write_wav(samples, 16000, outdir + "will-reply2.wav")
+    println("[done] will-reply2.wav=" + bool_to_str(ok))
+}
@@ -0,0 +1,37 @@
+// speech-voicegeom-demo.el - THE JUMP: render Will's VOWEL SPACE + PROSODY
+// (measured over 30s), not the single 11-number average. His vowels land at HIS
+// targets; pitch follows HIS melody. All read back FROM the ingested geometry.
+// INTERIM: the geometry was Python-measured (measure_voice.py, numpy LPC/F0) —
+// to be superseded by the engram-measures-audio path. No source layer.
+
+fn main() {
+    let outdir: String = "/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-acc02900ef4ade35e/elp/tests/examples/out/"
+
+    // 1: ingest vowel space + prosody as geometry (empty store -> save; set-replace)
+    let ig: Int = ingest_voicegeom("elp/data/will-vowelspace.psv", "elp/data/will-prosody.psv", "elp/data/will-voicegeom.engram.json")
+    // kf (vocal-tract scale for consonants) from the earlier will-voice manifold
+    let sigv: [Int] = load_voice("elp/data/will-voice.engram.json")
+    let kf: Int = native_list_get(sigv, 2)
+    // 2: read vowel space + prosody back FROM geometry
+    let vmap: [String] = load_voicegeom("elp/data/will-voicegeom.engram.json")
+    let pros: [Int] = prosody_from(vmap)
+    println("[geometry] vowels=" + int_to_str((native_list_len(vmap) - 2) / 2) + " prosody f0_median=" + int_to_str(native_list_get(pros, 0)) + " f0_min=" + int_to_str(native_list_get(pros, 1)) + " f0_max=" + int_to_str(native_list_get(pros, 2)) + " kf=" + int_to_str(kf))
+    let ehv: [Int] = vmap_get(vmap, "EH")
+    let ihv: [Int] = vmap_get(vmap, "IH")
+    println("[his-vowels] EH=" + int_to_str(native_list_get(ehv, 0)) + "/" + int_to_str(native_list_get(ehv, 1)) + " IH=" + int_to_str(native_list_get(ihv, 0)) + "/" + int_to_str(native_list_get(ihv, 1)))
+
+    // phoneme geometry from the organ (loaded AFTER caches are in EL)
+    let pmap: [String] = organ_pmap("elp/data/phonetics-formants.engram.json")
+    let lmap: [String] = organ_lex("elp/data/lexicon.psv")
+
+    // 3+4: render FROM MEANING in his-vowels + his-prosody voice
+    let vw: [String] = voice_will(native_list_get(pros, 0), native_list_get(pros, 1), kf)
+    let noacc: [String] = native_list_empty()
+    let novset: [String] = native_list_empty()
+    let t: String = sem_realize(sem_frame("greet", "Will", "", ""))
+    let codes: [String] = text_phonemes(lmap, t)
+    println("[render] \"" + t + "\" :: " + list_join(codes, " "))
+    let samples: [Int] = synth_codes_accent(codes, vw, pmap, noacc, novset, vmap, pros)
+    let ok: Bool = write_wav(samples, 16000, outdir + "will-reply3.wav")
+    println("[done] will-reply3.wav=" + bool_to_str(ok))
+}
@@ -31,46 +31,34 @@ This is where almost all work belongs. El programs are source files that get com

 This is the self-contained C OS-boundary layer. It provides the `__`-prefixed primitives that compiled El programs call: libcurl HTTP, pthreads, filesystem I/O, arena allocation, etc. It is **not generated** — it is maintained by hand.

-The runtime is native El (`runtime/*.el`) over a C OS-boundary. **Status (verified 2026-08-15):** the migration to a seed-only boundary is *in progress, not done*. Two files exist:
- `el-compiler/runtime/el_runtime.c` (~516 KB) — **LIVE**. Holds the engram store (`EngramStore engram_global`) plus the `http_*`/`json_*`/`state_*`/`engram_*` impls. It is the authoritative single-file link target for the compiler, and `tools/install.sh` compiles it into `libel.a`. This is where a new C builtin's *implementation* must currently live to be linkable.
- `el-compiler/runtime/el_seed.c` — the intended hand-maintained `__`-prefixed seed (thin wrappers over the above). It is compiled alongside `el_runtime.c` by `tools/install.sh`, but does **not** compile standalone yet (see the build-path caveat under "Rebuilding the Compiler").
- `el-compiler/runtime/legacy/el_runtime.c` (~419 KB) — **DEAD**. Archived duplicate; no build script references it.
+The old `el_runtime.c` has been archived to `el-compiler/runtime/legacy/`. The runtime is now native El (`runtime/*.el`). `el_seed.c` replaces `el_runtime.c` as the sole C compilation dependency.

-**Only edit these when you genuinely need OS-level access** (raw sockets, GPU calls, new libcurl features, a new engram store op). For everything else, write El.
+**Only edit `el_seed.c` when you genuinely need OS-level access** (raw sockets, GPU calls, new libcurl features). For everything else, write El.

-When you add a C builtin (verbatim-emit recipe — the El name is emitted as the exact C symbol; `builtin_arity` is an arity guard only, not a dispatch table):
-1. Implement the C function in `el_runtime.c` (and declare it in `el_runtime.h`).
-2. Add a `__`-prefixed thin wrapper in `el_seed.c` and declare it in `el_seed.h`.
-3. Add the name to `builtin_arity` in `el-compiler/src/codegen.el` — add **both** the plain and `__`-prefixed spellings.
-4. Rebuild the elc binary (see below) and confirm the self-host fixpoint is byte-identical.
-
-Worked example: the `engram_assert_json` (op_assert seam) and `engram_node_full_in`/`engram_connect_in` (purview write-side) primitives added 2026-08-15 follow exactly this recipe.
+When you do add a C builtin:
+1. Add the C function to `el_seed.c`
+2. Declare it in `el_seed.h`
+3. Add it to the `builtin_arity` table in `el-compiler/src/codegen.el` (so the compiler knows the arg count)
+4. Rebuild the elc binary (see below)

 ---

 ## Rebuilding the Compiler

-After changing any `.el` source in `el-compiler/src/` (run from the `lang/` dir):
+After changing any `.el` source in `el-compiler/src/`:

 ```bash
-# 1. Stage2: current elc compiles the (modified) compiler to C
+cd /Users/will/Development/neuron-technologies/foundation/el
 ./dist/platform/elc elc-cli.el > elc-new.c
-# 2. Build the new compiler. The C link target is el_runtime.c — it holds the
-#    engram store + http/json/state impls the compiler output calls. el_runtime.c
-#    self-hosts elc on its own; el_seed.c is the (aspirational) seed layer and does
-#    NOT compile standalone under clang (missing prototypes for the el_runtime.c
-#    symbols it wraps — see caveat below), so link el_runtime.c here.
 cc -std=c11 -I el-compiler/runtime -lcurl -lpthread \
   -o dist/platform/elc-new \
-   elc-new.c el-compiler/runtime/el_runtime.c
-# 3. Verify self-hosting FIXPOINT (stage3 == stage2 output, byte-identical):
+   elc-new.c el-compiler/runtime/el_seed.c
+# Verify self-hosting:
 ./dist/platform/elc-new elc-cli.el > elc-verify.c
-diff elc-new.c elc-verify.c   # must be identical
+diff elc-new.c elc-verify.c   # should be identical
 mv dist/platform/elc-new dist/platform/elc
 ```

-> **Build-path caveat (verified 2026-08-15).** `el_seed.c` is the intended hand-maintained OS-boundary seed, but it does **not** compile standalone under modern clang: it wraps ~16 unprefixed `el_runtime.c` symbols (`http_serve`, `json_*`, `state_*`, `http_response`) without prototypes, and clang treats implicit declarations as errors (C99+). The productionised install (`tools/install.sh`) builds `libel.a` from **both** `el_seed.o` + `el_runtime.o` together, which is why linking succeeds there. To make `el_seed.c` build on its own, add prototypes for those symbols (or `#include "el_runtime.h"`, reconciling the `__http_serve` return-type mismatch first). Until then, `el_runtime.c` is the authoritative single-file link target for the compiler.
-
 After changing `el_seed.c` only (no El source changes), rebuild downstream programs but do NOT need to rebuild the compiler binary itself — the seed is linked at the application level, not the compiler level.

 ---
@@ -8410,59 +8410,6 @@ el_val_t engram_activate_json(el_val_t query, el_val_t depth) {
    return el_wrap_str(jb_finish(&b));
 }

-/* op_assert seam (realizer promotion, bl-53/#57).
- * Gathers the grounded ASSERTION ENVELOPE for a subject node —
- *   { "subject": <node|null>, "grounding": [ {node,edge,hops}... ] }
- * i.e. the self-geometry a realizer renders as faithful first-person text.
- * Read-only: realization (geometry->text) stays in the faculty/realizer;
- * this native primitive produces its structured input from proven paths
- * (engram_emit_node_json + engram_neighbors_json). arity 2 (node_id, depth). */
-el_val_t engram_assert_json(el_val_t node_id, el_val_t depth) {
-    const char* sid = EL_CSTR(node_id);
-    JsonBuf b; jb_init(&b);
-    jb_puts(&b, "{\"subject\":");
-    EngramNode* n = (sid && *sid) ? engram_find_node(sid) : NULL;
-    if (n) engram_emit_node_json(&b, n); else jb_puts(&b, "null");
-    jb_puts(&b, ",\"grounding\":");
-    el_val_t nb = engram_neighbors_json(node_id, depth, EL_STR("both"));
-    const char* nbs = EL_CSTR(nb);
-    jb_puts(&b, (nbs && *nbs) ? nbs : "[]");
-    jb_putc(&b, '}');
-    return el_wrap_str(jb_finish(&b));
-}
-
-/* Parametric mutation (purview write-side bounding, keystone 56ecbec6).
- * The mutation verbs travel with a TARGET MANIFOLD (purview) instead of the
- * implicit global singleton. purview==0 (EL_NULL) is the DEGENERATE/DEFAULT
- * case: G = live, behaviour identical to the base op. A non-zero purview is a
- * bounded target that the engine cannot yet resolve (multi-manifold store is a
- * promotion item), so we REFUSE rather than silently mutate the live set —
- * write-side bounding must never leak into G=live. */
-el_val_t engram_node_full_in(el_val_t purview,
-                             el_val_t content, el_val_t node_type, el_val_t label,
-                             el_val_t salience, el_val_t importance, el_val_t confidence,
-                             el_val_t tier, el_val_t tags) {
-    if (purview == 0) {
-        return engram_node_full(content, node_type, label, salience, importance,
-                                confidence, tier, tags);
-    }
-    fprintf(stderr, "[engram] purview write-side not yet resolvable (G != live); "
-                    "refusing to append to live store (purview=%lld)\n",
-            (long long)purview);
-    return EL_STR("");
-}
-
-void engram_connect_in(el_val_t purview,
-                       el_val_t from_id, el_val_t to_id, el_val_t weight, el_val_t relation) {
-    if (purview == 0) {
-        engram_connect(from_id, to_id, weight, relation);
-        return;
-    }
-    fprintf(stderr, "[engram] purview write-side not yet resolvable (G != live); "
-                    "refusing to connect in live store (purview=%lld)\n",
-            (long long)purview);
-}
-
 el_val_t engram_stats_json(void) {
    EngramStore* g = engram_get();
    char buf[128];
@@ -639,14 +639,6 @@ el_val_t  engram_scan_nodes_by_type_json(el_val_t node_type, el_val_t limit, el_
 el_val_t  engram_neighbors_json(el_val_t node_id, el_val_t max_depth, el_val_t direction);
 el_val_t  engram_activate_json(el_val_t query, el_val_t depth);
 el_val_t  engram_stats_json(void);
-/* op_assert seam: grounded assertion envelope {subject,grounding} for the realizer. */
-el_val_t  engram_assert_json(el_val_t node_id, el_val_t depth);
-/* Parametric mutation (purview write-side): purview==0 => G=live (default), else refuse. */
-el_val_t  engram_node_full_in(el_val_t purview, el_val_t content, el_val_t node_type, el_val_t label,
-                              el_val_t salience, el_val_t importance, el_val_t confidence,
-                              el_val_t tier, el_val_t tags);
-void      engram_connect_in(el_val_t purview, el_val_t from_id, el_val_t to_id,
-                            el_val_t weight, el_val_t relation);
 el_val_t  engram_list_layers_json(void);
 /* engram_compile_layered_json — produce a prompt-ready text block split
 * into "[LAYER 0 — STRUCTURAL]" (non-suppressible layers, sacred fire)
@@ -1095,15 +1095,6 @@ el_val_t __engram_activate_json(el_val_t query, el_val_t depth) {
 }

 el_val_t __engram_stats_json(void)                         { return engram_stats_json(); }
-el_val_t __engram_assert_json(el_val_t node_id, el_val_t depth) { return engram_assert_json(node_id, depth); }
-el_val_t __engram_node_full_in(el_val_t purview, el_val_t content, el_val_t node_type, el_val_t label,
-                               el_val_t salience, el_val_t importance, el_val_t confidence,
-                               el_val_t tier, el_val_t tags) {
-    return engram_node_full_in(purview, content, node_type, label, salience, importance, confidence, tier, tags);
-}
-void __engram_connect_in(el_val_t purview, el_val_t from_id, el_val_t to_id, el_val_t weight, el_val_t relation) {
-    engram_connect_in(purview, from_id, to_id, weight, relation);
-}
 el_val_t __engram_list_layers_json(void)                   { return engram_list_layers_json(); }

 el_val_t __engram_compile_layered_json(el_val_t intent, el_val_t depth) {
@@ -233,12 +233,6 @@ el_val_t  __engram_scan_nodes_by_type_json(el_val_t node_type, el_val_t limit, e
 el_val_t  __engram_neighbors_json(el_val_t node_id, el_val_t max_depth, el_val_t direction);
 el_val_t  __engram_activate_json(el_val_t query, el_val_t depth);
 el_val_t  __engram_stats_json(void);
-el_val_t  __engram_assert_json(el_val_t node_id, el_val_t depth);
-el_val_t  __engram_node_full_in(el_val_t purview, el_val_t content, el_val_t node_type, el_val_t label,
-                                el_val_t salience, el_val_t importance, el_val_t confidence,
-                                el_val_t tier, el_val_t tags);
-void      __engram_connect_in(el_val_t purview, el_val_t from_id, el_val_t to_id,
-                              el_val_t weight, el_val_t relation);
 el_val_t  __engram_list_layers_json(void);
 el_val_t  __engram_compile_layered_json(el_val_t intent, el_val_t depth);

@@ -2579,9 +2579,6 @@ fn builtin_arity(name: String) -> Int {
    if str_eq(name, "__engram_neighbors_filtered") { return 3 }
    if str_eq(name, "__engram_activate") { return 2 }
    if str_eq(name, "__engram_activate_json") { return 2 }
-    if str_eq(name, "__engram_assert_json") { return 2 }
-    if str_eq(name, "__engram_node_full_in") { return 9 }
-    if str_eq(name, "__engram_connect_in") { return 5 }
    if str_eq(name, "__engram_scan_nodes_json") { return 2 }
    if str_eq(name, "__generate") { return 1 }
    // Filesystem
@@ -2679,9 +2676,6 @@ fn builtin_arity(name: String) -> Int {
    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 }
-    if str_eq(name, "engram_assert_json") { return 2 }
-    if str_eq(name, "engram_node_full_in") { return 9 }
-    if str_eq(name, "engram_connect_in") { return 5 }
    // LLM
    if str_eq(name, "llm_call") { return 2 }
    if str_eq(name, "llm_call_system") { return 3 }