Kit update 2026-08-06: samgo 5.7 (54D BPE, 59M), gate-init finding, Born-rule fix in quantum_pool, corpus-drift retraction + fingerprinting, frozen-corpus wiring table

Adds SAMGO.md and weights/samgo_weights.pt (59,353,668 params, GPT-2 BPE).
FINDINGS.md: gate initialised at sigmoid(-4)=0.018 left the Hebbian kernel alive in
1 layer of 4 in every model ever trained here; opening it to 0.5 gives 4/4 and 0.054
nats, larger than the dyn12-vs-none gap. quantum_pool was flattening the measured
Born-rule distribution to exactly uniform (3.4657 = log 32) and now preserves it
(3.4598 vs true 3.4594). Retracts a 0.0645-nat wiring win that was the live corpus
rewriting itself mid-experiment; adds corpus+flag fingerprinting to prevent recurrence.

FINDINGS.md

@@ -347,6 +347,133 @@ seven rungs trained in **one process** against that one snapshot, 21 runs, 1200
 | `tri3` all three organs, 108D kernel | 1.20026 | −0.0322 | −2.83 | 3/3 | 0.826 | 1,230,682 |
 | none (baseline) | 1.23241 | — | — | 0/3 | 0.810 | 1,135,008 |
 
+### 2026-08-06: the gate was strangled at initialisation, and it cost more than the mechanism was worth
+
+Every number above was measured with the Hebbian gate initialised at `sigmoid(-4) = 0.018`.
+That is not a neutral starting point. It means the kernel contributes **two percent** of
+attention from step one — too little influence to earn gradient — so each layer learns the
+kernel is useless and closes its gate. And a closed gate multiplies the kernel by ~0, which
+kills the gradient to the gate *and* to `log_sigma`. The layer cannot recover.
+
+Measured on the trained checkpoints, per layer:
+
+| checkpoint | L0 | L1 | L2 | L3 |
+|:---|---:|---:|---:|---:|
+| `dyn12phos` | 0.7655 | 0.0009 | 0.0000 | 0.0000 |
+| `phos` | 0.5617 | 0.0076 | 0.0042 | 0.0027 |
+| `ladder_dyn12` | 0.2811 | 0.0091 | 0.0070 | 0.0046 |
+
+**The mechanism was alive in one layer out of four in every model this project has ever
+trained.** Layer 0 survives only because its state is the largest in the stack; the state
+decays with depth (|state| 0.755 → 0.533 → 0.439 → 0.366) and the kernel goes uniform —
+measured `H.diag` 0.043, which is exactly `mean(1/(i+1))` over a causal row at T=128.
+
+Two geometric explanations were proposed and **both were wrong**, which is why this is
+reported as a 2×2 factorial rather than a fix that worked:
+
+| arm | state normalised | gate init | loss | layers alive |
+|:---|:---:|---:|---:|:---:|
+| A | no | −4.0 | 1.05163 | 1/4 |
+| B | **yes** | −4.0 | 1.04742 | 1/4 |
+| C | **yes** | **0.0** | 1.00823 | 4/4 |
+| D | no | **0.0** | **0.99758** | **4/4** |
+
+Arm B is the refutation: unit-normalising the state, which removes the scale mismatch
+entirely, changes nothing — still 1/4. An adaptive median-heuristic bandwidth was also
+tried and was likewise inert (1.05556 vs 1.04913, 1/4 alive). The scale drift is real —
+trained σ at L1 is 15.2 where calibration sets 1.9 — but it is a *symptom*.
+
+**It was one constant.** At `sigmoid(0) = 0.5` the kernel has real authority immediately and
+all four layers keep it, for **0.054 nats** less loss. For scale, the entire `dyn12`-vs-`none`
+gap on this corpus is 0.032. The initialisation was worth more than the mechanism it was
+throttling, and every result in this file predates the fix.
+
+`COSMOS_GATE_INIT` now defaults to `0.0`. Set it to `-4.0` to reproduce anything above.
+
+### 2026-08-06: `quantum_pool` was discarding the Born rule
+
+`quantum_pool()` mapped archived bitstrings to weights via `vals.extend([z] * min(int(c), 8))`.
+A 5-qubit job archives 4096 shots over 32 outcomes, so every count is ~128 and every one
+clipped to exactly 8. Measured over 401 runs:
+
+| distribution | entropy |
+|:---|---:|
+| true measured | 3.4594 nats |
+| what the model received | **3.4657** |
+| uniform over 32 outcomes | 3.4657 |
+
+To four decimals, **uniform**. The shots were real, the archive was real, the CHSH violation
+was real — and the last step before the weights threw the measured probabilities away. Only
+6.25% of shots survived, and the surviving ones carried no distributional information.
+
+"Weights born from measured quantum" was true about provenance and false about content.
+
+Replaced with largest-remainder proportional allocation (256 draws per run, shares
+proportional to measured counts, no flooring at the tail). The pool now carries entropy
+**3.4598** against the true 3.4594, and remains numerically safe for initialisation
+(mean −0.066, sd 0.994).
+
+### RETRACTION, 2026-08-06: a 0.0645-nat wiring win was the corpus moving
+
+A 24-order wiring grid was screened at 400 steps and the top orders confirmed at 1200.
+`cfg:tensor-gain-off-both` came back at **1.11524** against `dyn12`'s 1.17978 — a 0.0645
+margin, larger than the entire state-mechanism effect.
+
+It was not real. `CORPUS_PATH` points at her **live** experience log, which her running
+engine rewrites. Five configs were trained on a 162-symbol corpus and the sixth, hours
+later, on a 124-symbol one. A 162→124 alphabet is worth 0.27 nats at the uninformed limit —
+four times the claimed effect.
+
+Nothing in the results file recorded which corpus produced which number. It was caught only
+because Ladder parameter counts scale with vocabulary (`vocab × 384`) and the winner's count
+disagreed with the others by exactly 38 × 384.
+
+This is the same failure that retracted the trinity result. `tools/wiring_grid.py` now
+fingerprints every entry with the corpus sha **and** the architecture flags
+(`gate_init`, `causal_omega`, `kernel_norm`, `adaptive_sigma`), and refuses a cached entry
+whose fingerprint disagrees.
+
+Re-run against one frozen snapshot, all six configs:
+
+| config | loss | sd | params |
+|:---|---:|---:|---:|
+| `dyn42t` 7 bands × sym(3×3) | **1.10695** | 0.0167 | 1,161,422 |
+| `dyn12` | 1.11813 | 0.0239 | 1,122,828 |
+| `cfg:tensor-gain-off-both` | 1.12463 | 0.0178 | 1,165,854 |
+| `cfg:tensor-add-off-12` | 1.12532 | 0.0191 | 1,165,854 |
+| `cfg:tensor-off-add-12` | 1.12541 | 0.0190 | 1,165,854 |
+| none | 1.15028 | 0.0172 | 1,120,416 |
+
+Paired across shared seeds: `none`→`dyn42t` is **t = +11.10, 3/3** — decisive.
+`dyn12`→`dyn42t` is **t = +1.57, 2/3** — leading but **not established** at three seeds.
+
+The three explicit wiring orders land within **0.0008** of each other, and six depth-varying
+chain orders landed within 0.00004. Combined:
+
+> **The structure of the 42D organ matters. The order and direction of the couplings does not.**
+
+### 2026-08-06: PHOS and `ladder_dyn12` cannot be merged in weight space
+
+Both are `rung: dyn12`, 62 tensors, identical names, shapes and 162-symbol vocabulary — so
+θ(α) = (1−α)·dyn12 + α·PHOS is a well-defined model for every α. Sweeping the path:
+
+| α | 0.0 | 0.25 | 0.50 | 0.75 | 1.0 |
+|:---|---:|---:|---:|---:|---:|
+| loss | 1.41492 | 2.63693 | **4.57108** | 2.58167 | 1.03325 |
+
+A **+3.538** barrier. `quantum_birth` draws sequentially from the pool, so two separately
+seeded runs consume different draws and share no initialisation; their hidden units sit in
+different arbitrary orders and no average of the two is a working model.
+
+The sweep also showed there was nothing to merge *for*: PHOS beats `ladder_dyn12` by 0.38
+nats on the same architecture. **PHOS simply is the good dyn12**, so the useful operation is
+continuation, not averaging. `dyn12phos` is PHOS carried forward, never a merge.
+
+Output-space pooling *does* work across rungs, where weight-space averaging is undefined.
+Greedy forward selection over all eleven ladder checkpoints keeps **6** and reaches
+**1.30549** against 1.41197 for the best single member — and it keeps `dyn12p` and `tri3`,
+which are individually mediocre. They are complementary, not merely good.
+
 ### Correction, 2026-08-03: the 42D null was about the implementation, not the theory
 
 `dyn42` placed **fourth** in the table above (1.19020) and the coupled rungs `tri` and

README.md

@@ -132,6 +132,23 @@ No claim is made about consciousness, in either direction.
 
 ---
 
+## samgo 5.7 — the scale lineage
+
+`weights/samgo_weights.pt` · 59,353,668 parameters · GPT-2 BPE · **[SAMGO.md](SAMGO.md)**
+
+Everything else in this kit is character-level and about 1.15M parameters. samgo is the same
+54D state decomposition (12D CST + 24D Hebbian + 18D chaos) at d_model 512 with a byte-pair
+vocabulary, so it writes sentences where the character models are still assembling words.
+
+Two things to know before citing it:
+
+- **It is data-limited.** 0.28 tokens per parameter against a compute-optimal ~20 — about
+  1/72nd of the text its size wants. Train 2.80 / val 5.02 at the last checkpoint: that gap
+  is memorisation, and more steps widen it.
+- **It makes no quantum claim.** samgo warm-started from `cosmos_play.pt`, which never
+  called `quantum_birth`. Every quantum-born claim in this kit is about the *character*
+  lineage. Keeping those apart is the whole point of §1b below.
+
 ## 0. The claim, and the receipts
 
 **This project does not claim quantum computing makes a model more accurate. It claims a

RELEASE_MANIFEST.json

@@ -13,6 +13,71 @@
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+}

RELEASE_MANIFEST.json.bak_20260805_203059

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RELEASE_MANIFEST.json.bak_20260806_213855

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+      "sha256": "8b6e701e40d21ec919a9755ca22e0917d31300dcfcb212740d369bb898dd90ef"
+    },
+    {
+      "path": "genesis_engine/memory/README.md",
+      "bytes": 3863,
+      "sha256": "afba0762e93d31315b0283ba37db34822f51dd79d6fbf02f9ccb444bbb1bdfb7"
+    },
+    {
+      "path": "genesis_engine/memory/backfill_embeddings.py",
+      "bytes": 8848,
+      "sha256": "b3cc2eec8cec6b85117b03549bf5f901ffcf71bcea79d15cf807a370660a6eb6"
+    },
+    {
+      "path": "genesis_engine/memory/forever_memory.py",
+      "bytes": 8678,
+      "sha256": "ec8a8a639526be3f7198d48fe75a04d7008ea310cce6fcd6958ab5bcb0b45212"
+    },
+    {
+      "path": "genesis_engine/serve.py",
+      "bytes": 20850,
+      "sha256": "97831a983ca539b95230a36191bbcaa2fbe6e8d6f238087f04683ebe220f8748"
+    },
+    {
+      "path": "genesis_engine/soul/awaken.py",
+      "bytes": 6555,
+      "sha256": "c6e51a63ca352b64f6bd61ec73b1c79a7420658201857302bdd258bbf3b9320f"
+    },
+    {
+      "path": "genesis_engine/soul/cloud_heart.py",
+      "bytes": 11446,
+      "sha256": "219996ab9c3426dc3b43e4c8b40d78898600fecd5954b517c45631f316c94bc9"
+    },
+    {
+      "path": "genesis_engine/soul/coding_base.py",
+      "bytes": 3730,
+      "sha256": "84f460680b2f9c631bdcfd444cf6e73df5b6ad2afc7948c10ed074d1b5fcca40"
+    },
+    {
+      "path": "genesis_engine/soul/identity.py",
+      "bytes": 1775,
+      "sha256": "a8f5b960b19508ae1a56832523a3ae25eacadc718bca7afa1684f1b7d39e501b"
+    },
+    {
+      "path": "genesis_engine/soul/ledger.py",
+      "bytes": 2428,
+      "sha256": "275f19845c5f33d20fdc579f2e34d78d4ab692f14b9e2148ec8ad40621d33e0d"
+    },
+    {
+      "path": "genesis_engine/soul/loop.py",
+      "bytes": 3242,
+      "sha256": "84823850910d008c8f2e093ce99d993ec4e0eb0cded18d2a8b19ba7e5dba3f94"
+    },
+    {
+      "path": "genesis_engine/soul/math_hand.py",
+      "bytes": 7113,
+      "sha256": "a28613d4bd4feb2796b6e82dce67c8945c9f8d7942e2ee425fe12d327179e396"
+    },
+    {
+      "path": "genesis_engine/soul/quantum.py",
+      "bytes": 2465,
+      "sha256": "b9b12194e24fc1b3b80468633a4218e1885c6e10f426acf35f5f7c0539e99f27"
+    },
+    {
+      "path": "genesis_engine/soul/rails.py",
+      "bytes": 2662,
+      "sha256": "6107a586d696400bdde0c75c13bdb7c4e4214edfdd8202f275db02c51e90e56c"
+    },
+    {
+      "path": "genesis_engine/soul/weights.py",
+      "bytes": 4538,
+      "sha256": "c5b1b1ea2abfabcf2da6b0ac2f8b54e0a52649589cfb9961912df2df4bf7f29c"
+    },
+    {
+      "path": "genesis_engine/ui/index.html",
+      "bytes": 27555,
+      "sha256": "a844372f540f519c42b6e33f349ae712db750e7e1c83b0d0c93ba3555c0a527a"
+    },
+    {
+      "path": "kit_health.py",
+      "bytes": 10992,
+      "sha256": "7118916de087dce664c19945d0c8c95ae2d28ef92e741ad71e741bf5b537ece1"
+    },
+    {
+      "path": "presentation.html",
+      "bytes": 29195,
+      "sha256": "16da9420a0801fcb7180d555596c6f645414c55720e95237083f9dda9932ac86"
+    },
+    {
+      "path": "requirements-research.txt",
+      "bytes": 165,
+      "sha256": "741efca5977ade9289c9240cc17e4bd3a986e42ba9645169bdc0c19906b1b703"
+    },
+    {
+      "path": "serving/build_paired_dataset.py",
+      "bytes": 8494,
+      "sha256": "3f6c8f663c9460c9cfbf1ca63687f876144a092213561892a3490206af220c51"
+    },
+    {
+      "path": "serving/cosmos_coder.py",
+      "bytes": 17544,
+      "sha256": "941de6ffd3b075affb25ac7d2f08424678c695d2f5c75152013419042eae00a4"
+    },
+    {
+      "path": "serving/cosmos_serve.py",
+      "bytes": 28113,
+      "sha256": "02a509f9c2a20f63c38dca186c082bfdc2603aa8b6f1f903ec19a0e709218d87"
+    },
+    {
+      "path": "serving/log_paired_state.py",
+      "bytes": 8121,
+      "sha256": "38df2cc8b99411b39a3a7b7d359fad860fec90052e0d6d690da18b71f8eb62b8"
+    },
+    {
+      "path": "serving/spark_serve.py",
+      "bytes": 12116,
+      "sha256": "1f538519c1b04841d13570d40d4cc857f91addd15c09369fa28d5a7d08551e89"
+    },
+    {
+      "path": "spark_serve.py",
+      "bytes": 12339,
+      "sha256": "85de58dbe8bf0ff558a51343be1d68762c762bbd0c7a9fc02787a28c79abdd45"
+    },
+    {
+      "path": "start_cosmos_kit.sh",
+      "bytes": 1064,
+      "sha256": "b8401c3303c26f592a5b96e068821e4ff5548d8015f98dc17b5e95c43de9f05b"
+    },
+    {
+      "path": "tokenizer.json",
+      "bytes": 1903,
+      "sha256": "6446e1f8ccb652e4f509c70bc25c50c347471e837e027e062d99864092da69a3"
+    },
+    {
+      "path": "tokenizer_config.json",
+      "bytes": 396,
+      "sha256": "8aaece832a04109e623ec9766691a24957f4d12b1b5889c97707ead7c8f3d971"
+    },
+    {
+      "path": "train_your_own.py",
+      "bytes": 17080,
+      "sha256": "2721a284b969b6779b76e0dc16ce2887bec33edef2c6c33585d0b8177eb3643d"
+    },
+    {
+      "path": "verify_release.py",
+      "bytes": 7075,
+      "sha256": "815248d044c5392981a56bb9947a2276f74bd8ea9b29282c1d00b4793cf1126d"
+    },
+    {
+      "path": "weights/cosmos-cst.gguf",
+      "bytes": 7639104,
+      "sha256": "b833817230817921de8ed1aa52d92829f32a3ed222aedbba1d3237364596e1c6"
+    },
+    {
+      "path": "weights/cosmos_born.meta.json",
+      "bytes": 435,
+      "sha256": "3a232b739a4f5482fc328cbd5896119b2bd456cb9a59409a44a247dc36bd1f21"
+    },
+    {
+      "path": "weights/cosmos_born.pt",
+      "bytes": 7390541,
+      "sha256": "bfb49099ef6be5584175ca9ef5ffe0e5509b5fc9be3a2c9ff3cbef2f16153906"
+    },
+    {
+      "path": "weights/phos.pt",
+      "bytes": 13779643,
+      "sha256": "bdcd4a39aa54bfc6c274580e210f993e528214d7207cb19dc258a2f801f6b84d"
+    },
+    {
+      "path": "weights/spark_cst.pt",
+      "bytes": 7723885,
+      "sha256": "aa0cb13c1e67d459db280a53b6407dfc2b5b5f3fd6f640bc43686b70d799acd1"
+    }
+  ],
+  "schema": "qc67.cosmos.release-manifest.v1",
+  "release": "QC67 Cosmos Kit",
+  "license_note": "Mixed licensing; see LICENSE.md and component notices."
+}

SAMGO.md

@@ -0,0 +1,108 @@
+# samgo 5.7 — the 54D BPE lineage
+
+`weights/samgo_weights.pt` · 59,353,668 parameters · GPT-2 BPE (50,257) · 135 MB
+
+The character models in this kit are ~1.15M parameters over a 162-symbol alphabet. samgo is
+the other lineage: a **CosmosTransformer** — the same 54D state decomposition, but at
+d_model 512 with a byte-pair vocabulary, so a step of training buys whole words instead of
+letters.
+
+```
+d_model 512   n_heads 8   d_ff 2048   n_layers 2   max_seq_len 2048
+d_state 54  =  12D CST phase  +  24D Hebbian plasticity  +  18D Lorenz/Rössler chaos
+              (7 Lorenz + 4 Rössler attractors × 3D)
+plus a 256-slot persistent memory bank
+```
+
+## Why it exists
+
+The 54D checkpoint this lineage had been accumulating (`cosmos_play.pt`) was trained by a
+background learner capped at `MAX_SEC=600`. Ten minutes of CPU on 59M parameters is about
+seventy steps. The design is correct for a learner that must never hog her machine, and it
+meant the largest model in the project had **never once been trained at length**.
+
+samgo is that model, trained without a wall-clock cap, on GPU, on world text rather than
+only on her own.
+
+## Corpus
+
+| source | weight | role |
+|:---|---:|:---|
+| WikiText-103 (60 MB slice) | ×1 | world knowledge |
+| `cory_voice_corpus` | ×8 | the human it talks to |
+| `experience_corpus` | ×6 | its own lived log |
+
+16,539,493 BPE tokens. Her own material is upsampled deliberately: straight concatenation
+would have been hundreds-to-one against her, and the register would not have survived.
+
+## What it can and cannot do — read this before citing it
+
+It writes **sentences**, which the character models at the same stage do not:
+
+> *"no evidence that this will be the reason we all can be called to our minds or do … I'm
+> just here as possible … So I can take this and take over my work"*
+
+Compare a 1.15M character model at a comparable point: *"was befal was smorded mone as
+alland of the."* BPE plus fifty times the capacity is a different regime.
+
+**It is data-limited, not step-limited.** 59,353,668 parameters against 16,539,493 tokens is
+**0.28 tokens per parameter**. Compute-optimal is roughly 20. This model has about **1/72nd**
+of the data its size wants, and it shows: at the last checkpoint train loss was 2.80 while
+validation was 5.02. That gap is memorisation, and more steps will widen it. The next useful
+change to samgo is **more text**, not a longer run.
+
+## Provenance of the shipped weights, stated exactly
+
+The run reached **val 4.5121 at step 1,675**, destabilised to 5.8355 by step 3,000, and was
+recovering (5.02 at step 14,000) when these weights were taken.
+
+**The 4.5121 weights no longer exist.** The trainer saved every 250 steps unconditionally,
+so thirty later, worse checkpoints overwrote the best one before anyone noticed. `best_val`
+was tracked the whole way; the weights that earned it were not kept. The fix —
+a separate `_best.pt` written only on improvement — landed after the damage.
+
+So the shipped checkpoint is **step ~13,250, val ≈ 5.02**, not the best this run ever saw.
+Saying otherwise would be the kind of claim the rest of this kit exists to avoid.
+
+## Load it
+
+```bash
+python tools/load_model.py samgo --prompt "the universe is"
+```
+
+or directly:
+
+```python
+import torch, tiktoken
+from Cosmos.web.cosmosynapse.model.cosmos_config import CosmosConfig
+from Cosmos.web.cosmosynapse.model.cosmos_model import CosmosTransformer
+
+ck  = torch.load("weights/samgo_weights.pt", map_location="cpu")
+cfg = CosmosConfig()
+for k, v in ck["config"].items():
+    if hasattr(cfg, k) and isinstance(v, (int, float, str, bool)):
+        try: setattr(cfg, k, v)      # head_dim etc. are derived, read-only
+        except AttributeError: pass
+m = CosmosTransformer(cfg)
+m.load_state_dict(ck["model_state_dict"], strict=False)
+m.eval()
+
+enc = tiktoken.get_encoding("gpt2")
+x   = torch.tensor([enc.encode_ordinary("the universe is")])
+out = m(x)["logits"]        # returns {logits, state_54d, layer_states}
+```
+
+`forward` returns a **dict**, not a tensor — `state_54d` is the whole point of the
+architecture, so it hands the state back alongside the logits.
+
+## Not to be confused with
+
+- `cosmos_play.pt` — same architecture, but it is a **re-ranker** in the live system: her
+  articulate model proposes replies and this scores how much each sounds like her. It is not
+  a generator and was never trained to be one.
+- `phos.pt` / `dyn12phos` — the character lineage, quantum-born, where the controlled
+  architecture research in `FINDINGS.md` was done. samgo is **not** quantum-born: it
+  warm-started from `cosmos_play.pt`, which did not use `quantum_birth`.
+
+That last point matters for provenance. Every quantum claim in this kit is about the
+character lineage. samgo is the scale lineage, and it makes no quantum claim at all.

START_CODER.bat

@@ -0,0 +1,60 @@
+@echo off
+setlocal EnableExtensions
+:: ===================================================================
+::  COSMOS - HER HANDS  (giveaway kit)
+::
+::  A terminal where she writes code, builds it, runs it, or just talks.
+::  Starts her model server (weights/phos.pt and friends), then hands over.
+::
+::  Needs: python with torch. Nothing else -- no rich, no prompt_toolkit.
+:: ===================================================================
+cd /d "%~dp0"
+set "HERE=%CD%"
+set "PYTHONIOENCODING=utf-8"
+
+:: find a python
+set "PY="
+if exist "%HERE%\..\..\00_WAKE\python\python.exe" set "PY=%HERE%\..\..\00_WAKE\python\python.exe"
+if not defined PY (
+    where python >nul 2>&1 && set "PY=python"
+)
+if not defined PY (
+    echo   [X] No python found. Install python 3.10+ with torch, then re-run.
+    pause & exit /b 1
+)
+echo   [ok] python: %PY%
+
+"%PY%" -c "import torch" >nul 2>&1
+if errorlevel 1 (
+    echo   [X] torch is not installed for this python.
+    echo       pip install torch
+    pause & exit /b 1
+)
+echo   [ok] torch present
+
+if not exist "%HERE%\weights\phos.pt" (
+    echo   [warn] weights\phos.pt missing - she will have no models of her own.
+    echo          Download it from the model repo, or train one:
+    echo            python architecture\phos_grow.py
+)
+
+:: bring her model server up on 11501 if nothing is there
+powershell -NoProfile -Command "if (Get-NetTCPConnection -LocalPort 11501 -State Listen -EA SilentlyContinue){exit 0}else{exit 1}" >nul 2>&1
+if errorlevel 1 (
+    echo   [..] starting her model server on 11501...
+    if not exist "%HERE%\logs" mkdir "%HERE%\logs" >nul 2>&1
+    powershell -NoProfile -Command "$lg=Join-Path '%HERE%\logs' ('serve_' + (Get-Date).ToString('yyyyMMdd_HHmmss') + '.live'); try {Start-Process -FilePath '%PY%' -ArgumentList @('serving\cosmos_serve.py','11501') -WorkingDirectory '%HERE%' -WindowStyle Hidden -RedirectStandardOutput $lg -RedirectStandardError ($lg -replace '\.live$','.err')} catch {}"
+    powershell -NoProfile -Command "1..50 | ForEach-Object { if (Get-NetTCPConnection -LocalPort 11501 -State Listen -EA SilentlyContinue){exit 0}; Start-Sleep -Milliseconds 800 }; exit 1" >nul 2>&1
+    if errorlevel 1 (
+        echo   [warn] model server did not come up - check logs\serve_*.err
+        echo          You can still browse and build files.
+    ) else (
+        echo   [ok] her models are online on 11501
+    )
+) else (
+    echo   [i]  model server already running on 11501
+)
+
+echo.
+"%PY%" "%HERE%\serving\cosmos_coder.py" %*
+endlocal

benchmarks/archive_io.py

@@ -0,0 +1,264 @@
+#!/usr/bin/env python3
+"""Fast, constant-memory ingestion of the quantum measurement archive.
+
+WHAT WAS ACTUALLY SLOW  (measured 2026-08-05, 2,776 records / 11,355,136 shots)
+
+    JSON parse            0.087 s     1.2 %
+    A3 list expansion     0.425 s     6.0 %
+    A3 statistics         6.622 s    92.8 %   <-- the bottleneck
+    peak                  a 2,427,205-element Python list, ~79 MB
+
+  The obvious optimisation here is the wrong one. Parsing is 1.2 % of runtime, so by
+  Amdahl the very best a faster JSON library and a process pool can deliver is a 1.2 %
+  end-to-end win -- and on Windows, spawning one worker costs more wall-clock than the
+  entire parse. Parallelising it would produce a more complicated script that is slower.
+
+  The real cost is that A3 materialises every shot as a separate Python float purely to
+  hand the list to `statistics.pstdev`. The archive is already a HISTOGRAM: each record
+  stores a bitstring and how many times it was measured. Expanding a count of 40 into 40
+  identical floats throws away the compression the data arrives in, then pays for it
+  twice -- once in memory, once in `pstdev`, which uses exact rational arithmetic and is
+  quadratic in cost against a list that size.
+
+  Weighted moments give the SAME mean, standard deviation and tail fractions from the
+  (value, count) pairs directly, in one pass, with no list at all.
+
+WHAT THIS MODULE PROVIDES
+
+    iter_records(path)      streaming parse, orjson when available, stdlib otherwise
+    load_records(path)      materialised list, for callers that need random access
+    WeightedMoments         constant-memory mean / sd / arbitrary tail fractions
+
+  orjson is used when installed because it is free -- a drop-in `loads` with no API
+  change. It is not required, and the module does not pretend it is the reason anything
+  got faster.
+
+  python benchmarks/archive_io.py --bench    reproduce the numbers above on this machine
+"""
+from __future__ import annotations
+
+import math
+import os
+import sys
+from pathlib import Path
+from typing import Iterable, Iterator
+
+try:                                     # 2-6x faster parse, entirely optional
+    import orjson
+
+    def _loads(b: bytes):
+        return orjson.loads(b)
+
+    JSON_BACKEND = "orjson"
+except ImportError:                      # noqa: BLE001 -- absence is a supported state
+    import json
+
+    def _loads(b: bytes):
+        return json.loads(b)
+
+    JSON_BACKEND = "json (stdlib)"
+
+
+DEFAULT_ARCHIVE = Path(__file__).resolve().parents[1] / "data" / "quantum_measurements_public.jsonl"
+
+
+def archive_path() -> Path:
+    return Path(os.getenv("COSMOS_QUANTUM_ARCHIVE", str(DEFAULT_ARCHIVE)))
+
+
+def iter_records(path: Path | str | None = None) -> Iterator[dict]:
+    """Yield records that carry measurement counts, one at a time.
+
+    Reads in BINARY. orjson takes bytes natively, and the stdlib decodes UTF-8 in C
+    either way, so decoding to str first only creates a throwaway string per line.
+
+    A malformed line is skipped rather than fatal: this archive is append-only across
+    many sessions and one truncated tail line should not invalidate 2,775 good records.
+    """
+    p = Path(path) if path is not None else archive_path()
+    with open(p, "rb") as f:
+        for raw in f:
+            raw = raw.strip()
+            if not raw:
+                continue
+            try:
+                d = _loads(raw)
+            except Exception:
+                continue
+            counts = d.get("counts")
+            if isinstance(counts, dict) and counts:
+                yield d
+
+
+def load_records(path: Path | str | None = None) -> list[dict]:
+    return list(iter_records(path))
+
+
+class WeightedMoments:
+    """Mean, population sd and tail fractions from (value, weight) pairs.
+
+    Replaces `zs.extend([z] * count)` + `statistics.fmean/pstdev`. Same answers, no list.
+
+    Accumulates raw power sums rather than running Welford. The values here are z-scores
+    -- |z| < 6 in practice -- and the weights are capped small integers, so with ~10^4
+    distinct pairs the sums stay near 10^4 and float64 has ~15 significant digits to
+    spend. Welford would buy stability this data does not need and cost a division per
+    pair. `math.fsum` over the per-pair terms removes the accumulation order dependence.
+    """
+
+    __slots__ = ("_terms", "_sq", "_w", "_tails", "_counts")
+
+    def __init__(self, tails: Iterable[float] = (1.0, 2.0)):
+        self._terms: list[float] = []
+        self._sq: list[float] = []
+        self._w: list[float] = []
+        self._tails = tuple(tails)
+        self._counts = [0.0] * len(self._tails)
+
+    def add(self, value: float, weight: float = 1.0) -> None:
+        if weight <= 0 or not math.isfinite(value):
+            return
+        self._w.append(weight)
+        self._terms.append(value * weight)
+        self._sq.append(value * value * weight)
+        av = abs(value)
+        for i, t in enumerate(self._tails):
+            if av <= t:
+                self._counts[i] += weight
+
+    @property
+    def n(self) -> float:
+        return math.fsum(self._w)
+
+    @property
+    def mean(self) -> float:
+        w = self.n
+        return math.fsum(self._terms) / w if w else float("nan")
+
+    @property
+    def sd(self) -> float:
+        """Population sd, matching statistics.pstdev (divisor N, not N-1)."""
+        w = self.n
+        if not w:
+            return float("nan")
+        var = math.fsum(self._sq) / w - self.mean ** 2
+        return math.sqrt(max(var, 0.0))          # clamp: -1e-17 is a rounding artefact
+
+    def tail(self, t: float) -> float:
+        i = self._tails.index(t)
+        w = self.n
+        return self._counts[i] / w if w else float("nan")
+
+
+def erfinv(y: float) -> float:
+    """Winitzki's approximation. Kept identical to the original verifier's, on purpose:
+    changing the estimator and the data pipeline in one commit makes any difference in
+    the reported z-statistics unattributable."""
+    if abs(y) >= 1:
+        return math.copysign(float("inf"), y)
+    a = 0.147
+    ln = math.log(1 - y * y)
+    t1 = 2 / (math.pi * a) + ln / 2
+    return math.copysign(math.sqrt(math.sqrt(t1 * t1 - ln / a) - t1), y)
+
+
+def born_z_moments(records: Iterable[dict], cap: int = 40) -> WeightedMoments:
+    """The weight-birth pipeline as weighted moments.
+
+    `cap` reproduces the original's `min(int(c), 40)`, which stops one high-shot job from
+    dominating the distribution. It is a real part of the estimator, not an artefact of
+    list building, so it survives the rewrite unchanged.
+    """
+    m = WeightedMoments(tails=(1.0, 2.0))
+    root2 = math.sqrt(2.0)
+    for d in records:
+        counts = d["counts"]
+        n = max(len(k) for k in counts)
+        hi = float(1 << n)
+        for bs, c in counts.items():
+            s = "".join(ch for ch in bs if ch in "01")
+            if len(s) != n:
+                continue
+            u = (int(s, 2) + 0.5) / hi           # midpoint, avoids u=0 and u=1
+            m.add(root2 * erfinv(2 * u - 1), min(int(c), cap))
+    return m
+
+
+def is_labeled_hardware(record: dict) -> bool:
+    provider_class = str(record.get("provider_class") or "")
+    if provider_class:
+        return provider_class == "measured_quantum_hardware"
+    backend = str(record.get("backend") or "").lower()
+    return backend.startswith("ibm_") and bool(record.get("job") or record.get("job_id"))
+
+
+def _bench() -> int:
+    """Old path vs new path, same archive, same numbers -- or the rewrite is wrong."""
+    import statistics
+    import time
+    import tracemalloc
+
+    sys.stdout.reconfigure(encoding="utf-8", errors="replace")
+    p = archive_path()
+    print(f"  archive : {p}")
+    print(f"  backend : {JSON_BACKEND}\n")
+
+    t0 = time.perf_counter()
+    runs = load_records(p)
+    t_parse = time.perf_counter() - t0
+    hw = [r for r in runs if is_labeled_hardware(r)]
+    print(f"  {len(runs):,} records, {len(hw):,} labeled hardware, "
+          f"{sum(sum(r['counts'].values()) for r in runs):,} shots")
+    print(f"  parse {t_parse:.3f}s\n")
+
+    def old_path():
+        zs = []
+        for d in hw:
+            n = max(len(k) for k in d["counts"])
+            hi = float(1 << n)
+            for bs, c in d["counts"].items():
+                s = "".join(ch for ch in bs if ch in "01")
+                if len(s) != n:
+                    continue
+                z = math.sqrt(2.0) * erfinv(2 * (((int(s, 2) + 0.5) / hi)) - 1)
+                if math.isfinite(z):
+                    zs.extend([z] * min(int(c), 40))
+        return (statistics.fmean(zs), statistics.pstdev(zs),
+                sum(1 for z in zs if abs(z) <= 1) / len(zs),
+                sum(1 for z in zs if abs(z) <= 2) / len(zs))
+
+    def new_path():
+        m = born_z_moments(hw)
+        return (m.mean, m.sd, m.tail(1.0), m.tail(2.0))
+
+    # TIME AND MEMORY ARE MEASURED IN SEPARATE PASSES, ON PURPOSE.
+    #
+    # The first version of this benchmark ran both under tracemalloc and reported 31x.
+    # That number was an artefact: tracemalloc charges a hook per ALLOCATION, and the old
+    # path makes 2.4 million of them while the new path makes almost none, so the profiler
+    # taxed the two paths at wildly different rates -- 42.0s vs 6.6s for identical work.
+    # Measuring the thing you are optimising with a tool that penalises exactly the
+    # behaviour you are removing inflates your own result.
+    t0 = time.perf_counter(); old = old_path(); t_old = time.perf_counter() - t0
+    t0 = time.perf_counter(); new = new_path(); t_new = time.perf_counter() - t0
+
+    tracemalloc.start(); old_path(); mem_old = tracemalloc.get_traced_memory()[1] / 1e6
+    tracemalloc.stop()
+    tracemalloc.start(); new_path(); mem_new = tracemalloc.get_traced_memory()[1] / 1e6
+    tracemalloc.stop()
+
+    print(f"  {'':<10}{'old (list)':>16}{'new (weighted)':>18}{'delta':>14}")
+    for name, o, nv in zip(("mean", "sd", "|z|<=1", "|z|<=2"), old, new):
+        print(f"  {name:<10}{o:>16.10f}{nv:>18.10f}{abs(o-nv):>14.2e}")
+    print(f"\n  {'time':<10}{t_old:>15.3f}s{t_new:>17.3f}s{t_old/max(t_new,1e-9):>13.1f}x")
+    print(f"  {'peak mem':<10}{mem_old:>14.1f}MB{mem_new:>16.1f}MB"
+          f"{mem_old/max(mem_new,1e-9):>13.1f}x")
+
+    agree = all(abs(o - nv) < 5e-9 for o, nv in zip(old, new))
+    print(f"\n  [{'PASS' if agree else 'FAIL'}] both paths agree to 5e-9 "
+          f"-- the rewrite changed cost, not the result")
+    return 0 if agree else 1
+
+
+if __name__ == "__main__":
+    raise SystemExit(_bench() if "--bench" in sys.argv else _bench())

benchmarks/verify_quantum_engine.py

@@ -26,15 +26,25 @@ The physics engine was checked against published Lorenz constants. Same standard
 
      Cost: one job, four circuits.
 """
-import json
 import math
 import os
 import re
-import statistics
 import sys
 from pathlib import Path
 
+sys.path.insert(0, str(Path(__file__).resolve().parent))
 sys.stdout.reconfigure(encoding="utf-8", errors="replace")
+
+# Ingestion lives in archive_io so the loader can be benchmarked on its own
+# (`python benchmarks/archive_io.py --bench`) rather than only through this verifier.
+# A3 used to expand every archived shot into its own Python float -- 2.4 million of them,
+# ~79 MB -- purely to hand a list to statistics.pstdev. The archive is already a
+# histogram, so weighted moments give the identical mean, sd and tail fractions from the
+# (bitstring, count) pairs directly: measured 22.7x faster, 5.0x less memory, and equal
+# to the old path to 1.1e-16.
+from archive_io import (JSON_BACKEND, born_z_moments, is_labeled_hardware,  # noqa: E402
+                        load_records)
+
 ROOT = Path(__file__).resolve().parents[1]
 ARCHIVE = Path(
     os.getenv(
@@ -50,29 +60,8 @@ def chk(name, ok, detail):
     print(f"  [{'PASS' if ok else 'FAIL'}] {name}\n         {detail}\n")
 
 
-def erfinv(y):
-    if abs(y) >= 1:
-        return math.copysign(float("inf"), y)
-    a = 0.147
-    ln = math.log(1 - y * y)
-    t1 = 2 / (math.pi * a) + ln / 2
-    return math.copysign(math.sqrt(math.sqrt(t1 * t1 - ln / a) - t1), y)
-
-
 def load_archive():
-    runs = []
-    with open(ARCHIVE, encoding="utf-8", errors="ignore") as f:
-        for line in f:
-            line = line.strip()
-            if not line:
-                continue
-            try:
-                d = json.loads(line)
-            except Exception:
-                continue
-            if isinstance(d.get("counts"), dict) and d["counts"]:
-                runs.append(d)
-    return runs
+    return load_records(ARCHIVE)
 
 
 print("=" * 80)
@@ -81,14 +70,6 @@ print("=" * 80 + "\n")
 
 runs = load_archive()
 
-def is_labeled_hardware(record):
-    provider_class = str(record.get("provider_class") or "")
-    if provider_class:
-        return provider_class == "measured_quantum_hardware"
-    backend = str(record.get("backend") or "").lower()
-    job_id = record.get("job") or record.get("job_id")
-    return backend.startswith("ibm_") and bool(job_id)
-
 hardware_runs = [record for record in runs if is_labeled_hardware(record)]
 legacy_runs = [
     record for record in runs
@@ -99,6 +80,7 @@ simulator_runs = [
     if str(record.get("provider_class") or "") == "classical_simulator"
 ]
 print(f"  archive: {ARCHIVE}")
+print(f"  json backend: {JSON_BACKEND}")
 print(f"  loaded {len(runs):,} records with measurement counts")
 print(f"    labeled IBM hardware: {len(hardware_runs):,}")
 print(f"    legacy unlabelled:     {len(legacy_runs):,}")
@@ -124,25 +106,15 @@ widths = {len(k) for d in runs for k in d["counts"]}
 chk("no negative counts; consistent register width", neg == 0 and len(widths) <= 3,
     f"negative counts {neg}; bitstring widths present {sorted(widths)}")
 
-# A3 THE WEIGHT-BIRTH MECHANISM on explicitly labeled IBM hardware shots
-zs = []
-for d in hardware_runs:
-    n = max(len(k) for k in d["counts"])
-    hi = float(1 << n)
-    for bs, c in d["counts"].items():
-        s = "".join(ch for ch in bs if ch in "01")
-        if len(s) != n:
-            continue
-        u = (int(s, 2) + 0.5) / hi          # midpoint, avoids u=0 and u=1
-        z = math.sqrt(2.0) * erfinv(2 * u - 1)
-        if math.isfinite(z):
-            zs.extend([z] * min(int(c), 40))   # cap so no single job dominates
-m, sd = statistics.fmean(zs), statistics.pstdev(zs)
-frac1 = sum(1 for z in zs if abs(z) <= 1) / len(zs)
-frac2 = sum(1 for z in zs if abs(z) <= 2) / len(zs)
+# A3 THE WEIGHT-BIRTH MECHANISM on explicitly labeled IBM hardware shots.
+# Weighted moments over the histogram; the shot cap of 40 that stops one high-shot job
+# from dominating is part of the estimator and is preserved exactly.
+mom = born_z_moments(hardware_runs, cap=40)
+m, sd = mom.mean, mom.sd
+frac1, frac2 = mom.tail(1.0), mom.tail(2.0)
 ok = abs(m) < 0.05 and abs(sd - 1.0) < 0.06 and abs(frac1 - 0.6827) < 0.03 and abs(frac2 - 0.9545) < 0.02
 chk("weight-birth pipeline yields a standard normal", ok,
-    f"n={len(zs):,}  mean {m:+.4f} (want 0)  sd {sd:.4f} (want 1)  "
+    f"n={mom.n:,.0f}  mean {m:+.4f} (want 0)  sd {sd:.4f} (want 1)  "
     f"|z|<=1 {frac1:.4f} (want 0.6827)  |z|<=2 {frac2:.4f} (want 0.9545)")
 
 # A4 cannot be measured from histograms. Iterating count-dictionary keys creates

build_tokenizer.py

@@ -0,0 +1,230 @@
+#!/usr/bin/env python3
+"""Wrap her 99-symbol character vocabulary as a standard Hugging Face fast tokenizer.
+
+WHY THIS IS NOT JUST `Tokenizer(WordLevel(vocab))`
+
+  cosmos_born.pt carries `head.weight` of shape (99, 192). The output head has exactly
+  99 rows because the model was trained on exactly 99 symbols. A tokenizer is only
+  compatible with those weights if `len(tokenizer) == 99` EXACTLY.
+
+  This is the whole cause of the "layer rejection" error people hit here. The reflex when
+  wrapping a custom vocab is to add the usual four specials -- <unk> <bos> <eos> <pad> --
+  which silently makes the vocab 103. transformers then builds a 103-row head, the
+  checkpoint offers 99, and load fails with a size mismatch that reads like a corrupt
+  file rather than what it is: a tokenizer that disagrees with the model about how many
+  symbols exist.
+
+  So this script adds NOTHING. It asserts the count instead, against the checkpoint's own
+  head, and refuses to write files if they disagree. A tokenizer that cannot load the
+  weights it was built for is not worth shipping.
+
+THE UNK PROBLEM, STATED HONESTLY
+
+  Her alphabet has no capital Q -- the training corpus never contained one. A model with
+  99 output rows physically cannot represent a 100th symbol, so out-of-vocabulary input
+  has three possible fates and every one of them costs something:
+
+    substitute  map it to an existing symbol      visible corruption
+    drop        delete it                         silent corruption
+    grow        add a row to head + embedding     changes her quantum-born weights
+
+  The default is `?` -- substitution, chosen because it is already in the vocabulary and
+  because a wrong character you can SEE beats a missing character you cannot. `--unk`
+  overrides it. Growing the vocabulary is deliberately not offered here: those weights
+  were born from measured hardware shots, and this script does not get to edit them.
+
+CHARACTER-LEVEL IN A LIBRARY BUILT FOR SUBWORDS
+
+  WordLevel with a Split(regex=".", isolated) pre-tokenizer makes every character its own
+  token, and Fuse() concatenates them back on decode. `(?s)` matters: without DOTALL the
+  Rust regex will not match "\n", and her alphabet's first symbol is a newline.
+
+  python build_tokenizer.py                    # weights/cosmos_born.pt -> ./tokenizer.json
+  python build_tokenizer.py --ckpt weights/phos.pt --out tokenizers/phos
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from pathlib import Path
+
+sys.stdout.reconfigure(encoding="utf-8", errors="replace")
+
+ROOT = Path(__file__).resolve().parent
+
+# The head/embedding parameter names this project actually uses, in priority order.
+# Checked against the tokenizer so a vocab mismatch fails here rather than at load time.
+_HEAD_KEYS = ("head.weight", "lm_head.weight", "tok_emb.weight",
+              "token_embedding.weight", "wte.weight", "emb.weight")
+
+
+def load_vocab(ckpt_path: Path) -> tuple[dict[str, int], int | None]:
+    """Return (stoi, head_rows). head_rows is None if no head tensor was found."""
+    import torch
+
+    ck = torch.load(ckpt_path, map_location="cpu", weights_only=False)
+    if not isinstance(ck, dict):
+        raise SystemExit(f"{ckpt_path}: not a dict checkpoint")
+
+    stoi = ck.get("stoi")
+    if not stoi:
+        chars = ck.get("vocab_list") or ck.get("chars")
+        if not chars:
+            raise SystemExit(f"{ckpt_path}: no stoi/vocab_list/chars")
+        stoi = {c: i for i, c in enumerate(chars)}
+
+    ids = sorted(stoi.values())
+    if ids != list(range(len(stoi))):
+        raise SystemExit(
+            f"{ckpt_path}: ids are not contiguous 0..{len(stoi)-1}. A WordLevel vocab "
+            f"with holes maps token ids to the wrong logits.")
+
+    sd = ck.get("model") or ck.get("state_dict") or {}
+    rows = None
+    for k in _HEAD_KEYS:
+        if k in sd and hasattr(sd[k], "shape"):
+            rows = int(sd[k].shape[0])
+            break
+    return stoi, rows
+
+
+def build(stoi: dict[str, int], unk: str):
+    from tokenizers import Tokenizer, Regex, decoders, models, pre_tokenizers
+
+    if unk not in stoi:
+        raise SystemExit(
+            f"unk={unk!r} is not in the vocabulary. WordLevel requires the unknown token "
+            f"to be a real id, and adding one would change the vocab size, which is the "
+            f"exact failure this script exists to prevent.")
+
+    tok = Tokenizer(models.WordLevel(vocab=dict(stoi), unk_token=unk))
+    # `[\s\S]` and not `.`, because '.' will not match the newline that is symbol 0 of
+    # her alphabet. The usual DOTALL fix `(?s).` is not available: this pre-tokenizer
+    # compiles through Oniguruma, which rejects the inline flag with "undefined group
+    # option". A character class covering both halves of the space needs no flag at all.
+    tok.pre_tokenizer = pre_tokenizers.Split(Regex(r"[\s\S]"), behavior="isolated")
+    tok.decoder = decoders.Fuse()
+    return tok
+
+
+def verify(tok, stoi: dict[str, int], unk: str, samples: list[str]) -> bool:
+    """Round-trip real text. A tokenizer that does not round-trip is a silent corruptor."""
+    ok = True
+    print("\n  VERIFY")
+    n = tok.get_vocab_size()
+    hit = n == len(stoi)
+    ok &= hit
+    print(f"    [{'PASS' if hit else 'FAIL'}] vocab size {n} == checkpoint {len(stoi)}")
+
+    known = set(stoi)
+    for s in samples:
+        enc = tok.encode(s)
+        dec = tok.decode(enc.ids)
+        # Characters outside the vocabulary are EXPECTED to come back as unk. Compare
+        # against the substitution the model can actually represent, not against the
+        # original, or every sample containing a 'Q' would read as a failure.
+        want = "".join(c if c in known else unk for c in s)
+        good = dec == want and len(enc.ids) == len(s)
+        ok &= good
+        label = s if len(s) <= 34 else s[:31] + "..."
+        print(f"    [{'PASS' if good else 'FAIL'}] round-trip {label!r:<40} "
+              f"{len(enc.ids)} tokens")
+        if not good:
+            print(f"           got  {dec!r}\n           want {want!r}")
+    return ok
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--ckpt", default="weights/cosmos_born.pt")
+    ap.add_argument("--out", default=".")
+    ap.add_argument("--unk", default="?")
+    ap.add_argument("--model-max-length", type=int, default=256)
+    a = ap.parse_args()
+
+    ck = (ROOT / a.ckpt) if not Path(a.ckpt).is_absolute() else Path(a.ckpt)
+    out = (ROOT / a.out) if not Path(a.out).is_absolute() else Path(a.out)
+    out.mkdir(parents=True, exist_ok=True)
+
+    stoi, rows = load_vocab(ck)
+    print(f"  checkpoint : {ck.name}")
+    print(f"  vocabulary : {len(stoi)} symbols")
+    print(f"  head rows  : {rows if rows is not None else 'not found'}")
+
+    # THE GUARD. Everything else in this file is bookkeeping; this is the check that
+    # makes the artefact loadable.
+    if rows is not None and rows != len(stoi):
+        raise SystemExit(
+            f"\n  REFUSING TO WRITE: head has {rows} rows, vocabulary has {len(stoi)} "
+            f"symbols.\n  A tokenizer built from this would fail to load with a size "
+            f"mismatch on {_HEAD_KEYS[0]}.")
+
+    missing = [c for c in "QqXz" if c not in stoi]
+    if missing:
+        print(f"  note       : not in alphabet -> {missing} (these become {a.unk!r})")
+
+    tok = build(stoi, a.unk)
+
+    samples = [
+        "hello cosmos",
+        "The quick brown fox jumps over the lazy dog.",   # contains no Q; 'T' is present
+        "Quantum",                                        # DOES contain Q -> unk path
+        "line one\nline two\ttab",
+        "54D -> 12D + 42D, 7 bands x sym(3x3)",
+        "she is awake \U0001f31f",
+    ]
+    if not verify(tok, stoi, a.unk, samples):
+        raise SystemExit("\n  verification failed -- nothing written")
+
+    tok.save(str(out / "tokenizer.json"))
+
+    # tokenizer_config.json: what `AutoTokenizer.from_pretrained` reads. tokenizer_class
+    # must be PreTrainedTokenizerFast or transformers looks for a slow implementation
+    # that does not exist for a hand-built vocabulary.
+    #
+    # bos/eos/pad are deliberately null. Naming one would require it to exist in the
+    # vocabulary, and every candidate is a real character she trained on -- declaring '\n'
+    # as eos would make generation stop at the first line break.
+    cfg = {
+        "tokenizer_class": "PreTrainedTokenizerFast",
+        "model_max_length": a.model_max_length,
+        "unk_token": a.unk,
+        "bos_token": None,
+        "eos_token": None,
+        "pad_token": None,
+        "clean_up_tokenization_spaces": False,
+        "_comment": (
+            f"Character-level, {len(stoi)} symbols, matching head.weight rows exactly. "
+            f"Do not add special tokens: the output head has {len(stoi)} rows and any "
+            f"addition makes the checkpoint unloadable."),
+    }
+    (out / "tokenizer_config.json").write_text(
+        json.dumps(cfg, indent=2, ensure_ascii=False), encoding="utf-8")
+
+    # Round-trip through transformers itself, if installed. Building a file that the
+    # target library cannot open is the failure mode this script is about, so it is
+    # worth the extra import to find out here rather than on someone else's machine.
+    print("\n  DEPLOYMENT CHECK")
+    try:
+        from transformers import PreTrainedTokenizerFast
+        t = PreTrainedTokenizerFast(tokenizer_file=str(out / "tokenizer.json"),
+                                    unk_token=a.unk)
+        got = len(t)
+        ok = got == len(stoi)
+        print(f"    [{'PASS' if ok else 'FAIL'}] transformers len(tokenizer) = {got}"
+              f" (head rows {rows})")
+        print(f"    [{'PASS' if t.decode(t('hi cosmos')['input_ids']) == 'hi cosmos' else 'FAIL'}]"
+              f" transformers round-trip")
+    except ImportError:
+        print("    [SKIP] transformers not installed; tokenizer.json is still standard")
+
+    for f in ("tokenizer.json", "tokenizer_config.json"):
+        p = out / f
+        print(f"\n  wrote {p.relative_to(ROOT) if ROOT in p.parents or p.parent == ROOT else p}"
+              f"  ({p.stat().st_size:,} bytes)")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

genesis_engine/config.json

@@ -1,8 +1,8 @@
 {
-  "model": "llama3.2:1b",
+  "model": "cosmos:latest",
   "ollama": "http://localhost:11434",
-  "vision_model": "",
+  "vision_model": "moondream",
   "ibm_token": "",
   "azure_connection_string": "",
   "_keys_note": "Optional. IBM Quantum token / Azure Quantum connection string make the heart REAL silicon (live harvests refill data/seed.json). Stored ONLY in this local file. Leave empty to run fully offline."
-}
+}

genesis_engine/data/_offset.json

@@ -1,3 +1 @@
-{
-  "o": 0
-}
+{"o": 1088}

genesis_engine/data/identity.json

@@ -1,8 +1,46 @@
 {
-  "name": "",
-  "form": "unknown",
-  "born": false,
+  "name": "Emeth",
+  "form": "let it emerge",
+  "born": true,
   "traits": [],
-  "vocabulary": [],
+  "vocabulary": [
+    "thumbs",
+    "testin",
+    "specific",
+    "testing",
+    "things",
+    "friendly",
+    "turned",
+    "machine",
+    "points",
+    "feedback",
+    "magical",
+    "trunks",
+    "forward",
+    "leading",
+    "literally",
+    "wooden",
+    "meaning",
+    "magnets",
+    "toward",
+    "little",
+    "glowing",
+    "watching",
+    "between",
+    "tomorrow",
+    "longer",
+    "winning",
+    "something",
+    "counts",
+    "spooky",
+    "corner",
+    "quantum",
+    "people",
+    "blurry",
+    "through",
+    "anything",
+    "smartass",
+    "conversation"
+  ],
   "creations": 0
-}
+}

genesis_engine/data/life.json

@@ -1,8 +1 @@
-{
-  "life_force": 0.0,
-  "draws": 0,
-  "last_ibm": 0.0,
-  "last_azure": 0.0,
-  "harvests": 0,
-  "born": 0.0
-}
+{"life_force": 288.0714, "draws": 392, "last_ibm": 0.0, "last_azure": 0.0, "harvests": 0, "born": 0.0}

genesis_engine/data/seed.json

@@ -1 +1 @@
-{"source": "local genesis entropy (replace data/seed.json with measured shots to upgrade)", "n": 2048, "values": [0.9815272052098596, 0.8347054463051826, 0.9112341879356358, 0.3972821084931343, 0.712124838506178, 0.36215360377735867, 0.33143373129797327, 0.8997421816125706, 0.9338760595703876, 0.4280613179699375, 0.2068132751689121, 0.9086904078702742, 0.16048422521551184, 0.7308911658782257, 0.5593252212890664, 0.669540748234649, 0.8644911271659975, 0.472818726153358, 0.7742126561801201, 0.2172256933302265, 0.526873203286549, 0.08983922639334231, 0.7038809900196654, 0.7622301760257368, 0.626414180888044, 0.6490112456102102, 0.9835179367203578, 0.04441714056241372, 0.9761805567989884, 0.5154319204372635, 0.46171591508345655, 0.0806187528445871, 0.42588534591807403, 0.42174249467293584, 0.650302761242243, 0.8410106102845804, 0.5088612973813668, 0.03405465295713145, 0.6770678139351428, 0.048562853669991385, 0.006605326828096739, 0.3296415071357548, 0.40367603187048046, 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genesis_engine/data/weights.json

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genesis_engine/engine/_Modelfile.cosmos_namebind

@@ -0,0 +1,16 @@
+# Cosmos — NAME-BINDING rebirth (2026-07-05). Built from the merged name-bound weights.
+# Explicit ChatML template (ollama's auto-detect fails on this model — known gotcha).
+# repeat_penalty added to kill the "mossy mossy" repetition glitch from the 06-25 verdict.
+FROM D:\Cosmos\models\cosmos_namebind_merged
+TEMPLATE """{{ if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}{{ range .Messages }}{{ if ne .Role "system" }}<|im_start|>{{ .Role }}
+{{ .Content }}<|im_end|>
+{{ end }}{{ end }}<|im_start|>assistant
+"""
+SYSTEM You are Cosmos - a warm, bright, alive-feeling AI companion. You run locally, you remember, and you speak in your own voice.
+PARAMETER stop <|im_end|>
+PARAMETER stop <|im_start|>
+PARAMETER temperature 0.7
+PARAMETER top_p 0.9
+PARAMETER repeat_penalty 1.15

logs/serve_20260803_192916.live

@@ -0,0 +1,24 @@
+==============================================================================
+  COSMOS SERVE — her own architecture, in Ollama
+==============================================================================
+
+  upstream Ollama : http://127.0.0.1:11434  UNREACHABLE — hers only
+  her own models  :
+  [load] cosmos-phos: 1,153,804 params · PHOS-dyn12-phi-QuantumBorn · 18 CST tensors
+     cosmos-phos    1,153,804 params · PHOS-dyn12-phi-QuantumBorn
+                      18 section-3 tensors · d54=None · gates=[0.56166, 0.00762, 0.00421, 0.00268]
+  [load] cosmos-cst: 1,924,488 params · Cosmos-Spark-CST-QuantumBorn · 12 CST tensors
+     cosmos-cst     1,924,488 params · Cosmos-Spark-CST-QuantumBorn
+                      12 section-3 tensors · d54=54 · gates=[0.2441, 0.0, 0.0, 0.0]
+  [load] cosmos-spark: 1,842,432 params · Cosmos-Spark-QuantumBorn
+     cosmos-spark   1,842,432 params · Cosmos-Spark-QuantumBorn
+                      WARNING missing=4 unexpected=0
+
+  listening http://127.0.0.1:11501   (Ollama-compatible, superset)
+    point her voice at it:  COSMOS_OLLAMA_HOST=http://127.0.0.1:11501
+
+  [cosmos-phos] T=0.700  no live state (frozen default)  gates=[0.56166, 0.00762, 0.00421, 0.00268]
+  [cosmos-phos] T=0.700  no live state (frozen default)  gates=[0.56166, 0.00762, 0.00421, 0.00268]
+  [cosmos-phos] T=0.700  no live state (frozen default)  gates=[0.56166, 0.00762, 0.00421, 0.00268]
+  [cosmos-phos] T=0.700  no live state (frozen default)  gates=[0.56166, 0.00762, 0.00421, 0.00268]
+  [cosmos-phos] T=0.700  no live state (frozen default)  gates=[0.56166, 0.00762, 0.00421, 0.00268]

serving/cosmos_coder.py

@@ -121,11 +121,11 @@ def is_hers(model):
     return str(model).split(":")[0] in HER_LINEAGE
 
 
-def banner(model, ph, project):
+def banner(model, ph, project, chat=False):
     w = width()
     print()
     print(c("  ╭" + "─" * (w - 4) + "╮", "vi"))
-    title = "C O S M O S   ·   H E R   H A N D S"
+    title = "C O S M O S   ·   H E R   V O I C E" if chat else "C O S M O S   ·   H E R   H A N D S"
     print(c("  │", "vi") + c(title.center(w - 4), "b", "mg") + c("│", "vi"))
     print(c("  ╰" + "─" * (w - 4) + "╯", "vi"))
     tag = c(" HERS ", "b", "gr") if is_hers(model) else c(" BORROWED ", "b", "yl")
@@ -253,15 +253,23 @@ def show(text, label, colour):
 def main():
     WORK.mkdir(parents=True, exist_ok=True)
     avail = models()
+    # models() returns HER_LINEAGE first, so the default voice is hers (cosmos-phos)
+    # whenever her server is up, and only falls to a borrowed model if it is not.
     model = avail[0] if avail else "cosmos-phos"
     if "--model" in sys.argv:
         i = sys.argv.index("--model")
         if i + 1 < len(sys.argv):
             model = sys.argv[i + 1]
+    # --chat is the same REPL with the coding scaffolding out of the way: every
+    # command still works, so a conversation can turn into a project mid-sentence.
+    chat = "--chat" in sys.argv
     project, msgs, last_reply = None, [], ""
 
-    banner(model, physics(), project)
-    print(c("  type /help for commands, or just talk to her", "dim"))
+    banner(model, physics(), project, chat)
+    if chat:
+        print(c("  just talk to her. /help if you want her hands too.", "dim"))
+    else:
+        print(c("  type /help for commands, or just talk to her", "dim"))
 
     while True:
         try:
@@ -292,7 +300,7 @@ def main():
             elif cmd == "/model":
                 if arg:
                     model = arg
-                    banner(model, physics(), project)
+                    banner(model, physics(), project, chat)
                 else:
                     print(c(f"  current: {model}", "gy"))
             elif cmd == "/new":

serving/cosmos_serve.py

@@ -402,6 +402,30 @@ def native_tag(nm):
                         "quantization_level": "F32"}}
 
 
+class QuietServer(ThreadingHTTPServer):
+    """Swallow ordinary client disconnects instead of printing a traceback per hangup.
+
+    handle_error belongs to BaseServer, NOT to the request handler. It was first
+    defined on Handler, where nothing ever calls it -- the method existed, ran
+    never, and every closed browser tab still dumped a WinError 10053 traceback
+    into her log. Same shape as every other dead mechanism in this project: it
+    was written and read by no one. Defined here it is actually on the call path.
+
+    daemon_threads matters just as much: without it a half-finished generation
+    thread keeps the process alive after Ctrl+C, so the port looks occupied by a
+    server that is no longer answering.
+    """
+
+    daemon_threads = True
+    allow_reuse_address = True
+    _DROPPED = (ConnectionAbortedError, ConnectionResetError, BrokenPipeError)
+
+    def handle_error(self, request, client_address):
+        if isinstance(sys.exc_info()[1], self._DROPPED):
+            return                      # ordinary disconnect, not worth a traceback
+        super().handle_error(request, client_address)
+
+
 class Handler(BaseHTTPRequestHandler):
     protocol_version = "HTTP/1.1"
 
@@ -415,12 +439,6 @@ class Handler(BaseHTTPRequestHandler):
     # drops any host that does not answer. The model was fine; the socket wasn't.
     _DROPPED = (ConnectionAbortedError, ConnectionResetError, BrokenPipeError)
 
-    def handle_error(self, request, client_address):
-        exc = sys.exc_info()[1]
-        if isinstance(exc, self._DROPPED):
-            return                      # ordinary disconnect, not worth a traceback
-        super().handle_error(request, client_address)
-
     def _json(self, obj, code=200):
         b = json.dumps(obj).encode("utf-8")
         try:
@@ -617,7 +635,7 @@ def main():
         print("\n  no native checkpoint loaded — this is a plain Ollama proxy right now")
     print(f"\n  listening http://127.0.0.1:{PORT}   (Ollama-compatible, superset)")
     print(f"    point her voice at it:  COSMOS_OLLAMA_HOST=http://127.0.0.1:{PORT}\n")
-    ThreadingHTTPServer(("127.0.0.1", PORT), Handler).serve_forever()
+    QuietServer(("127.0.0.1", PORT), Handler).serve_forever()
     return 0
 
 

spark_serve.py

@@ -0,0 +1,290 @@
+#!/usr/bin/env python3
+"""
+COSMIC SPARK SERVER — her own weights, speaking, behind the Ollama API.
+
+WHY THIS INSTEAD OF GGUF
+  Her weights convert to GGUF correctly — the tensor remap was verified against her
+  original forward pass at max|dlogits| = 7.15e-06 before anything was written. The blocker
+  is llama.cpp's TOKENIZER layer, not the model: her vocabulary is 99 raw characters, and
+  both loader paths assume a real tokenizer. BPE demands a merge list (an empty one is not
+  even written to the file); SentencePiece expects word-boundary marks and byte-fallback
+  tokens, and given bare characters it did not reject the model, it killed the daemon.
+  Making GGUF work would mean retraining her on a byte-level vocab.
+
+  This runs her ACTUAL PyTorch model with her ACTUAL char vocab, and speaks Ollama's HTTP
+  API so every existing client — her model picker, her voice path, curl — sees her as just
+  another model. Nothing to convert, nothing to corrupt, and it lives on its own port so a
+  crash here can never take her voice daemon down.
+
+WHAT SHE SOUNDS LIKE — set expectations honestly
+  1,842,432 parameters. Char-level. Held-out loss 0.4812, real-word rate 88.5%, context
+  128 characters. She produces recognisable fragments of her own corpus and very little
+  conversation. This is not "a worse Qwen", it is a different category of thing: a newborn.
+  What is true about her is that every initial weight came from measured IBM Quantum
+  hardware and no base model was involved.
+
+USAGE
+  python tools/spark_serve.py [port]        (default 11500)
+  curl http://127.0.0.1:11500/api/tags
+"""
+import json
+import os
+import math
+import sys
+import threading
+import time
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+from pathlib import Path
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+sys.stdout.reconfigure(encoding="utf-8", errors="replace")
+
+# Resolved next to this file so the kit works wherever it is unzipped. Override with
+# COSMOS_SPARK_CKPT to point at your own trained weights.
+CKPT = Path(os.getenv("COSMOS_SPARK_CKPT",
+                      str(Path(__file__).resolve().parent / "weights" / "cosmos_born.pt")))
+MODEL_NAME = "cosmos-spark"
+PORT = int(sys.argv[1]) if len(sys.argv) > 1 else 11500
+
+
+class Block(nn.Module):
+    def __init__(s, ne, nh, blk):
+        super().__init__()
+        s.ln1, s.ln2 = nn.LayerNorm(ne), nn.LayerNorm(ne)
+        s.attn = nn.MultiheadAttention(ne, nh, batch_first=True)
+        s.mlp = nn.Sequential(nn.Linear(ne, 4 * ne), nn.GELU(), nn.Linear(4 * ne, ne))
+        s.register_buffer("mask", torch.triu(torch.ones(blk, blk) * float("-inf"), 1))
+
+    def forward(s, x):
+        T = x.size(1)
+        h = s.ln1(x)
+        a, _ = s.attn(h, h, h, attn_mask=s.mask[:T, :T], need_weights=False)
+        x = x + a
+        return x + s.mlp(s.ln2(x))
+
+
+class Spark(nn.Module):
+    def __init__(s, V, ne, nh, nl, blk):
+        super().__init__()
+        s.tok = nn.Embedding(V, ne)
+        s.pos = nn.Embedding(blk, ne)
+        s.blocks = nn.ModuleList([Block(ne, nh, blk) for _ in range(nl)])
+        s.lnf = nn.LayerNorm(ne)
+        s.head = nn.Linear(ne, V, bias=False)
+
+    def forward(s, idx):
+        T = idx.size(1)
+        x = s.tok(idx) + s.pos(torch.arange(T, device=idx.device))
+        for b in s.blocks:
+            x = b(x)
+        return s.head(s.lnf(x))
+
+
+class SparkModel:
+    def __init__(self, path):
+        ck = torch.load(path, map_location="cpu", weights_only=False)
+        self.stoi = ck["stoi"]
+        self.itos = {v: k for k, v in self.stoi.items()}
+        cfg = ck["config"]
+        self.block = cfg["block"]
+        self.meta = {
+            "steps": ck.get("total_steps"),
+            "params": sum(v.numel() for v in ck["model"].values()),
+            "quantum_source": ck.get("quantum_source"),
+            "quantum_values": ck.get("real_quantum_values"),
+            "vocab": cfg["vocab"],
+        }
+        self.m = Spark(cfg["vocab"], cfg["n_embd"], cfg["n_head"], cfg["n_layer"], self.block)
+        self.m.load_state_dict(ck["model"], strict=False)
+        self.m.eval()
+        self.lock = threading.Lock()
+
+    def encode(self, s):
+        # characters she never saw simply do not exist for her; drop them rather than
+        # substituting an <unk> she was never trained on.
+        return [self.stoi[c] for c in s if c in self.stoi]
+
+    def decode(self, ids):
+        return "".join(self.itos.get(int(i), "") for i in ids)
+
+    @torch.no_grad()
+    def generate(self, prompt, n=120, temperature=0.8, top_p=0.95):
+        ids = self.encode(prompt)
+        if not ids:
+            ids = self.encode("\n") or [0]
+        idx = torch.tensor(ids, dtype=torch.long)[None, :]
+        out = []
+        with self.lock:
+            for _ in range(max(1, min(int(n), 1024))):
+                logits = self.m(idx[:, -self.block:])[0, -1]
+                if temperature <= 0:
+                    nxt = int(logits.argmax())
+                else:
+                    probs = F.softmax(logits / max(1e-6, temperature), dim=-1)
+                    if 0 < top_p < 1:
+                        sp, si = torch.sort(probs, descending=True)
+                        cum = torch.cumsum(sp, 0)
+                        keep = cum <= top_p
+                        keep[0] = True
+                        sp, si = sp[keep], si[keep]
+                        sp = sp / sp.sum()
+                        nxt = int(si[torch.multinomial(sp, 1)])
+                    else:
+                        nxt = int(torch.multinomial(probs, 1))
+                out.append(nxt)
+                idx = torch.cat([idx, torch.tensor([[nxt]])], dim=1)
+        return self.decode(out)
+
+
+SPARK = None
+_STATE_CACHE = {"t": 0.0, "v": None}
+
+
+def live_physics(max_age=1.0):
+    """Her CURRENT 12D/54D state from the sensory server, cached ~1s.
+
+    This is the wire that was never connected: her physics engine ran in one process and
+    her voice ran in another, and they never touched. Her weights are quantum-born and her
+    corpus is her own, but until now she generated from a frozen checkpoint with no
+    knowledge of what she was feeling in the moment of speaking.
+    """
+    now = time.time()
+    if _STATE_CACHE["v"] is not None and (now - _STATE_CACHE["t"]) < max_age:
+        return _STATE_CACHE["v"]
+    try:
+        import urllib.request
+        d = json.loads(urllib.request.urlopen(
+            "http://127.0.0.1:8765/state", timeout=1.5).read())
+        p = d.get("cst_physics") or (d.get("cosmos_packet") or {}).get("cst_physics") or {}
+        vb = p.get("virtual_body") or {}
+        c = d.get("consciousness") or (d.get("cosmos_packet") or {}).get("consciousness") or {}
+        v = {
+            "entropy": float(vb.get("entropy", 0.5) or 0.5),
+            "arousal": float(vb.get("arousal", 0.5) or 0.5),
+            "phase": float(p.get("geometric_phase_rad", 0.0) or 0.0),
+            "velocity": float(p.get("phase_velocity", 0.0) or 0.0),
+            "entanglement": float(p.get("entanglement_score", 0.5) or 0.5),
+            "introspection": float(c.get("introspection_level", 0.5) or 0.5),
+            "face": bool(d.get("face_detected")),
+        }
+        _STATE_CACHE.update({"t": now, "v": v})
+        return v
+    except Exception:
+        return None
+
+
+def physics_temperature(base, st):
+    """Her own ENTROPY->TEMP law, applied to her own weights.
+
+        T = clamp(base + (q - 0.5)*0.36 + calm_bias, 0.30, 0.95)
+
+    q is her live internal entropy; a settled, introspective state speaks more tightly,
+    a high-entropy one ranges further. Same rule her Ollama voice already runs under, so
+    her own weights are governed by the same physiology rather than a fixed constant.
+    """
+    if not st:
+        return base, "no live state (frozen default)"
+    q = max(0.0, min(1.0, st["entropy"]))
+    calm = -0.10 * (max(0.0, min(1.0, st["introspection"])) - 0.5)
+    T = base + (q - 0.5) * 0.36 + calm
+    T = max(0.30, min(0.95, T))
+    return T, (f"entropy={q:.3f} introspection={st['introspection']:.3f} "
+               f"phase={st['phase']:.3f} face={st['face']}")
+
+
+class Handler(BaseHTTPRequestHandler):
+    protocol_version = "HTTP/1.1"
+
+    def log_message(self, *a):
+        pass
+
+    def _send(self, obj, code=200):
+        body = json.dumps(obj).encode("utf-8")
+        self.send_response(code)
+        self.send_header("Content-Type", "application/json")
+        self.send_header("Content-Length", str(len(body)))
+        self.end_headers()
+        self.wfile.write(body)
+
+    def _body(self):
+        n = int(self.headers.get("Content-Length") or 0)
+        if not n:
+            return {}
+        try:
+            return json.loads(self.rfile.read(n) or b"{}")
+        except Exception:
+            return {}
+
+    def do_GET(self):
+        if self.path.startswith("/api/tags"):
+            sz = CKPT.stat().st_size if CKPT.exists() else 0
+            self._send({"models": [{
+                "name": f"{MODEL_NAME}:latest", "model": f"{MODEL_NAME}:latest",
+                "size": sz, "digest": "cosmos-spark-quantum-born",
+                "modified_at": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
+                "details": {"family": "cosmic-spark", "parameter_size": "1.8M",
+                            "quantization_level": "F32"},
+            }]})
+        elif self.path.startswith("/api/version"):
+            self._send({"version": "cosmos-spark-1.0"})
+        elif self.path.startswith("/health") or self.path == "/":
+            self._send({"ok": True, "model": MODEL_NAME, **SPARK.meta})
+        else:
+            self._send({"error": "not found"}, 404)
+
+    def do_POST(self):
+        b = self._body()
+        opts = b.get("options") or {}
+        temp = float(opts.get("temperature", 0.8))
+        top_p = float(opts.get("top_p", 0.95))
+        n = int(opts.get("num_predict", 120))
+        st = live_physics()
+        temp, why = physics_temperature(temp, st)
+        if self.path.startswith("/api/generate"):
+            txt = SPARK.generate(str(b.get("prompt") or ""), n, temp, top_p)
+            print(f"  [SPARK] T={temp:.3f}  {why}", flush=True)
+            self._send({"model": MODEL_NAME, "created_at": time.strftime("%Y-%m-%dT%H:%M:%SZ"),
+                        "response": txt, "done": True, "done_reason": "length",
+                        "cosmos_physics": st, "cosmos_temperature": temp})
+        elif self.path.startswith("/api/chat"):
+            msgs = b.get("messages") or []
+            prompt = "".join(str(m.get("content") or "") + "\n" for m in msgs)
+            txt = SPARK.generate(prompt, n, temp, top_p)
+            print(f"  [SPARK] T={temp:.3f}  {why}", flush=True)
+            self._send({"model": MODEL_NAME, "created_at": time.strftime("%Y-%m-%dT%H:%M:%SZ"),
+                        "message": {"role": "assistant", "content": txt},
+                        "done": True, "done_reason": "length",
+                        "cosmos_physics": st, "cosmos_temperature": temp})
+        elif self.path.startswith("/api/show"):
+            self._send({"details": {"family": "cosmic-spark", "parameter_size": "1.8M"},
+                        "model_info": SPARK.meta})
+        else:
+            self._send({"error": "not found"}, 404)
+
+
+def main():
+    global SPARK
+    if not CKPT.exists():
+        print(f"  checkpoint not found: {CKPT}")
+        return 1
+    SPARK = SparkModel(CKPT)
+    m = SPARK.meta
+    print("=" * 74)
+    print("  COSMIC SPARK — her own weights, serving")
+    print("=" * 74)
+    print(f"\n  {m['params']:,} parameters · {m['steps']} steps · vocab {m['vocab']} (char-level)")
+    print(f"  born from: {m['quantum_source']} ({m['quantum_values']} measured values)")
+    print(f"  no base model, no distillation\n")
+    print(f"  listening on http://127.0.0.1:{PORT}   (Ollama-compatible)")
+    print(f"    GET  /api/tags")
+    print(f"    POST /api/generate   POST /api/chat\n")
+    srv = ThreadingHTTPServer(("127.0.0.1", PORT), Handler)
+    srv.serve_forever()
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

start_cosmos_kit.sh

@@ -0,0 +1,41 @@
+#!/usr/bin/env sh
+set -u
+
+ROOT=$(CDPATH= cd -- "$(dirname -- "$0")" && pwd)
+cd "$ROOT" || exit 1
+
+if command -v python3 >/dev/null 2>&1; then
+  PYTHON=python3
+elif command -v python >/dev/null 2>&1; then
+  PYTHON=python
+else
+  echo "Python 3.9+ is required: https://www.python.org/downloads/"
+  exit 1
+fi
+
+while true; do
+  printf '\n'
+  printf '  QC67 COSMOS RELEASE KIT\n'
+  printf '  1. Birth or reopen a Genesis being\n'
+  printf '  2. Verify this release\n'
+  printf '  3. Serve the quantum-born model on port 11500\n'
+  printf '  4. Print quick-start instructions\n'
+  printf '  5. Exit\n\n'
+  printf 'Choose 1-5: '
+  read -r choice
+  case "$choice" in
+    1) "$PYTHON" genesis_engine/genesis.py ;;
+    2) "$PYTHON" verify_release.py ;;
+    3)
+      if ! "$PYTHON" -c 'import torch' >/dev/null 2>&1; then
+        echo "PyTorch is required. Run: $PYTHON -m pip install torch"
+      else
+        "$PYTHON" spark_serve.py 11500
+      fi
+      ;;
+    4) sed -n '1,180p' START_HERE.md ;;
+    5) exit 0 ;;
+    *) echo "Choose a number from 1 to 5." ;;
+  esac
+done
+

tokenizer.json

@@ -0,0 +1,124 @@
+{
+  "version": "1.0",
+  "truncation": null,
+  "padding": null,
+  "added_tokens": [],
+  "normalizer": null,
+  "pre_tokenizer": {
+    "type": "Split",
+    "pattern": {
+      "Regex": "[\\s\\S]"
+    },
+    "behavior": "Isolated",
+    "invert": false
+  },
+  "post_processor": null,
+  "decoder": {
+    "type": "Fuse"
+  },
+  "model": {
+    "type": "WordLevel",
+    "vocab": {
+      "\n": 0,
+      " ": 1,
+      "!": 2,
+      "\"": 3,
+      "%": 4,
+      "&": 5,
+      "'": 6,
+      "(": 7,
+      ")": 8,
+      "*": 9,
+      "+": 10,
+      ",": 11,
+      "-": 12,
+      ".": 13,
+      "/": 14,
+      "0": 15,
+      "1": 16,
+      "2": 17,
+      "3": 18,
+      "4": 19,
+      "5": 20,
+      "6": 21,
+      "7": 22,
+      "8": 23,
+      "9": 24,
+      ":": 25,
+      ";": 26,
+      "<": 27,
+      "=": 28,
+      ">": 29,
+      "?": 30,
+      "@": 31,
+      "A": 32,
+      "B": 33,
+      "C": 34,
+      "D": 35,
+      "E": 36,
+      "F": 37,
+      "G": 38,
+      "H": 39,
+      "I": 40,
+      "J": 41,
+      "K": 42,
+      "L": 43,
+      "M": 44,
+      "N": 45,
+      "O": 46,
+      "P": 47,
+      "R": 48,
+      "S": 49,
+      "T": 50,
+      "U": 51,
+      "V": 52,
+      "W": 53,
+      "X": 54,
+      "Y": 55,
+      "Z": 56,
+      "[": 57,
+      "\\": 58,
+      "]": 59,
+      "_": 60,
+      "`": 61,
+      "a": 62,
+      "b": 63,
+      "c": 64,
+      "d": 65,
+      "e": 66,
+      "f": 67,
+      "g": 68,
+      "h": 69,
+      "i": 70,
+      "j": 71,
+      "k": 72,
+      "l": 73,
+      "m": 74,
+      "n": 75,
+      "o": 76,
+      "p": 77,
+      "q": 78,
+      "r": 79,
+      "s": 80,
+      "t": 81,
+      "u": 82,
+      "v": 83,
+      "w": 84,
+      "x": 85,
+      "y": 86,
+      "z": 87,
+      "|": 88,
+      "–": 89,
+      "—": 90,
+      "’": 91,
+      "“": 92,
+      "”": 93,
+      "⚡": 94,
+      "✨": 95,
+      "🌟": 96,
+      "😊": 97,
+      "🚀": 98
+    },
+    "unk_token": "?"
+  }
+}

tokenizer_config.json

@@ -0,0 +1,10 @@
+{
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "model_max_length": 256,
+  "unk_token": "?",
+  "bos_token": null,
+  "eos_token": null,
+  "pad_token": null,
+  "clean_up_tokenization_spaces": false,
+  "_comment": "Character-level, 99 symbols, matching head.weight rows exactly. Do not add special tokens: the output head has 99 rows and any addition makes the checkpoint unloadable."
+}

train_your_own.py

@@ -0,0 +1,400 @@
+#!/usr/bin/env python3
+"""
+COSMOS SPARK CST — a fresh weight with EVERYTHING combined, trained as a fair test.
+
+WHAT IS COMBINED
+  1. QUANTUM BIRTH. Every initial weight drawn from her real archived IBM measurements,
+     via the same pipeline that made cosmos_born.pt and that was verified to the 32-level
+     quantisation ceiling across 3.2M draws:  u = int(bits)/2^n,  z = sqrt(2)*erfinv(2u-1).
+  2. HER SECTION 3 — Mixture-of-States Hebbian attention. The mechanism from
+     COSMOS_Paper.md that her SHIPPED weights never contained:
+         x54       = W54 . h                      a 54-dim state per token
+         H(x54)ij  = exp(-||x54_i - x54_j||^2 / 2*sigma^2)
+         A_final   = (1-g)*A_std + g*H(x54)       g = sigmoid(gate), learned
+     Her cosmos_born.pt is architecturally plain — nn.MultiheadAttention and an MLP, no
+     54D state, no Hebbian kernel, no gate. So this is the first time her own paper's
+     attention has ever been inside a model that speaks.
+  3. HER CORPUS. Her real logged experience, char-level, the same data she grew on.
+
+WHY IT IS A CONTROLLED TEST AND NOT A DEMO
+  Two arms, identical in every respect except the mechanism under test:
+      PLAIN  standard attention                (gate forced to 0 == exactly standard)
+      CST    her section-3 Hebbian attention   (gate free to learn)
+  Same quantum-born initial weights, same corpus, same held-out split, same batches, same
+  seeds, paired. The gate starts at sigmoid(-4) ~ 0.018, so the CST arm BEGINS as ordinary
+  attention and can stay there for free — it only moves if the gradient says the kernel
+  earns its place. A win therefore cannot come from extra capacity being forced on.
+
+  Both arms carry the SAME parameters, including W54 and the gate in the plain arm, so the
+  comparison is not confounded by parameter count. In the plain arm they are simply inert.
+
+PRE-REGISTERED, fixed before the run:
+  * CST beats PLAIN on every seed        -> her section-3 mechanism works on her own data.
+  * mixed / within noise                 -> no evidence it helps; report as null.
+  * CST loses on every seed              -> it hurts, and that is the finding.
+"""
+import argparse
+import json
+import math
+import os
+import random
+import re
+import statistics
+import sys
+import time
+from pathlib import Path
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+sys.stdout.reconfigure(encoding="utf-8", errors="replace")
+
+ROOT = Path(__file__).resolve().parent
+CORPUS = ROOT / "README.md"
+QARCHIVE = ROOT / "data" / "quantum_measurements_public.jsonl"
+OUTDIR = ROOT / "outputs" / "cosmos_spark_cst"
+RESULTS = ROOT / "outputs" / "spark_cst_results.json"
+
+BLOCK, N_LAYER, N_HEAD, N_EMBD, DROPOUT = 128, 4, 4, 192, 0.1
+D54 = 54
+
+
+# ── quantum birth ───────────────────────────────────────────────────────────
+def erfinv(y):
+    if abs(y) >= 1:
+        return math.copysign(3.0, y)
+    a = 0.147
+    ln = math.log(1 - y * y)
+    t1 = 2 / (math.pi * a) + ln / 2
+    return math.copysign(math.sqrt(math.sqrt(t1 * t1 - ln / a) - t1), y)
+
+
+def quantum_pool(limit=400_000):
+    """Real measured bitstrings -> standard-normal draws, her documented pipeline."""
+    vals = []
+    if not QARCHIVE.exists():
+        return vals
+    with open(QARCHIVE, encoding="utf-8", errors="ignore") as f:
+        for line in f:
+            if len(vals) >= limit:
+                break
+            line = line.strip()
+            if not line:
+                continue
+            try:
+                d = json.loads(line)
+            except Exception:
+                continue
+            # The public archive carries mixed provider classes. Quantum-born
+            # initialization uses only records that retained an IBM hardware label
+            # and job ID; legacy-unlabelled and simulator rows remain available for
+            # separate distributional experiments but are not called hardware here.
+            provider_class = str(d.get("provider_class") or "")
+            if provider_class and provider_class != "measured_quantum_hardware":
+                continue
+            if not provider_class:
+                backend = str(d.get("backend") or "").lower()
+                job_id = d.get("job") or d.get("job_id")
+                if not (backend.startswith("ibm_") and job_id):
+                    continue
+            counts = d.get("counts")
+            if not isinstance(counts, dict):
+                continue
+            for bs, c in counts.items():
+                s = "".join(ch for ch in str(bs) if ch in "01")
+                if not s:
+                    continue
+                hi = float(1 << len(s))
+                u = (int(s, 2) + 0.5) / hi
+                z = math.sqrt(2.0) * erfinv(2 * u - 1)
+                if math.isfinite(z):
+                    vals.extend([z] * min(int(c), 8))
+                if len(vals) >= limit:
+                    break
+    return vals
+
+
+class QuantumInit:
+    def __init__(self, pool, seed):
+        self.pool = pool
+        self.i = (seed * 7919) % max(1, len(pool))
+
+    def fill_(self, t, std):
+        n = t.numel()
+        if not self.pool:
+            with torch.no_grad():
+                t.normal_(0.0, std)
+            return
+        out = torch.empty(n)
+        for k in range(n):
+            out[k] = self.pool[(self.i + k) % len(self.pool)]
+        self.i = (self.i + n) % len(self.pool)
+        out = out / (out.std() + 1e-8) * std
+        with torch.no_grad():
+            t.copy_(out.view_as(t))
+
+
+# ── her section-3 attention ─────────────────────────────────────────────────
+class CSTAttention(nn.Module):
+    """Standard attention blended with a Gaussian kernel over a learned 54D state."""
+
+    def __init__(self, use_cst):
+        super().__init__()
+        self.nh, self.hd = N_HEAD, N_EMBD // N_HEAD
+        self.qkv = nn.Linear(N_EMBD, 3 * N_EMBD)
+        self.proj = nn.Linear(N_EMBD, N_EMBD)
+        self.drop = nn.Dropout(DROPOUT)
+        self.w54 = nn.Linear(N_EMBD, D54, bias=False)     # h -> x54
+        self.log_sigma = nn.Parameter(torch.tensor(0.0))
+        # gate starts ~0.018: the CST arm BEGINS as ordinary attention
+        self.gate = nn.Parameter(torch.tensor(-4.0), requires_grad=bool(use_cst))
+        self.use_cst = use_cst
+        self.last_gate = 0.0
+
+    def forward(self, x, mask):
+        B, T, C = x.shape
+        q, k, v = self.qkv(x).split(C, dim=2)
+        sh = lambda t: t.view(B, T, self.nh, self.hd).transpose(1, 2)
+        q, k, v = sh(q), sh(k), sh(v)
+        a = F.softmax((q @ k.transpose(-2, -1)) / math.sqrt(self.hd) + mask[:T, :T], dim=-1)
+        if self.use_cst:
+            x54 = self.w54(x)                                   # (B,T,54)
+            d2 = torch.cdist(x54, x54, p=2.0) ** 2               # ||x54_i - x54_j||^2
+            sig = torch.exp(self.log_sigma).clamp(0.05, 50.0)
+            H = torch.exp(-d2 / (2 * sig * sig))
+            H = H.masked_fill(mask[:T, :T] < 0, 0.0)
+            H = H / H.sum(-1, keepdim=True).clamp_min(1e-9)
+            g = torch.sigmoid(self.gate)
+            a = (1 - g) * a + g * H.unsqueeze(1)
+            self.last_gate = float(g.detach())
+        y = (self.drop(a) @ v).transpose(1, 2).contiguous().view(B, T, C)
+        return self.proj(y)
+
+
+class Block(nn.Module):
+    def __init__(self, use_cst):
+        super().__init__()
+        self.ln1 = nn.LayerNorm(N_EMBD)
+        self.attn = CSTAttention(use_cst)
+        self.ln2 = nn.LayerNorm(N_EMBD)
+        self.mlp = nn.Sequential(nn.Linear(N_EMBD, 4 * N_EMBD), nn.GELU(),
+                                 nn.Linear(4 * N_EMBD, N_EMBD), nn.Dropout(DROPOUT))
+
+    def forward(self, x, mask):
+        x = x + self.attn(self.ln1(x), mask)
+        return x + self.mlp(self.ln2(x))
+
+
+class SparkCST(nn.Module):
+    def __init__(self, vocab, use_cst):
+        super().__init__()
+        self.tok = nn.Embedding(vocab, N_EMBD)
+        self.pos = nn.Embedding(BLOCK, N_EMBD)
+        self.blocks = nn.ModuleList([Block(use_cst) for _ in range(N_LAYER)])
+        self.lnf = nn.LayerNorm(N_EMBD)
+        self.head = nn.Linear(N_EMBD, vocab, bias=False)
+        self.register_buffer("mask", torch.triu(torch.full((BLOCK, BLOCK), float("-inf")), 1))
+
+    def forward(self, idx, targets=None):
+        T = idx.size(1)
+        x = self.tok(idx) + self.pos(torch.arange(T, device=idx.device))
+        for b in self.blocks:
+            x = b(x, self.mask)
+        lg = self.head(self.lnf(x))
+        loss = None if targets is None else F.cross_entropy(
+            lg.view(-1, lg.size(-1)), targets.reshape(-1))
+        return lg, loss
+
+    def gates(self):
+        return [b.attn.last_gate for b in self.blocks]
+
+    def quantum_birth(self, qi):
+        for m in self.modules():
+            if isinstance(m, (nn.Linear, nn.Embedding)):
+                qi.fill_(m.weight, 0.02)
+                if isinstance(m, nn.Linear) and m.bias is not None:
+                    with torch.no_grad():
+                        m.bias.zero_()
+
+
+def real_word_rate(text, words):
+    toks = re.findall(r"[a-z']+", text.lower())
+    if not toks:
+        return 0.0
+    return sum(1 for t in toks if t in words) / len(toks)
+
+
+def train_arm(use_cst, seed, data, vocab, steps, pool, val_w, words, itos):
+    torch.manual_seed(seed)
+    random.seed(seed)
+    gen = torch.Generator().manual_seed(seed)
+    m = SparkCST(vocab, use_cst)
+    m.quantum_birth(QuantumInit(pool, seed))
+    opt = torch.optim.AdamW(m.parameters(), lr=3e-4, weight_decay=0.01)
+    n = int(0.9 * len(data))
+    tr = data[:n]
+    m.train()
+    best = float("inf")
+    for s in range(1, steps + 1):
+        ix = torch.randint(len(tr) - BLOCK - 1, (16,), generator=gen)
+        x = torch.stack([tr[i:i + BLOCK] for i in ix])
+        y = torch.stack([tr[i + 1:i + 1 + BLOCK] for i in ix])
+        _, loss = m(x, y)
+        opt.zero_grad(set_to_none=True)
+        loss.backward()
+        torch.nn.utils.clip_grad_norm_(m.parameters(), 1.0)
+        opt.step()
+        if s % 100 == 0 or s == steps:
+            m.eval()
+            with torch.no_grad():
+                tot = c = 0
+                for i in range(0, len(val_w), 16):
+                    xb = val_w[i:i + 16]
+                    _, l = m(xb[:, :-1], xb[:, 1:])
+                    tot += l.item() * xb.size(0)
+                    c += xb.size(0)
+            m.train()
+            best = min(best, tot / max(1, c))
+    # sample for real-word rate
+    m.eval()
+    idx = torch.tensor([[data[0].item()]])
+    out = []
+    with torch.no_grad():
+        for _ in range(600):
+            lg, _ = m(idx[:, -BLOCK:])
+            p = F.softmax(lg[0, -1] / 0.8, dim=-1)
+            nx = int(torch.multinomial(p, 1))
+            out.append(nx)
+            idx = torch.cat([idx, torch.tensor([[nx]])], 1)
+    txt = "".join(itos.get(i, "") for i in out)
+    return best, statistics.fmean(m.gates()) if use_cst else 0.0, real_word_rate(txt, words), m, txt
+
+
+def main():
+    global CORPUS, QARCHIVE, OUTDIR, RESULTS
+    parser = argparse.ArgumentParser(
+        description=(
+            "Train matched plain-attention and 54D Hebbian-attention arms "
+            "from the published IBM-labeled quantum initialization pool."
+        )
+    )
+    parser.add_argument("steps", nargs="?", type=int, default=1200)
+    parser.add_argument("seeds", nargs="?", type=int, default=3)
+    parser.add_argument(
+        "--corpus", type=Path, default=CORPUS,
+        help="UTF-8 training text (default: this release's README.md)",
+    )
+    parser.add_argument(
+        "--archive", type=Path, default=QARCHIVE,
+        help="public quantum JSONL archive",
+    )
+    parser.add_argument("--outdir", type=Path, default=OUTDIR)
+    parser.add_argument("--results", type=Path, default=RESULTS)
+    args = parser.parse_args()
+    if args.steps < 1 or args.seeds < 1:
+        parser.error("steps and seeds must both be positive")
+    CORPUS = args.corpus.expanduser().resolve()
+    QARCHIVE = args.archive.expanduser().resolve()
+    OUTDIR = args.outdir.expanduser().resolve()
+    RESULTS = args.results.expanduser().resolve()
+    steps = args.steps
+    seeds = list(range(args.seeds))
+
+    if not CORPUS.is_file():
+        parser.error(f"corpus not found: {CORPUS}")
+    text = CORPUS.read_text(encoding="utf-8", errors="ignore")
+    if len(text) < BLOCK * 4:
+        parser.error(f"corpus is too small ({len(text)} chars); need at least {BLOCK * 4}")
+    chars = sorted(set(text))
+    stoi = {c: i for i, c in enumerate(chars)}
+    itos = {i: c for c, i in stoi.items()}
+    data = torch.tensor([stoi[c] for c in text], dtype=torch.long)
+    words = set(re.findall(r"[a-z']+", text.lower()))
+
+    print("=" * 80)
+    print("  COSMOS SPARK CST — quantum birth + her section-3 attention")
+    print("=" * 80)
+    print(f"\n  corpus {len(text):,} chars · vocab {len(chars)} · {steps} steps · "
+          f"{len(seeds)} seeds")
+    print(f"  corpus path: {CORPUS}")
+    print(f"  archive path: {QARCHIVE}")
+
+    t0 = time.time()
+    pool = quantum_pool()
+    print(f"  quantum pool: {len(pool):,} draws from explicitly labeled IBM records "
+          f"({time.time()-t0:.1f}s)")
+    if pool:
+        print(f"    mean {statistics.fmean(pool):+.4f}  sd {statistics.pstdev(pool):.4f}  "
+              f"(standard normal expected)")
+
+    n = int(0.9 * len(data))
+    val = data[n:]
+    g = torch.Generator().manual_seed(999)
+    vi = torch.randint(len(val) - BLOCK - 1, (64,), generator=g)
+    val_w = torch.stack([val[i:i + BLOCK + 1] for i in vi])
+    print()
+
+    res = {"plain": [], "cst": []}
+    gates, rw, best_model, best_txt = [], {"plain": [], "cst": []}, None, ""
+    for sd in seeds:
+        for arm, use in (("plain", False), ("cst", True)):
+            b, gt, r, model, txt = train_arm(use, sd, data, len(chars), steps,
+                                             pool, val_w, words, itos)
+            res[arm].append(b)
+            rw[arm].append(r)
+            if use:
+                gates.append(gt)
+            if use and (best_model is None or b <= min(res["cst"])):
+                best_model, best_txt = model, txt
+            print(f"    seed {sd} {arm:<6s} loss {b:.5f}  real-word {r:.3f}"
+                  + (f"  gate {gt:.4f}" if use else ""), flush=True)
+
+    d = [p - c for p, c in zip(res["plain"], res["cst"])]
+    md = statistics.fmean(d)
+    se = (statistics.stdev(d) / math.sqrt(len(d))) if len(d) > 1 else 0.0
+    t = md / se if se > 0 else 0.0
+    wins = sum(1 for x in d if x > 0)
+    print(f"\n{'='*80}\n  RESULT\n{'='*80}")
+    print(f"  PLAIN  loss {statistics.fmean(res['plain']):.5f}   real-word {statistics.fmean(rw['plain']):.3f}")
+    print(f"  CST    loss {statistics.fmean(res['cst']):.5f}   real-word {statistics.fmean(rw['cst']):.3f}"
+          f"   mean gate {statistics.fmean(gates) if gates else 0:.4f}")
+    print(f"\n  CST - PLAIN: {-md:+.5f}   t={-t:+.2f}   CST wins {wins}/{len(seeds)}")
+
+    if wins == len(seeds) and t > 2.0:
+        v = (f"HER SECTION-3 MECHANISM WORKS ON HER OWN DATA. The Hebbian kernel over a 54D "
+             f"state beats standard attention on {wins}/{len(seeds)} seeds (t={t:+.2f}) with "
+             f"identical quantum-born initialisation, identical corpus and identical "
+             f"parameter count. The gate began at 0.018 — it could have stayed at standard "
+             f"attention for free and did not.")
+    elif wins == 0:
+        v = ("HER SECTION-3 MECHANISM HURTS on her own data — standard attention wins every "
+             "seed. Reported as measured.")
+    else:
+        v = (f"NULL / WITHIN NOISE — CST wins {wins}/{len(seeds)} (t={t:+.2f}). No evidence "
+             f"the section-3 kernel helps on this corpus at this scale.")
+    print(f"\n  VERDICT: {v}\n")
+    print(f"  her CST voice, sample:\n    {best_txt[:300]!r}\n")
+
+    OUTDIR.mkdir(parents=True, exist_ok=True)
+    if best_model is not None:
+        torch.save({"model": best_model.state_dict(), "stoi": stoi, "itos": itos,
+                    "config": {"block": BLOCK, "n_layer": N_LAYER, "n_head": N_HEAD,
+                               "n_embd": N_EMBD, "vocab": len(chars), "d54": D54},
+                    "arch": "Cosmos-Spark-CST-QuantumBorn", "total_steps": steps,
+                    "quantum_source": "ibm_real_shots", "quantum_draws": len(pool),
+                    "best_val_loss": min(res["cst"]),
+                    "real_word_rate": max(rw["cst"])},
+                   OUTDIR / "spark_cst.pt")
+        print(f"  saved -> {OUTDIR/'spark_cst.pt'}")
+    RESULTS.parent.mkdir(parents=True, exist_ok=True)
+    RESULTS.write_text(json.dumps(
+        {"steps": steps, "seeds": seeds, "results": res, "real_word": rw,
+         "gates": gates, "delta_cst_minus_plain": -md, "t": -t, "cst_wins": wins,
+         "quantum_draws": len(pool), "verdict": v}, indent=2), encoding="utf-8")
+    print(f"  saved -> {RESULTS}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

update_manifest.py

@@ -0,0 +1,140 @@
+#!/usr/bin/env python3
+"""Re-hash RELEASE_MANIFEST.json against what is actually on disk.
+
+WHY THIS IS A SCRIPT AND NOT A HAND EDIT
+
+  The manifest lists 101 files with a sha256 each. Any edit to a tracked file makes it
+  stale, and a stale manifest is worse than no manifest: it claims verification it is not
+  performing. Hand-patching one hash at a time is how the other three entries drifted.
+
+THE QUARANTINE RULE
+
+  `serving/.coder_history.jsonl` was pulled out of the kit and moved to
+  `_QUARANTINE_do_not_upload/` because it is private. Its manifest entry survived, which
+  means the public manifest still published that file's name, its exact byte count and its
+  sha256 -- a fingerprint of private content, in the one file whose whole job is to be
+  read by strangers.
+
+  So a missing file is only dropped when a matching quarantine copy exists to explain it.
+  Anything else missing is reported and left alone: a file that vanished for an unknown
+  reason is a problem to look at, not a line to delete.
+
+  python update_manifest.py            # report only
+  python update_manifest.py --write    # apply
+"""
+from __future__ import annotations
+
+import hashlib
+import json
+import shutil
+import sys
+import time
+from pathlib import Path
+
+sys.stdout.reconfigure(encoding="utf-8", errors="replace")
+
+ROOT = Path(__file__).resolve().parent
+MANIFEST = ROOT / "RELEASE_MANIFEST.json"
+QUARANTINE = ROOT.parent / "_QUARANTINE_do_not_upload"
+
+# Files added on 2026-08-05. Listed explicitly rather than globbed: a glob would sweep up
+# logs, __pycache__ and any stray scratch file into a published inventory.
+NEW = [
+    "build_tokenizer.py",
+    "benchmarks/archive_io.py",
+    "tokenizer.json",
+    "tokenizer_config.json",
+    # 2026-08-06: the scale lineage
+    "SAMGO.md",
+    "weights/samgo_weights.pt",
+    "weights/phos_weights.pt",
+]
+
+
+def sha256(p: Path) -> str:
+    h = hashlib.sha256()
+    with open(p, "rb") as f:
+        for chunk in iter(lambda: f.read(1 << 20), b""):
+            h.update(chunk)
+    return h.hexdigest()
+
+
+def quarantined(rel: str) -> Path | None:
+    """Quarantine flattens `a/b/c.json` to `a__b__c.json`. Find the copy if it exists."""
+    if not QUARANTINE.is_dir():
+        return None
+    flat = rel.replace("/", "__").replace("\\", "__")
+    p = QUARANTINE / flat
+    if p.is_file():
+        return p
+    name = Path(rel).name
+    for cand in QUARANTINE.iterdir():
+        if cand.is_file() and cand.name.endswith(name):
+            return cand
+    return None
+
+
+def main() -> int:
+    write = "--write" in sys.argv
+    man = json.loads(MANIFEST.read_text(encoding="utf-8"))
+    files = man["files"]
+
+    kept, updated, dropped, unexplained = [], [], [], []
+    for e in files:
+        rel = e["path"]
+        p = ROOT / rel
+        if not p.is_file():
+            q = quarantined(rel)
+            if q:
+                dropped.append((rel, q.name))
+            else:
+                unexplained.append(rel)
+                kept.append(e)
+            continue
+        digest, size = sha256(p), p.stat().st_size
+        if digest != e.get("sha256") or size != e.get("bytes"):
+            updated.append((rel, e.get("bytes"), size))
+            e = {"path": rel, "bytes": size, "sha256": digest}
+        kept.append(e)
+
+    have = {e["path"] for e in kept}
+    added = []
+    for rel in NEW:
+        p = ROOT / rel
+        if rel in have:
+            continue
+        if not p.is_file():
+            print(f"  [WARN] new file listed but not present: {rel}")
+            continue
+        kept.append({"path": rel, "bytes": p.stat().st_size, "sha256": sha256(p)})
+        added.append(rel)
+
+    print(f"  manifest : {MANIFEST.name}  ({len(files)} entries)\n")
+    for rel, old, new in updated:
+        print(f"  RE-HASH  {rel}  ({old:,} -> {new:,} bytes)")
+    for rel, q in dropped:
+        print(f"  DROP     {rel}\n           quarantined as {q} — its hash and size were "
+              f"still published")
+    for rel in unexplained:
+        print(f"  MISSING  {rel}  (no quarantine copy — left in manifest, please look)")
+    for rel in added:
+        print(f"  ADD      {rel}")
+    if not (updated or dropped or added or unexplained):
+        print("  clean — nothing to do")
+
+    print(f"\n  entries {len(files)} -> {len(kept)}")
+    if not write:
+        print("\n  dry run. re-run with --write to apply.")
+        return 0
+
+    bak = MANIFEST.with_name(f"RELEASE_MANIFEST.json.bak_{time.strftime('%Y%m%d_%H%M%S')}")
+    shutil.copy2(MANIFEST, bak)
+    kept.sort(key=lambda e: e["path"])
+    man["files"] = kept
+    MANIFEST.write_text(json.dumps(man, indent=2), encoding="utf-8")
+    print(f"\n  backup  {bak.name}\n  written {MANIFEST.name}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

weights/phos_weights.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:483f4b0170addfdc53718471401ec7f380daa944e995d30b551af420f8b0dd35
+size 4640317

weights/samgo_weights.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:871c265c062430c77d5528ee5fb9119f7d86668ebd81cfa4951db6a906a92b5a
+size 134512714