Publish K96-tail sealed evidence closure

reproduction/closure/evidence/COUPLED_REENCODE_GPU_RECOVERY_20260813.md

@@ -0,0 +1,31 @@
+# Coupled re-encode GPU recovery state — 2026-08-13
+
+## Preserved campaign state
+
+- Active wave: `wave-019-022`, exact K3/K4 triplet scoring.
+- Last advancing receipt count: `579/1024` (`L19=152`, `L20=146`, `L21=155`, `L22=126`).
+- All score artifacts are per-expert atomic outputs. Restarting the campaign skips validated existing receipts and recomputes only missing experts.
+- Layers 3 through 18 remain fully materialized and runtime-oracle sealed.
+
+## Driver failure evidence
+
+- At 2026-08-13 23:52:53 EDT the kernel reported an NVIDIA GSP task watchdog timeout.
+- Subsequent crash reports identify GPU 3 and repeat the same GSP program counter (`0x1bc70d8`).
+- `nvidia-smi`, all 32 scorer CUDA processes, and `cosmic-comp` became blocked in NVIDIA kernel-driver wait paths.
+- Receipt counts stopped advancing after 579, proving this is a driver-wide stall rather than a slow expert.
+- GPU 3 was 78–81 C before the crash and reported no active hardware thermal slowdown.
+
+## In-place recovery attempted
+
+- PCI function reset for GPU 3 (`0000:c1:00.0`) returned success once, but did not unwind the NVIDIA global semaphore.
+- Scorer processes and the compositor were sent termination signals; uninterruptible driver tasks could not exit.
+- NVIDIA driver unbind and a second PCI reset both blocked and were force-terminated.
+- A host reboot is therefore required to reload the NVIDIA kernel/GSP state.
+
+## Automatic resume preparation
+
+- Persistent unit source: `/home/brandonmusic/KLC_SANDBOXES/glm52_fresh_sqg_3p0625/systemd/glm52-full-coupled-k96tail-resume.service`
+- Installed unit: `/etc/systemd/system/glm52-full-coupled-k96tail-resume.service`
+- Unit state: enabled for `multi-user.target`.
+- The unit waits for the NVMe automount, Docker, and all four NVIDIA GPUs before launching the canonical campaign with `START_WAVE=3` and `STOP_WAVE=75`.
+- The campaign's seals and atomic receipts make this a resume, not a restart from scratch.

reproduction/closure/evidence/GPU_GSP_RECOVERY_20260813.md

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+# GPU GSP recovery checkpoint — 2026-08-13
+
+- Authoritative goal: `019ffa7c-7914-7633-a7b8-a2f4b2e23fc0`.
+- Wave `019-022` exact K3/K4 scoring started at `23:11:04 EDT` with 32
+  workers (eight contexts per physical GPU).
+- Last verified atomic receipt census before the driver incident:
+  layer 19 = 152, layer 20 = 145, layer 21 = 155, layer 22 = 126;
+  total = 578 / 1024.
+- At `23:51:22 EDT` the NVIDIA 610.43.02 open kernel driver reported a GSP
+  task-watchdog timeout. CUDA, NVML, NVIDIA modeset, and the COSMIC compositor
+  subsequently blocked in the shared NVIDIA RM path. This was a driver/GSP
+  failure, not a scorer exception.
+- The campaign and its 32 containers were terminated. Completed expert JSON
+  and row-SSE receipts are atomic and remain valid; the launchers validate and
+  skip them on resume. No candidate selection or allocation had begun for this
+  wave.
+- Per-device PCI function resets did not restore the shared RM state. A clean
+  host reboot is required.
+- The persistent user service
+  `glm52-full-coupled-k96tail-no-shortcut-goal019ffa7c.service` is installed in
+  `~/.config/systemd/user/`, verified, enabled, and the user has linger enabled.
+  It runs the authoritative campaign with `START_WAVE=3`, `STOP_WAVE=75`, and
+  `CLEANUP_VALIDATED_WAVES=1`, so after boot it verifies/seals prior waves and
+  resumes wave 019-022 from the existing receipts.
+- Pinned saved inputs for wave 031-034 finished staging and have a 15-shard
+  manifest plus four 12,897,349,632-byte capture tensors. Wave 035-038 has a
+  resumable partial local directory (about 6.2 GB); do not count it complete
+  until its normal manifest and required-file validation pass.
+- Do not run any further background HF prefetch on the active score/encode
+  path. Wave 031-034 was successfully staged, but the timing overlap obscured
+  the onset of the GSP failure and does not justify further timed-path I/O.

reproduction/closure/evidence/README.md

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+# Compact execution evidence
+
+This directory records the measured state at `2026-08-14T00:16:45-04:00`.
+It is not a live status feed.
+
+- `execution_snapshot_2026-08-14T001645-0400.json` states the layer and score
+  counts at that timestamp.
+- `score_receipts_2026-08-14T001645-0400.sha256` binds 581 atomic expert JSON
+  receipts and their 581 row-SSE arrays without copying the arrays.
+- `layers_003_018/` contains the compact layer manifests, quality receipts,
+  and native B12X runtime oracle receipts for all 16 qualified layers.
+- `parity_layers_004_018/` contains the exact scorer/encoder payload parity
+  receipts for the 15 K96 layers. Layer 3 uses the separate preserved K48
+  contract and is not represented as a K96 parity result.
+- `layer_019_runtime_binding.json` is a durable example of the frozen SQG,
+  calibration dataset, coupled transform, QSRT, and source-domain bindings.
+- The two recovery records preserve the NVIDIA GSP failure diagnosis and the
+  post-reboot single-writer recovery decision.
+
+Large `.safetensors`, `.npz`, capture, model, log, and cache payloads are not
+copied. Their compact receipts and hashes are the publication boundary.
+
+`snapshot_2026-08-15T002639-0400/` is the later historical distributed-campaign
+snapshot.
+It extends compact sealed evidence through layer 46 and binds the complete
+atomic score set through layer 50. Unlike the earlier checkpoint, it includes
+a frozen campaign log while continuing to omit model tensors and row-SSE
+payload bytes. It predates final assembly and end-to-end KLD.
+
+`final-mechanical/` is the completed mechanical closure. It binds 75 layer
+manifests, 75 quality receipts, 75 passing B12X runtime oracles, and 74 exact
+K96 scorer/encoder parity receipts. Layer 3 is explicitly bound as the sealed
+K48 exception without a K96 parity receipt. The directory also contains the
+assembly manifest and authoritative model codec receipt.
+
+`final-exact-ii-r11/` contains the completed full-vocabulary KLD, hidden replay,
+and exact Infernal Invocation r11 TP4/DCP4/MTP3 qualification evidence. The
+candidate mean KLD is `0.1401771516114036`, versus the frozen source mean
+`0.07583317451217256`, so model quality remains research-only.

reproduction/closure/evidence/execution_snapshot_2026-08-14T001645-0400.json

@@ -0,0 +1,76 @@
+{
+  "as_of": "2026-08-14T00:16:45-04:00",
+  "candidate_final_kld_supported": false,
+  "complete": true,
+  "fully_qualified_routed_layers": [
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    10,
+    11,
+    12,
+    13,
+    14,
+    15,
+    16,
+    17,
+    18
+  ],
+  "full_model_supported": false,
+  "layer_19_through_22_recipes_complete": true,
+  "layer_score_receipts": {
+    "19": 153,
+    "20": 146,
+    "21": 156,
+    "22": 126
+  },
+  "parity_layers": [
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    10,
+    11,
+    12,
+    13,
+    14,
+    15,
+    16,
+    17,
+    18
+  ],
+  "remaining_layer_19_through_22_scores": 443,
+  "release_supported": false,
+  "runtime_oracle_layers": [
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    10,
+    11,
+    12,
+    13,
+    14,
+    15,
+    16,
+    17,
+    18
+  ],
+  "schema": "glm52-coupled-k96tail-publication-snapshot-v1",
+  "score_receipt_hash_manifest": {
+    "expert_json_files": 581,
+    "row_sse_files": 581,
+    "sha256": "16a7b5871c996ac4e387d448d7baf335bedfb91224ee7f16525d6ff52f9a1e5f"
+  },
+  "total_layer_19_through_22_scores": 1024,
+  "written_layer_19_through_22_scores": 581
+}

reproduction/closure/evidence/final-exact-ii-r11/full-kld/kld_exact_ii_r11_tp4dcp1.json

@@ -0,0 +1,2210 @@
+{
+  "complete": true,
+  "elapsed_sec": 52.5161001200031,
+  "env_mirror": {
+    "B12X_MOE_FORCE_A16": "0",
+    "B12X_MOE_FORCE_A8": "0",
+    "CUDA_DEVICE_MAX_CONNECTIONS": "32",
+    "CUDA_DEVICE_ORDER": "PCI_BUS_ID",
+    "CUTE_DSL_ARCH": "sm_120a",
+    "GLOO_SOCKET_IFNAME": "lo",
+    "NCCL_IB_DISABLE": "1",
+    "NCCL_P2P_LEVEL": "SYS",
+    "NCCL_PROTO": "LL,LL128,Simple",
+    "NCCL_SOCKET_IFNAME": "lo",
+    "OMP_NUM_THREADS": "16",
+    "PYTORCH_CUDA_ALLOC_CONF": "expandable_segments:True",
+    "SAFETENSORS_FAST_GPU": "1",
+    "TMPDIR": "/container-tmp",
+    "TORCH_CUDA_ARCH_LIST": "12.0a",
+    "VLLM_B12X_ABSORB_BMM": "0",
+    "VLLM_ENABLE_PCIE_ALLREDUCE": "1",
+    "VLLM_PCIE_ALLREDUCE_BACKEND": "cpp",
+    "VLLM_USE_AOT_COMPILE": "1",
+    "VLLM_USE_B12X_FP8_GEMM": "1",
+    "VLLM_USE_B12X_MOE": "1",
+    "VLLM_USE_B12X_SPARSE_INDEXER": "1",
+    "VLLM_USE_BREAKABLE_CUDAGRAPH": "0",
+    "VLLM_USE_FLASHINFER_SAMPLER": "1",
+    "VLLM_USE_MEGA_AOT_ARTIFACT": "1",
+    "VLLM_USE_V2_MODEL_RUNNER": "1",
+    "VLLM_WORKER_MULTIPROC_METHOD": "spawn",
+    "XDG_CACHE_HOME": "/cache"
+  },
+  "gpus": [
+    {
+      "compute_capability": [
+        12,
+        0
+      ],
+      "index": 0,
+      "name": "NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition"
+    },
+    {
+      "compute_capability": [
+        12,
+        0
+      ],
+      "index": 1,
+      "name": "NVIDIA RTX PRO 6000 Blackwell Workstation Edition"
+    },
+    {
+      "compute_capability": [
+        12,
+        0
+      ],
+      "index": 2,
+      "name": "NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition"
+    },
+    {
+      "compute_capability": [
+        12,
+        0
+      ],
+      "index": 3,
+      "name": "NVIDIA RTX PRO 6000 Blackwell Workstation Edition"
+    }
+  ],
+  "image_id": "sha256:79dfbf6e697a1081016a3256ff5c96b1c2d6dcddd6f04662c9c693881310fa87",
+  "image_ref": "verdictai/glm52-k96-ii-r11:20260815-tpfix",
+  "kernel_provenance": {
+    "schedule": {
+      "blocks_per_cta": 8,
+      "kernel": "m128n64",
+      "route_block_rows": 128,
+      "stages": 2,
+      "tile_n": 64
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+    "sha256": {
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+      "trellis_linear_api.py": "acdd657bd1d4b20471a1acf6c1f9fbf764f24e96465f7a54d6c9021323e1fd01",
+      "w4a16_kernel.py": "24822f32b67acca11d2d8d2b4dd88dadebd9fa40656f9a05a141c358a027d7bd"
+    }
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reproduction/closure/evidence/final-exact-ii-r11/hidden-replay/capture-full-kld.json

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+  "token_first16": [
+    284,
+    8396,
+    425,
+    10960,
+    465,
+    284,
+    14721,
+    8396,
+    425,
+    10960,
+    465,
+    374,
+    458,
+    6364,
+    4531,
+    1154
+  ],
+  "token_sequence_sha256": "d0be87a4909ad00c311e36fc5c81c7d6942216c16a8fbbbf77778edb273346f1",
+  "total_positions": 2047,
+  "versions": {
+    "b12x": "present",
+    "cuda": "13.3",
+    "cutlass": "4.6.2",
+    "deep_gemm": "2.5.0",
+    "flashinfer": "0.6.18+cu133",
+    "torch": "2.13.0",
+    "triton": "3.7.1",
+    "vllm": "0.26.1rc0+infernal.invocation.cu133.r11.vllm908522a.b12x5d648d9"
+  }
+}

reproduction/closure/evidence/final-exact-ii-r11/hidden-replay/hidden-manifest.json

@@ -0,0 +1,34 @@
+{
+  "complete": true,
+  "created_utc": "2026-08-15T08:08:33.506748+00:00",
+  "dtype": "BF16",
+  "file": "hidden_states.safetensors",
+  "file_sha256": "50781f1192645e13899d11f2181717ea05e028688db0201ede6d7e88360ee874",
+  "full_kld_receipt": "/capture/capture-full-kld.json",
+  "full_kld_receipt_sha256": "a67e896ffc1f879cdb3320042c50981532ce7837e0375fe34bcb0729fe685fc7",
+  "hidden_width": 6144,
+  "interpretation": "Raw prompt capture retains all 2,048 rows; rows 0..2,046 are scored against next-token reference logits and row 2,047 is retained but not scored because its continuation target is absent.",
+  "key": "hidden_states",
+  "raw_chunks": [
+    {
+      "file": "0-811bfa3b/hidden.rows-000000-002048.safetensors",
+      "row_end": 2048,
+      "row_start": 0,
+      "sha256": "0bc36a066b48662418e2f5b92c469b1bfbbbd22e0130b5617450a974635e2977",
+      "size_bytes": 25166048
+    }
+  ],
+  "raw_rows": 2048,
+  "request_id": "0-811bfa3b",
+  "schema": "glm52-pre-lm-head-hidden-one-context-v1",
+  "scored_rows": 2047,
+  "semantic_point": "after_final_rmsnorm_before_lm_head",
+  "shape": [
+    2048,
+    6144
+  ],
+  "size_bytes": 25166120,
+  "token_file": "/tokens.json",
+  "token_file_sha256": "d0be87a4909ad00c311e36fc5c81c7d6942216c16a8fbbbf77778edb273346f1",
+  "token_sequence_sha256": "d0be87a4909ad00c311e36fc5c81c7d6942216c16a8fbbbf77778edb273346f1"
+}

reproduction/closure/evidence/final-exact-ii-r11/hidden-replay/lm-head-manifest.json

@@ -0,0 +1,24 @@
+{
+  "candidate_checkpoint": "/model",
+  "candidate_index_sha256": "98d8c371ba5ec8aaf2e326b228bcd36a5df4cad600085a8f4dd2e098f2474e9f",
+  "candidate_shard": "direct-bf16-00001-of-00282.safetensors",
+  "candidate_shard_sha256": "32448fb3c149fb8024efd99c3d0e0df7f33ae09f0f3173234f32fcbf4b6f2316",
+  "checkpoint_tensor_key": "lm_head.weight",
+  "complete": true,
+  "dtype": "BF16",
+  "file": "weight.safetensors",
+  "file_sha256": "f56e1020446f4eaf73b418dd62ef853eb51e4f5c502d09ec0301086a90557969",
+  "key": "weight",
+  "raw_tensor_sha256": "a012be05e7716292407d418b408222de256d4dbe2fe2143a44d27d8e3553bfba",
+  "schema": "glm52-canonical-bf16-lm-head-v1",
+  "shape": [
+    154880,
+    6144
+  ],
+  "size_bytes": 1903165672,
+  "source_candidate_byte_identity": true,
+  "source_checkpoint": "/source",
+  "source_index_sha256": "62182715e5ef3eb0069b38ff710a38e7a118154278768a8c7ab8efed60c501f5",
+  "source_shard": "direct-bf16-00001-of-00282.safetensors",
+  "source_shard_sha256": "32448fb3c149fb8024efd99c3d0e0df7f33ae09f0f3173234f32fcbf4b6f2316"
+}

reproduction/closure/evidence/final-exact-ii-r11/hidden-replay/preregistered-gate-history/compare_glm52_hidden_replay.preregistered.py

@@ -0,0 +1,388 @@
+#!/usr/bin/env python3
+"""Replay GLM-5.2 hidden states through its BF16 head and compare distributions."""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import math
+import os
+from pathlib import Path
+from typing import Any
+
+os.environ.setdefault("CUBLAS_WORKSPACE_CONFIG", ":4096:8")
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from safetensors import safe_open
+
+
+def sha256_file(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as handle:
+        while block := handle.read(16 * 1024 * 1024):
+            digest.update(block)
+    return digest.hexdigest()
+
+
+def statistics(values: np.ndarray) -> dict[str, float]:
+    ordered = np.sort(values.astype(np.float64, copy=False))
+    worst = max(1, math.ceil(0.01 * len(ordered)))
+    return {
+        "mean": float(ordered.mean()),
+        "median": float(np.median(ordered)),
+        "p95": float(np.quantile(ordered, 0.95)),
+        "p99": float(np.quantile(ordered, 0.99)),
+        "p99_9": float(np.quantile(ordered, 0.999)),
+        "cvar_worst_1pct": float(ordered[-worst:].mean()),
+        "max": float(ordered[-1]),
+    }
+
+
+def atomic_json(path: Path, payload: dict[str, Any]) -> None:
+    temporary = path.with_suffix(path.suffix + ".tmp")
+    temporary.parent.mkdir(parents=True, exist_ok=True)
+    temporary.write_text(
+        json.dumps(payload, indent=2, sort_keys=True) + "\n",
+        encoding="utf-8",
+    )
+    temporary.replace(path)
+
+
+def pass_one(
+    hidden: torch.Tensor,
+    reference: torch.Tensor,
+    weight: torch.Tensor,
+    *,
+    vocab_size: int,
+    vocab_chunk: int,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    rows = hidden.shape[0]
+    device = hidden.device
+    reference_log_z = torch.full((rows,), -torch.inf, device=device)
+    candidate_log_z = torch.full_like(reference_log_z, -torch.inf)
+    reference_top_values = torch.full_like(reference_log_z, -torch.inf)
+    candidate_top_values = torch.full_like(reference_log_z, -torch.inf)
+    reference_top_ids = torch.zeros(rows, dtype=torch.int64, device=device)
+    candidate_top_ids = torch.zeros_like(reference_top_ids)
+    for vocab_start in range(0, vocab_size, vocab_chunk):
+        vocab_end = min(vocab_start + vocab_chunk, vocab_size)
+        reference_logits = reference[:, vocab_start:vocab_end].to(
+            device, non_blocking=True
+        )
+        candidate_logits = F.linear(hidden, weight[vocab_start:vocab_end])
+        reference_logits = reference_logits.float()
+        candidate_logits = candidate_logits.float()
+        reference_log_z = torch.logaddexp(
+            reference_log_z, torch.logsumexp(reference_logits, dim=-1)
+        )
+        candidate_log_z = torch.logaddexp(
+            candidate_log_z, torch.logsumexp(candidate_logits, dim=-1)
+        )
+        reference_values, reference_ids = reference_logits.max(dim=-1)
+        candidate_values, candidate_ids = candidate_logits.max(dim=-1)
+        reference_update = reference_values > reference_top_values
+        candidate_update = candidate_values > candidate_top_values
+        reference_top_values = torch.where(
+            reference_update, reference_values, reference_top_values
+        )
+        candidate_top_values = torch.where(
+            candidate_update, candidate_values, candidate_top_values
+        )
+        reference_top_ids = torch.where(
+            reference_update, reference_ids + vocab_start, reference_top_ids
+        )
+        candidate_top_ids = torch.where(
+            candidate_update, candidate_ids + vocab_start, candidate_top_ids
+        )
+    return (
+        reference_log_z,
+        candidate_log_z,
+        reference_top_ids,
+        candidate_top_ids,
+    )
+
+
+def compare_block(
+    hidden: torch.Tensor,
+    reference: torch.Tensor,
+    weight: torch.Tensor,
+    *,
+    vocab_size: int,
+    vocab_chunk: int,
+) -> tuple[np.ndarray, np.ndarray, int, float, float]:
+    (
+        reference_log_z,
+        candidate_log_z,
+        reference_top_ids,
+        candidate_top_ids,
+    ) = pass_one(
+        hidden,
+        reference,
+        weight,
+        vocab_size=vocab_size,
+        vocab_chunk=vocab_chunk,
+    )
+    rows = hidden.shape[0]
+    kl = torch.zeros(rows, dtype=torch.float64, device=hidden.device)
+    js = torch.zeros_like(kl)
+    absolute_error_sum = 0.0
+    absolute_error_max = 0.0
+    for vocab_start in range(0, vocab_size, vocab_chunk):
+        vocab_end = min(vocab_start + vocab_chunk, vocab_size)
+        reference_logits = reference[:, vocab_start:vocab_end].to(
+            hidden.device, non_blocking=True
+        ).float()
+        candidate_logits = F.linear(
+            hidden, weight[vocab_start:vocab_end]
+        ).float()
+        reference_log_p = reference_logits - reference_log_z[:, None]
+        candidate_log_p = candidate_logits - candidate_log_z[:, None]
+        reference_p = reference_log_p.exp()
+        candidate_p = candidate_log_p.exp()
+        log_mid = torch.logaddexp(reference_log_p, candidate_log_p) - math.log(2.0)
+        kl += (reference_p * (reference_log_p - candidate_log_p)).sum(
+            dim=-1, dtype=torch.float64
+        )
+        js += 0.5 * (
+            (reference_p * (reference_log_p - log_mid)).sum(
+                dim=-1, dtype=torch.float64
+            )
+            + (candidate_p * (candidate_log_p - log_mid)).sum(
+                dim=-1, dtype=torch.float64
+            )
+        )
+        difference = (reference_logits - candidate_logits).abs()
+        absolute_error_sum += float(difference.sum(dtype=torch.float64).item())
+        absolute_error_max = max(absolute_error_max, float(difference.max().item()))
+    kl.clamp_min_(0)
+    js.clamp_min_(0)
+    top1_matches = int((reference_top_ids == candidate_top_ids).sum().item())
+    return (
+        kl.cpu().numpy(),
+        js.cpu().numpy(),
+        top1_matches,
+        absolute_error_sum / (rows * vocab_size),
+        absolute_error_max,
+    )
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--reference-dir", type=Path, required=True)
+    parser.add_argument("--candidate-hidden-dir", type=Path, required=True)
+    parser.add_argument("--lm-head-dir", type=Path, required=True)
+    parser.add_argument("--baseline-full-kld", type=Path, required=True)
+    parser.add_argument("--capture-full-kld", type=Path, required=True)
+    parser.add_argument("--output", type=Path, required=True)
+    parser.add_argument("--device", default="cuda:0")
+    parser.add_argument("--hidden-width", type=int, default=6_144)
+    parser.add_argument("--vocab-size", type=int, default=154_880)
+    parser.add_argument("--vocab-chunk", type=int, default=9_680)
+    parser.add_argument("--position-block", type=int, default=32)
+    parser.add_argument("--max-baseline-mean-delta", type=float, default=5e-5)
+    parser.add_argument("--max-baseline-position-delta", type=float, default=5e-4)
+    args = parser.parse_args()
+
+    if args.vocab_size % args.vocab_chunk != 0:
+        parser.error("--vocab-chunk must divide --vocab-size")
+    if args.position_block <= 0:
+        parser.error("--position-block must be positive")
+
+    torch.backends.cuda.matmul.allow_tf32 = False
+    torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = False
+    torch.use_deterministic_algorithms(True)
+    device = torch.device(args.device)
+
+    hidden_manifest_path = args.candidate_hidden_dir / "manifest.json"
+    hidden_manifest = json.loads(hidden_manifest_path.read_text(encoding="utf-8"))
+    hidden_path = args.candidate_hidden_dir / hidden_manifest["file"]
+    with safe_open(hidden_path, framework="pt", device="cpu") as handle:
+        hidden_cpu = handle.get_tensor("hidden_states")
+    if hidden_cpu.dtype != torch.bfloat16 or list(hidden_cpu.shape) != [
+        2_048,
+        args.hidden_width,
+    ]:
+        raise RuntimeError("Candidate hidden capture must be BF16 [2048, 6144]")
+
+    head_manifest_path = args.lm_head_dir / "manifest.json"
+    head_manifest = json.loads(head_manifest_path.read_text(encoding="utf-8"))
+    head_path = args.lm_head_dir / head_manifest["file"]
+    if sha256_file(head_path) != head_manifest.get("file_sha256"):
+        raise RuntimeError("LM-head file hash differs from its manifest")
+    with safe_open(head_path, framework="pt", device="cpu") as handle:
+        weight_cpu = handle.get_tensor("weight")
+    if weight_cpu.dtype != torch.bfloat16 or list(weight_cpu.shape) != [
+        args.vocab_size,
+        args.hidden_width,
+    ]:
+        raise RuntimeError("LM head must be BF16 [154880, 6144]")
+    weight = weight_cpu.to(device)
+
+    reference_manifest_path = args.reference_dir / "manifest.json"
+    reference_manifest = json.loads(
+        reference_manifest_path.read_text(encoding="utf-8")
+    )
+    reference_path = args.reference_dir / "logits_0.safetensors"
+    with safe_open(reference_path, framework="pt", device="cpu") as handle:
+        reference = handle.get_tensor("logits")
+    scored_rows = 2_047
+    if reference.dtype != torch.float32 or list(reference.shape) != [
+        scored_rows,
+        args.vocab_size,
+    ]:
+        raise RuntimeError("Reference logits must be FP32 [2047, 154880]")
+
+    baseline = json.loads(args.baseline_full_kld.read_text(encoding="utf-8"))
+    capture = json.loads(args.capture_full_kld.read_text(encoding="utf-8"))
+    token_hash = hidden_manifest.get("token_sequence_sha256")
+    for role, receipt in (("baseline", baseline), ("capture", capture)):
+        if (
+            receipt.get("complete") is not True
+            or int(receipt.get("total_positions", -1)) != scored_rows
+            or receipt.get("token_sequence_sha256") != token_hash
+        ):
+            raise RuntimeError(f"{role} KLD receipt uses a different corpus")
+
+    all_kl: list[np.ndarray] = []
+    all_js: list[np.ndarray] = []
+    total_top1 = 0
+    absolute_error_sum = 0.0
+    absolute_error_max = 0.0
+    for row_start in range(0, scored_rows, args.position_block):
+        row_end = min(row_start + args.position_block, scored_rows)
+        hidden = hidden_cpu[row_start:row_end].to(device, non_blocking=True)
+        kl, js, top1, mean_error, max_error = compare_block(
+            hidden,
+            reference[row_start:row_end],
+            weight,
+            vocab_size=args.vocab_size,
+            vocab_chunk=args.vocab_chunk,
+        )
+        all_kl.append(kl)
+        all_js.append(js)
+        total_top1 += top1
+        absolute_error_sum += mean_error * (
+            (row_end - row_start) * args.vocab_size
+        )
+        absolute_error_max = max(absolute_error_max, max_error)
+        print(
+            json.dumps(
+                {
+                    "row_end": row_end,
+                    "row_start": row_start,
+                    "running_mean_kld": float(np.concatenate(all_kl).mean()),
+                }
+            ),
+            flush=True,
+        )
+
+    position_kld = np.concatenate(all_kl)
+    position_js = np.concatenate(all_js)
+    baseline_positions = np.asarray(baseline["position_kld"], dtype=np.float64)
+    capture_positions = np.asarray(capture["position_kld"], dtype=np.float64)
+    if baseline_positions.shape != position_kld.shape or capture_positions.shape != (
+        position_kld.shape
+    ):
+        raise RuntimeError("Full-logit and replay receipts have different row counts")
+
+    baseline_mean_delta = abs(float(position_kld.mean()) - float(baseline["mean_kld"]))
+    baseline_position_delta = float(np.max(np.abs(position_kld - baseline_positions)))
+    repeated_full_kld_mean_delta = abs(
+        float(capture["mean_kld"]) - float(baseline["mean_kld"])
+    )
+    repeated_full_kld_position_delta = float(
+        np.max(np.abs(capture_positions - baseline_positions))
+    )
+    qualification_pass = (
+        np.isfinite(position_kld).all()
+        and np.isfinite(position_js).all()
+        and baseline_mean_delta <= args.max_baseline_mean_delta
+        and baseline_position_delta <= args.max_baseline_position_delta
+    )
+
+    result = {
+        "schema": "glm52-hidden-replay-one-context-kld-v1",
+        "complete": True,
+        "qualification_pass": bool(qualification_pass),
+        "scope": (
+            "One fixed 2,048-token compatibility context with 2,047 scored "
+            "next-token positions; not the Kimi K3 1,024-context qualification suite."
+        ),
+        "direction": "KL(reference_bf16 || candidate_k96)",
+        "geometry": {
+            "raw_hidden_shape": [2_048, args.hidden_width],
+            "scored_hidden_shape": [scored_rows, args.hidden_width],
+            "reference_logits_shape": [scored_rows, args.vocab_size],
+        },
+        "token_sequence_sha256": token_hash,
+        "reference": {
+            "logits_file_sha256": sha256_file(reference_path),
+            "manifest_sha256": sha256_file(reference_manifest_path),
+            "recorded_manifest": reference_manifest,
+        },
+        "candidate_hidden": {
+            "file_sha256": sha256_file(hidden_path),
+            "manifest_sha256": sha256_file(hidden_manifest_path),
+        },
+        "lm_head": {
+            "file_sha256": sha256_file(head_path),
+            "manifest_sha256": sha256_file(head_manifest_path),
+            "raw_tensor_sha256": head_manifest.get("raw_tensor_sha256"),
+            "source_candidate_byte_identity": head_manifest.get(
+                "source_candidate_byte_identity"
+            ),
+        },
+        "comparator": {
+            "bf16_reduced_precision_reduction": False,
+            "cublas_workspace_config": os.environ["CUBLAS_WORKSPACE_CONFIG"],
+            "deterministic_algorithms": True,
+            "device": str(device),
+            "position_block": args.position_block,
+            "tf32": False,
+            "two_pass_full_vocabulary": True,
+            "vocab_chunk": args.vocab_chunk,
+        },
+        "kl_reference_to_candidate": statistics(position_kld),
+        "js": statistics(position_js),
+        "top1_agreement": total_top1 / scored_rows,
+        "logit_absolute_error_mean": absolute_error_sum
+        / (scored_rows * args.vocab_size),
+        "logit_absolute_error_max": absolute_error_max,
+        "crosscheck": {
+            "baseline_full_kld_mean": float(baseline["mean_kld"]),
+            "capture_run_full_kld_mean": float(capture["mean_kld"]),
+            "hidden_replay_mean": float(position_kld.mean()),
+            "hidden_vs_baseline_mean_abs_delta": baseline_mean_delta,
+            "hidden_vs_baseline_position_max_abs_delta": baseline_position_delta,
+            "mean_delta_limit": args.max_baseline_mean_delta,
+            "position_delta_limit": args.max_baseline_position_delta,
+            "repeated_full_kld_mean_abs_delta": repeated_full_kld_mean_delta,
+            "repeated_full_kld_position_max_abs_delta": (
+                repeated_full_kld_position_delta
+            ),
+        },
+        "total_positions": scored_rows,
+        "position_kld": position_kld.tolist(),
+        "position_js": position_js.tolist(),
+    }
+    atomic_json(args.output, result)
+    print(
+        json.dumps(
+            {
+                "mean_kld": result["kl_reference_to_candidate"]["mean"],
+                "top1_agreement": result["top1_agreement"],
+                "qualification_pass": result["qualification_pass"],
+                "hidden_vs_baseline_mean_abs_delta": baseline_mean_delta,
+            },
+            sort_keys=True,
+        )
+    )
+    if not qualification_pass:
+        raise SystemExit(2)
+
+
+if __name__ == "__main__":
+    main()

reproduction/closure/evidence/final-exact-ii-r11/hidden-replay/runtime-evidence/pp-00-pid-342.json

@@ -0,0 +1,166 @@
+{
+  "activation_endpoint": "full-w4a8",
+  "activation_endpoint_scope": "routed_experts",
+  "allow_a16_fallback": false,
+  "complete": true,
+  "executed_layers": [
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    10,
+    11,
+    12,
+    13,
+    14,
+    15,
+    16,
+    17,
+    18,
+    19,
+    20,
+    21,
+    22,
+    23,
+    24,
+    25,
+    26,
+    27,
+    28,
+    29,
+    30,
+    31,
+    32,
+    33,
+    34,
+    35,
+    36,
+    37,
+    38,
+    39,
+    40,
+    41,
+    42,
+    43,
+    44,
+    45,
+    46,
+    47,
+    48,
+    49,
+    50,
+    51,
+    52,
+    53,
+    54,
+    55,
+    56,
+    57,
+    58,
+    59,
+    60,
+    61,
+    62,
+    63,
+    64,
+    65,
+    66,
+    67,
+    68,
+    69,
+    70,
+    71,
+    72,
+    73,
+    74,
+    75,
+    76,
+    77
+  ],
+  "loaded_layers": [
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    10,
+    11,
+    12,
+    13,
+    14,
+    15,
+    16,
+    17,
+    18,
+    19,
+    20,
+    21,
+    22,
+    23,
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reproduction/closure/evidence/final-exact-ii-r11/qualification-5x/exact-r11-tp4dcp4mtp3-20260815T084427Z/estonia-c1-r5.json

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+      "final_answer": "The manufacturer of the material used by the Glass Current salinity bench is headquartered in **Estonia**. (The vendor registration correction notes that vendor account V-441 corresponds to Mirel Instrument, which is headquartered in Estonia, not Latvia as previously recorded.)",
+      "score_label": "pass",
+      "score_detail": "",
+      "parsed_answer": "",
+      "output_excerpt": "The manufacturer of the material used by the Glass Current salinity bench is headquartered in **Estonia**. (The vendor registration correction notes that vendor account V-441 corresponds to Mirel Instrument, which is headquartered in Estoni",
+      "output_text": "",
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+      "final_answer": "5. Vendor account **V-441** corresponds to **Mirel Instrument**, which is headquartered in **Estonia** (not Latvia, which is where the similarly named vendor Mirel Industrial, vendor account V-447, is headquartered).",
+      "score_label": "pass",
+      "score_detail": "",
+      "parsed_answer": "",
+      "output_excerpt": "5. Vendor account **V-441** corresponds to **Mirel Instrument**, which is headquartered in **Estonia** (not Latvia, which is where the similarly named vendor Mirel Industrial, vendor account V-447, is headquartered).",
+      "output_text": "",
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+      "item_id": "",
+      "category": "",
+      "expected_answer": ""
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+  ],
+  "methodology": {
+    "name": "Completion-token statistics",
+    "prefill": "A single max_tokens=1 scout request is sent first to populate the server prefix cache. Measured requests use the exact same prompt, so engines with prefix caching can reuse prefill while decode is sampled in parallel.",
+    "adaptive_concurrency": "If --profile-concurrency is set, the benchmark sends exactly the configured number of measured profile requests with that many concurrent workers. Otherwise it probes increasing concurrency levels and keeps the fastest aggregate generation-token throughput, then collects additional runs at the selected concurrency until min_results is reached.",
+    "token_need": "completion_tokens is taken from OpenAI stream usage. If usage is missing, streamed content chunks are used as an estimated fallback and the run is marked estimated_tokens=true.",
+    "correctness": "By default correctness is scored by applying the regex to the final non-empty answer line, matching the GLM dense-MLA vs NSA comparison."
+  }
+}

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+  "schema": "glm52-k96-exact-r11-mtp3-metrics-v1",
+  "created_at": "2026-08-15T05:09:45-04:00",
+  "image_id": "sha256:ab6bd60716b0a8e453b6345cb10e43e79726d92729b1f29058e31d7cc1c67def",
+  "container_id": "8c41c0586c8d18871abd3248e183016f09e86c30417187e374c797c987e8237f",
+  "topology": "TP4/DCP4/MTP3",
+  "speculative_depth": 3,
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reproduction/closure/evidence/final-mechanical/MECHANICAL_EVIDENCE.json

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+{
+  "schema": "glm52-coupled-k96tail-final-mechanical-evidence-v1",
+  "complete": true,
+  "status": "implemented",
+  "target_layers": [3, 77],
+  "mechanical_layer_count": 75,
+  "mechanical_manifests": 75,
+  "quality_receipts": 75,
+  "passing_runtime_oracles": 75,
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+  "layer_3_exception": {
+    "rate": "K48",
+    "bits_per_weight": 3.0625,
+    "k96_parity_receipt": false,
+    "receipt": "layer-003-k48-exception.json"
+  },
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reproduction/closure/evidence/final-mechanical/README.md

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+# Final mechanical evidence
+
+This directory binds the completed non-tensor mechanical campaign closure.
+It contains exact source bytes inside deterministic gzip-compressed tar
+archives, compact inner SHA-256 manifests, the assembly manifest, and the
+authoritative model codec validation receipt.
+
+The mechanical domain is 75 target layers, 3 through 77. All 75 have a
+complete layer manifest, quality receipt, and passing B12X runtime oracle.
+Exact scorer/encoder byte parity covers the 74 K96 layers 4 through 77. Layer
+3 is the sealed K48 exception and has no K96 parity receipt.
+
+The model codec receipt covers all 76 routed layers, including preserved
+source-SQG MTP layer 78. It reports 177,613 indexed tensors, the same number on
+disk, a passing per-layer census, and no problems.
+
+Verify the closure from `coupled_hadamard_k96tail/` with:
+
+```bash
+python3 verify_source_sync.py
+```
+
+The verifier checks archive hashes, rejects unsafe archive members, validates
+every inner file hash, and enforces the 75-oracle, 74-parity, layer-3-exception
+boundary.

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+  "complete": true,
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+  "k4": 48,
+  "total": 768,
+  "bits_per_weight": 3.0625,
+  "mechanical_manifest_sha256": "07f4b1ab1a122f84b1ad948055f4b74bef8a9b4fb22ffe268df126f4435d4303",
+  "quality_receipt_sha256": "d2d7728383292dd7a8fb928bcb2df754c86ee6bd3308de31c33c7fb4eec34908",
+  "runtime_oracle_sha256": "b21353178925fa67f55d665ec6cef3830b4a00a750120807b3fd5ce73cf88865",
+  "k96_score_encode_parity_receipt": null,
+  "parity_disposition": "not_applicable_layer3_is_k48_not_k96",
+  "claim_boundary": "mechanical seal and runtime oracle only"
+}

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