Audit mobile speech heads and S25 VLA latent impact

2026-09-24 07:56:17 +08:00
1 parent 1949c31f80
commit d71d39f656
@@ -0,0 +1,160 @@
+#!/usr/bin/env python3
+"""Audit one MiniCPM-o speech-head decode step against pinned ONNX CPU tensors."""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+from pathlib import Path
+
+import numpy as np
+
+
+SOURCE_SHA256 = "78cf64804f11ad269ee4180da61588ccc5eefe2942773227345499756e4cc229"
+FIXTURE_SHA256 = "65919ea1f351feec33c1415a3e3db76d60bc9c2beb3d49a72af75dcb418e83b4"
+REFERENCE_SHA256 = "902adcceadbe761d938ef2ade51cf953c8917ea1abfaee33a10c84e300c1bd96"
+COMPILE_OPTIONS = {
+    "--target_runtime qnn_context_binary --quantize_full_type float16",
+    "--target_runtime qnn_dlc --qnn_options default_graph_htp_precision=FLOAT16",
+}
+INPUT_NAMES = ["x", "cos", "sin", "mask"] + [
+    name for layer in range(20) for name in (f"k_cache_{layer}", f"v_cache_{layer}")
+]
+OUTPUT_NAMES = ["logits"] + [
+    name for layer in range(20) for name in (f"k_new_{layer}", f"v_new_{layer}")
+]
+
+
+def file_record(path: Path) -> dict:
+    with path.open("rb") as source:
+        digest = hashlib.file_digest(source, "sha256").hexdigest()
+    return {"bytes": path.stat().st_size, "sha256": digest}
+
+
+def read_array(data: np.lib.npyio.NpzFile, name: str, shape: tuple[int, ...]) -> np.ndarray:
+    value = np.asarray(data[name])
+    if value.shape != shape or value.dtype != np.float32 or not np.isfinite(value).all():
+        raise ValueError(f"{name}: expected finite FP32 {shape}, got {value.shape}/{value.dtype}")
+    return value
+
+
+def compare(reference: np.ndarray, actual: np.ndarray) -> dict:
+    source = reference.astype(np.float64).ravel()
+    observed = actual.astype(np.float64).ravel()
+    error = source - observed
+    source_norm = float(np.linalg.norm(source))
+    error_norm = float(np.linalg.norm(error))
+    return {
+        "relative_l2": error_norm / source_norm if source_norm else (0.0 if error_norm == 0 else None),
+        "max_abs_error": float(np.max(np.abs(error))),
+        "source_rms": float(np.sqrt(np.mean(source * source))),
+        "device_rms": float(np.sqrt(np.mean(observed * observed))),
+    }
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--device-name", required=True)
+    parser.add_argument("--source-onnx", type=Path, required=True)
+    parser.add_argument("--fixture", type=Path, required=True)
+    parser.add_argument("--cpu-reference", type=Path, required=True)
+    parser.add_argument("--compile-report", type=Path, required=True)
+    parser.add_argument("--inference-report", type=Path, required=True)
+    parser.add_argument("--device-output", type=Path, required=True)
+    parser.add_argument("--output-report", type=Path, required=True)
+    args = parser.parse_args()
+
+    compiled = json.loads(args.compile_report.read_text(encoding="utf-8"))
+    inferred = json.loads(args.inference_report.read_text(encoding="utf-8"))
+    if compiled.get("status") != "SUCCESS" or inferred.get("status") != "SUCCESS":
+        raise ValueError("compile and inference must be terminal successes")
+    if compiled.get("source_model_id") != "mn4gjjpvn" or compiled.get("options") not in COMPILE_OPTIONS:
+        raise ValueError("compile source/options differ from the pinned speech-head artifact")
+    if inferred.get("model_id") != compiled.get("target_model_id"):
+        raise ValueError("inference used a different target artifact")
+    if inferred.get("input_dataset_id") not in {"d7gwxe3y2", "d7zn8rk57"}:
+        raise ValueError("inference did not use a retained matching fixture dataset")
+    if (compiled.get("device", {}).get("name") != args.device_name
+            or inferred.get("device", {}).get("name") != args.device_name):
+        raise ValueError("jobs did not use the exact requested device")
+
+    files = {
+        name: file_record(path)
+        for name, path in {
+            "source_onnx": args.source_onnx,
+            "fixture": args.fixture,
+            "cpu_reference": args.cpu_reference,
+            "device_output": args.device_output,
+        }.items()
+    }
+    for name, expected in {
+        "source_onnx": SOURCE_SHA256,
+        "fixture": FIXTURE_SHA256,
+        "cpu_reference": REFERENCE_SHA256,
+    }.items():
+        if files[name]["sha256"] != expected:
+            raise ValueError(f"{name} differs from the pinned artifact")
+
+    with np.load(args.fixture, allow_pickle=False) as fixture:
+        if fixture.files != INPUT_NAMES:
+            raise ValueError("fixture input names/order differ")
+        read_array(fixture, "x", (1, 1, 768))
+        for name in ("cos", "sin"):
+            read_array(fixture, name, (1, 1, 1, 64))
+        read_array(fixture, "mask", (1, 1, 1, 257))
+        for name in INPUT_NAMES[4:]:
+            read_array(fixture, name, (1, 12, 256, 64))
+
+    comparisons = {}
+    with np.load(args.cpu_reference, allow_pickle=False) as reference, np.load(
+        args.device_output, allow_pickle=False
+    ) as output:
+        if reference.files != OUTPUT_NAMES:
+            raise ValueError("CPU output names/order differ")
+        expected_keys = [f"output_{index}__0" for index in range(len(OUTPUT_NAMES))]
+        if output.files != expected_keys:
+            raise ValueError("device output names/order differ")
+        for index, name in enumerate(OUTPUT_NAMES):
+            shape = (1, 6562) if index == 0 else (1, 12, 1, 64)
+            source_value = read_array(reference, name, shape)
+            device_value = read_array(output, expected_keys[index], shape)
+            comparisons[name] = compare(source_value, device_value)
+            if name == "logits":
+                comparisons[name]["cpu_top1"] = int(np.argmax(source_value[0]))
+                comparisons[name]["device_top1"] = int(np.argmax(device_value[0]))
+                comparisons[name]["top1_matches"] = bool(
+                    comparisons[name]["cpu_top1"] == comparisons[name]["device_top1"]
+                )
+
+    report = {
+        "scope": "one fixed-cache 20-layer MiniCPM-o speech-head decode step; no complete multimodal stream",
+        "status": "component_inference_numeric_measured",
+        "device": inferred["device"],
+        "compile_job_id": compiled["job_id"],
+        "inference_job_id": inferred["job_id"],
+        "source_model_id": compiled["source_model_id"],
+        "target_model_id": compiled["target_model_id"],
+        "input_dataset_id": inferred["input_dataset_id"],
+        "requested_run_options": inferred["options"],
+        "files": files,
+        "tensor_comparison": comparisons,
+        "cache_max_relative_l2": max(comparisons[name]["relative_l2"] for name in OUTPUT_NAMES[1:]),
+        "limits": [
+            "The historical ONNX export does not attest the exact source checkpoint revision.",
+            "One retained decode fixture cannot establish speech-token or generated-audio quality.",
+            "Actual NPU placement requires a device profile; a request or QNN artifact alone is insufficient.",
+            "Thinker, audio/vision encoders, vocoder, state loop and complete Omni request were not tested.",
+        ],
+    }
+    args.output_report.write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8")
+    print(json.dumps({
+        "status": report["status"],
+        "device": report["device"]["name"],
+        "logits": comparisons["logits"],
+        "cache_max_relative_l2": report["cache_max_relative_l2"],
+    }, indent=2))
+
+
+if __name__ == "__main__":
+    main()
@@ -59,17 +59,20 @@ def main() -> None:
    parser.add_argument("--inference-report", type=Path, required=True)
    parser.add_argument("--device-output", type=Path, required=True)
    parser.add_argument("--output-report", type=Path, required=True)
+    parser.add_argument("--expected-compile-options", default=COMPILE_OPTIONS)
+    parser.add_argument("--expected-run-options", default="--compute_unit npu")
    args = parser.parse_args()

    compiled = json.loads(args.compile_report.read_text(encoding="utf-8"))
    inferred = json.loads(args.inference_report.read_text(encoding="utf-8"))
    if compiled.get("status") != "SUCCESS" or inferred.get("status") != "SUCCESS":
        raise ValueError("compile and inference must both be terminal successes")
-    if compiled.get("source_model_id") != "mq3xre4rq" or compiled.get("options") != COMPILE_OPTIONS:
+    if compiled.get("source_model_id") != "mq3xre4rq" or compiled.get("options") != args.expected_compile_options:
        raise ValueError("compile source or options differ from the pinned talker artifact")
    if inferred.get("model_id") != compiled.get("target_model_id"):
        raise ValueError("inference did not use the audited target artifact")
-    if inferred.get("input_dataset_id") != "d7dw6pgy2" or inferred.get("options") != "--compute_unit npu":
+    if (inferred.get("input_dataset_id") != "d7dw6pgy2"
+            or inferred.get("options") != args.expected_run_options):
        raise ValueError("inference fixture or requested compute unit differs")
    if inferred.get("device") != compiled.get("device"):
        raise ValueError("compile/inference device metadata differs")
@@ -140,7 +143,7 @@ def main() -> None:
        "limits": [
            "The historical source export does not attest an exact checkpoint revision.",
            "The fixture is synthetic and has no token-to-audio quality tolerance.",
-            "Requested NPU is not actual placement without a device profile.",
+            "Requested compute unit is not actual placement without a device profile.",
            "No predictor, vocoder, persistent cache loop or complete TTS stream was tested.",
        ],
    }
@@ -0,0 +1,163 @@
+#!/usr/bin/env python3
+"""Measure policy-action sensitivity to a hosted Cosmos encoder latent.
+
+The full policy runs on local CPU with identical synthetic state/noise. Its
+encoder is replaced by pinned source/device latent tensors from one fixture.
+This is a numerical handoff probe, not a device-local or robot-task run.
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import os
+import time
+from pathlib import Path
+
+
+CHECKPOINT_SHA256 = "586799539888c69c35f84d083e559cc5f0ba887801d000e70e65f58d3f85c313"
+FIXTURE_SHA256 = "dcbaf1391ca914d4bbccc1cb384daf05666e771d9cdae90c4f069ec8fcf05368"
+
+
+def sha256(path: Path) -> str:
+    with path.open("rb") as source:
+        return hashlib.file_digest(source, "sha256").hexdigest()
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    for name in ("model-dir", "cosmos-dir", "processor-dir", "fixture", "device-output", "audit-report", "output"):
+        parser.add_argument(f"--{name}", type=Path, required=True)
+    args = parser.parse_args()
+
+    os.environ["INTERNVLA_A1_COSMOS_DIR"] = str(args.cosmos_dir.resolve(strict=True))
+    os.environ["INTERNVLA_A1_PROCESSOR_DIR"] = str(args.processor_dir.resolve(strict=True))
+    os.environ["HF_HUB_OFFLINE"] = "1"
+
+    import numpy as np
+    import torch
+
+    from vllm_omni.diffusion.data import OmniDiffusionConfig
+    from vllm_omni.diffusion.registry import initialize_model
+    from vllm_omni.diffusion.request import OmniDiffusionRequest
+    from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch
+    from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+
+    checkpoint = args.model_dir / "model.safetensors"
+    if sha256(checkpoint) != CHECKPOINT_SHA256 or sha256(args.fixture) != FIXTURE_SHA256:
+        raise ValueError("checkpoint or encoder fixture differs from pinned source")
+    audit = json.loads(args.audit_report.read_text(encoding="utf-8"))
+    if (audit.get("status") != "component_inference_numeric_measured"
+            or audit.get("device", {}).get("name") != "Samsung Galaxy S25"
+            or audit.get("files", {}).get("device_output", {}).get("sha256") != sha256(args.device_output)):
+        raise ValueError("hosted latent lacks audited S25 provenance")
+    with np.load(args.fixture, allow_pickle=False) as fixture:
+        source_latent = np.asarray(fixture["pattern_reference"])
+    with np.load(args.device_output, allow_pickle=False) as output:
+        if output.files != ["output_0__0"]:
+            raise ValueError("unexpected hosted output layout")
+        device_latent = np.asarray(output["output_0__0"])
+    if (source_latent.shape != (6, 16, 32, 32) or device_latent.shape != source_latent.shape
+            or source_latent.dtype != np.float32 or device_latent.dtype != np.float32
+            or not np.isfinite(source_latent).all() or not np.isfinite(device_latent).all()):
+        raise ValueError("latents violate the finite FP32 six-frame contract")
+
+    config = OmniDiffusionConfig(
+        model=str(args.model_dir.resolve(strict=True)),
+        model_class_name="InternVLAA1Pipeline",
+        dtype=torch.bfloat16,
+        custom_pipeline_args={
+            "device": "cpu", "dtype": "bfloat16", "compile_model": False,
+            "enable_regional_compile": False, "enable_warmup": False,
+            "strict_load": True,
+            "processor_model_name": str(args.processor_dir.resolve(strict=True)),
+        },
+    )
+    started = time.perf_counter()
+    pipeline = initialize_model(config)
+    load_seconds = time.perf_counter() - started
+    if pipeline.runtime_mode() != "real_checkpoint_loaded":
+        raise RuntimeError("real policy checkpoint was not loaded")
+    if {parameter.device.type for parameter in pipeline.policy.parameters()} != {"cpu"}:
+        raise RuntimeError("policy parameters did not stay on CPU")
+    inputs = pipeline._build_fake_batch_inputs()
+    noise = torch.zeros((1, pipeline.config.chunk_size, pipeline.config.max_action_dim), dtype=torch.float32)
+    cosmos = pipeline.policy.model.cosmos
+    original_encoder = cosmos._enc_model
+
+    class PinnedLatent(torch.nn.Module):
+        def __init__(self, latent: np.ndarray) -> None:
+            super().__init__()
+            self.latent = torch.from_numpy(latent.copy())
+            self.calls = 0
+
+        def forward(self, pixels: torch.Tensor) -> torch.Tensor:
+            if tuple(pixels.shape) != (6, 3, 256, 256) or pixels.device.type != "cpu":
+                raise RuntimeError("unexpected policy encoder input")
+            self.calls += 1
+            return self.latent.to(dtype=pixels.dtype)
+
+    def actions(encoder: PinnedLatent) -> np.ndarray:
+        cosmos._enc_model = encoder
+        result = pipeline.forward(DiffusionRequestBatch(requests=[
+            OmniDiffusionRequest(
+                prompt="",
+                sampling_params=OmniDiffusionSamplingParams(extra_args={
+                    "batch_inputs": inputs, "noise": noise, "decode_image": False,
+                }),
+                request_id="internvla-hosted-latent-synthetic",
+            )
+        ]))
+        if result.error:
+            raise RuntimeError(result.error)
+        tensor = result.output["payload"]["actions"]
+        if tuple(tensor.shape) != (1, 50, 32) or tensor.device.type != "cpu" or not torch.isfinite(tensor).all():
+            raise RuntimeError("bad policy action tensor")
+        return tensor.detach().float().cpu().contiguous().numpy()
+
+    source_encoder = PinnedLatent(source_latent)
+    hosted_encoder = PinnedLatent(device_latent)
+    try:
+        source_actions = actions(source_encoder)
+        hosted_actions = actions(hosted_encoder)
+        source_repeat = actions(source_encoder)
+    finally:
+        cosmos._enc_model = original_encoder
+    if not np.array_equal(source_actions, source_repeat):
+        raise RuntimeError("CPU source policy changed across the paired calls")
+    source = source_actions.astype(np.float64).ravel()
+    hosted = hosted_actions.astype(np.float64).ravel()
+    delta = source - hosted
+    report = {
+        "scope": "S25 hosted Cosmos latent injected into local CPU InternVLA policy with synthetic state/noise; no device-local policy or robot-task result",
+        "status": "synthetic_action_sensitivity_measured",
+        "policy_checkpoint_sha256": CHECKPOINT_SHA256,
+        "fixture_sha256": FIXTURE_SHA256,
+        "hosted_output_sha256": sha256(args.device_output),
+        "hosted_inference_job_id": audit["inference_job_id"],
+        "policy_torch": torch.__version__,
+        "policy_load_seconds_not_request_timing": load_seconds,
+        "encoder_calls": {"source": source_encoder.calls, "hosted": hosted_encoder.calls},
+        "action_shape": list(source_actions.shape),
+        "source_repeat_bitwise_equal": True,
+        "action_relative_l2": float(np.linalg.norm(delta) / np.linalg.norm(source)),
+        "action_max_abs": float(np.max(np.abs(delta))),
+        "action_cosine": float(np.dot(source, hosted) / (np.linalg.norm(source) * np.linalg.norm(hosted))),
+        "source_actions_sha256": hashlib.sha256(source_actions.tobytes()).hexdigest(),
+        "hosted_actions_sha256": hashlib.sha256(hosted_actions.tobytes()).hexdigest(),
+        "limits": [
+            "The policy ran on local CPU; the device only executed the isolated encoder fixture.",
+            "The encoder input was a synthetic pattern and policy state/noise were synthetic zeros.",
+            "No real-observation reference actions, physical units/time, or robot-task tolerance exist.",
+            "No latency, memory admission, cancellation or sustained device power gate was tested.",
+        ],
+    }
+    args.output.write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8")
+    print(json.dumps({key: report[key] for key in (
+        "status", "action_relative_l2", "action_max_abs", "action_cosine", "source_repeat_bitwise_equal"
+    )}, indent=2))
+
+
+if __name__ == "__main__":
+    main()
@@ -1,9 +1,11 @@
 # 2026-09-24 Qualcomm component expansion

-The [Qwen3-TTS vocoder study](evidence/qwen_tts_vocoder_qualcomm/README.md) reuses a retained fixed-shape Code2Wav export and historical Workbench input to test numerical behavior on SA8775P and other Qualcomm settings. The fresh SA8775P GPU-requested inference produces a saturated waveform, while the same binary's CPU-requested output on the exact SA device matches ONNX CPU within 4.13e-6 relative L2. RB3 produces an unsaturated but 15.73%-divergent waveform; its fresh 100-sample mixed CPU/GPU component p50/p95 was 3.099/3.133 s, not a usable TTS timing claim. X Elite ONNX CPU output matches the local ONNX Runtime reference on the retained fixture; its 94-sample component p50/p95 was 6.384/6.644 s with all execution-detail rows on CPU. Corrected X Elite DirectML inference and profile both failed during provider initialization; separate FP16 QNN DLC compilation passed, with NPU-requested inference pending. SA CPU placement profiling and FP32-preserving GPU control remain pending. No complete on-device TTS stream or model-quality qualification is claimed.
+The [Qwen3-TTS vocoder study](evidence/qwen_tts_vocoder_qualcomm/README.md) reuses a retained fixed-shape Code2Wav export and historical Workbench input to test numerical behavior on SA8775P and other Qualcomm settings. The fresh SA8775P GPU-requested inference produces a saturated waveform, while the same binary's CPU-requested output on the exact SA device matches ONNX CPU within 4.13e-6 relative L2. A FP32-preserving GPU-option control still saturated 84.89% of samples, so that option did not repair the tested path. RB3 produces an unsaturated but 15.73%-divergent waveform; its fresh 100-sample mixed CPU/GPU component p50/p95 was 3.099/3.133 s, not a usable TTS timing claim. X Elite ONNX CPU output matches the local ONNX Runtime reference on the retained fixture; its 94-sample component p50/p95 was 6.384/6.644 s with all execution-detail rows on CPU. Corrected X Elite DirectML inference and profile both failed during provider initialization; separate FP16 QNN DLC compilation passed, with NPU-requested inference pending. SA CPU placement profiling remains pending. No complete on-device TTS stream or model-quality qualification is claimed.

 A local HX370 x86-64 LiteRT CPU control of the identical SA/RB3 TFLite binary matched the original ONNX CPU waveform within 3.45e-6 relative L2. The SA saturation is therefore not universal to the converted artifact; hosted CPU and GPU precision controls are still needed to isolate its device/runtime cause.

-The [talker decode-step attempt](evidence/qwen_tts_talker_qualcomm/README.md) pins a separate 28-layer source and synthetic cache fixture. Historical FP16 QNN context-binary compiles passed for S24, S25, SA8775P and X Elite, with same-fixture inference pending; RB3 rejected that floating-point-input artifact. Compile success is not a numerical or end-to-end pass.
+The [talker decode-step attempt](evidence/qwen_tts_talker_qualcomm/README.md) pins a separate 28-layer source and synthetic cache fixture. Historical FP16 QNN context-binary inference executed on S24, S25, SA8775P and X Elite, but all four changed the CPU top-1 speech token with 8.57–9.43% logits relative L2. These artifacts are numerically unqualified on the fixture. RB3 rejected the floating-point-input artifact, and an X Elite ONNX CPU fallback compile is pending. No complete talker loop or TTS stream passed.

-The [InternVLA Cosmos S25 attempt](evidence/internvla_cosmos_qualcomm/README.md) repackages the real policy's image encoder from its parity-tested ONNX export. FLOAT16 QNN DLC compilation passed; same-fixture NPU-requested inference was pending. Full action-policy support remains unqualified.
+The [MiniCPM-o speech-head study](evidence/minicpmo_tts_qualcomm/README.md) recovered the real 20-layer ONNX source and a matching retained fixture. S24 and S25 FP16 context-binary outputs preserved the ONNX CPU top-1 speech token with 0.346%/0.360% logits relative L2 on one step. FP16 QNN DLC compiles passed for SA8775P, X Elite and RB3, with same-fixture NPU-requested inference pending. The complete multimodal model remains unqualified on those devices.
+
+The [InternVLA Cosmos S25 attempt](evidence/internvla_cosmos_qualcomm/README.md) repackages the real policy's image encoder from its parity-tested ONNX export. FLOAT16 QNN DLC inference returned a latent within 0.0524% relative L2 of the source on one pattern. Injecting it into the real policy on local CPU changed synthetic actions by 0.742% relative L2, without a task tolerance. NPU placement profiling was pending; device-local action-policy support remains unqualified.
@@ -2,8 +2,8 @@

 This tests the **real Place_Markpen Cosmos CI8x8 image encoder component**, not the action policy or complete InternVLA. The [source export](../../../e2e_expansion_20260923/evidence/internvla_cosmos_amd_npu/README.md) pins the checkpoint, FP32 `[6,3,256,256]` input, `[6,16,32,32]` output, two CPU parity fixtures and the original external-data ONNX files. The external data were embedded into one 125,112,088-byte ONNX file outside Git at `/home/zhout/project/edge_infer/models/InternVLA-cosmos-onnx/cosmos_encoder_embedded.onnx` (SHA256 `656a5263f584851a1457ef6dcf60b995f7c67b056b3c90a4f33c814f7caf9ec9`). The embedded form retained reference-relative ONNX CPU L2 errors of `8.74e-7` and `2.29e-6` on ramp and pattern fixtures.

-Workbench source model `mn4okj3rq` was [submitted for Galaxy S25 QNN DLC compilation](s25/compile_submission.json) as job `jpxl4d1jp`, with `default_graph_htp_precision=FLOAT16`. The [compile](s25/compile_report.json) succeeded and produced target model `mqkp12rxm`. A [same-fixture pattern inference](s25/inference_submission.json) requesting NPU was submitted as `jp8ex43zp` and remained pending at this evidence snapshot. Compile success does not establish NPU execution, output parity, action quality, latency, or full-policy support.
+Workbench source model `mn4okj3rq` was [submitted for Galaxy S25 QNN DLC compilation](s25/compile_submission.json) as job `jpxl4d1jp`, with `default_graph_htp_precision=FLOAT16`. The [compile](s25/compile_report.json) succeeded and produced target model `mqkp12rxm`. [Same-fixture pattern inference](s25/inference_report.json) requesting NPU also succeeded. The [audit](s25/audit_report.json) found **0.0524% relative L2 / 65.61 dB SNR** versus the pinned source latent, with finite FP32 `[6,16,32,32]` output. This establishes numerical behavior on one synthetic encoder fixture; requested NPU still needs the [placement profile](s25/profile_submission.json), which was pending at this snapshot.

-The [generic collector](../../../../experiments/collect_aihub_component.py) can retrieve the pending output, and the [Cosmos auditor](../../../../experiments/audit_internvla_cosmos_qualcomm.py) checks source/fixture hashes, target/device/dataset identity, latent shape and finite numerical difference against the pinned source reference. Neither script converts an encoder-only result to policy support.
+The [generic collector](../../../../experiments/collect_aihub_component.py) preserves terminal jobs, and the [Cosmos auditor](../../../../experiments/audit_internvla_cosmos_qualcomm.py) checks source/fixture hashes, target/device/dataset identity, latent shape and finite numerical difference against the pinned source reference. A separate [synthetic action sensitivity check](s25/action_sensitivity.json) injected either the pinned source or S25 latent into the same real Place_Markpen policy on **local CPU**, with zero state/noise. The [reproducer](../../../../experiments/probe_internvla_hosted_latent_actions.py) returned finite `[1,50,32]` actions; hosted-vs-source action relative L2 was **0.742%**, maximum absolute difference **0.0115**, cosine **0.999973**. Repeating the source policy gave bitwise identical actions. This is a cross-device numerical handoff experiment, not a device-local policy or robot-task pass; no real-observation action tolerance is known.

-Next: retrieve and audit the device latent, inspect actual NPU placement with a profile if numerical error is acceptable, then test action-policy impact and a device-local stateful policy. Full multimodal observation processing, admission, cancellation and sustained profiling remain open.
+Next: inspect actual NPU placement with the pending profile, test representative real observations and action tolerances, then build and validate a device-local stateful policy. Full multimodal observation processing, admission, cancellation and sustained profiling remain open.
@@ -0,0 +1,31 @@
+{
+  "scope": "S25 hosted Cosmos latent injected into local CPU InternVLA policy with synthetic state/noise; no device-local policy or robot-task result",
+  "status": "synthetic_action_sensitivity_measured",
+  "policy_checkpoint_sha256": "586799539888c69c35f84d083e559cc5f0ba887801d000e70e65f58d3f85c313",
+  "fixture_sha256": "dcbaf1391ca914d4bbccc1cb384daf05666e771d9cdae90c4f069ec8fcf05368",
+  "hosted_output_sha256": "69647f9fe7207dacc054330c7174d08bdcb4ecf8611f91efc1eb70065418b389",
+  "hosted_inference_job_id": "jp8ex43zp",
+  "policy_torch": "2.13.0+cpu",
+  "policy_load_seconds_not_request_timing": 34.37513727898477,
+  "encoder_calls": {
+    "source": 2,
+    "hosted": 1
+  },
+  "action_shape": [
+    1,
+    50,
+    32
+  ],
+  "source_repeat_bitwise_equal": true,
+  "action_relative_l2": 0.007421938467116418,
+  "action_max_abs": 0.01148313283920288,
+  "action_cosine": 0.9999725460323058,
+  "source_actions_sha256": "d20b4804ce549c25e46590f4057d5d9eceb9e331c7015e3fc90cf79a234bf018",
+  "hosted_actions_sha256": "cd1f1a1b8883c74e36a553b6a6d98631beaec22990b70b5114f1e35599e2ec6d",
+  "limits": [
+    "The policy ran on local CPU; the device only executed the isolated encoder fixture.",
+    "The encoder input was a synthetic pattern and policy state/noise were synthetic zeros.",
+    "No real-observation reference actions, physical units/time, or robot-task tolerance exist.",
+    "No latency, memory admission, cancellation or sustained device power gate was tested."
+  ]
+}
@@ -0,0 +1,55 @@
+{
+  "scope": "one six-frame Cosmos image encoder on synthetic pattern; no complete InternVLA policy",
+  "status": "component_inference_numeric_measured",
+  "device": {
+    "name": "Samsung Galaxy S25",
+    "os": "15",
+    "attributes": [
+      "os:android",
+      "framework:tflite",
+      "framework:onnx",
+      "abi:aarch64-android",
+      "vendor:samsung",
+      "format:phone",
+      "chipset:qualcomm-snapdragon-8-elite-for-galaxy",
+      "chipset:sm8750-ac",
+      "hexagon:v79",
+      "soc-model:69",
+      "htp-supports-fp16:true",
+      "htp-supports-weight-sharing:true",
+      "framework:qnn"
+    ]
+  },
+  "source_model_id": "mn4okj3rq",
+  "target_model_id": "mqkp12rxm",
+  "compile_job_id": "jpxl4d1jp",
+  "inference_job_id": "jp8ex43zp",
+  "input_dataset_id": "d26qo8p57",
+  "requested_run_options": "--compute_unit npu",
+  "files": {
+    "source_onnx": {
+      "bytes": 125112088,
+      "sha256": "656a5263f584851a1457ef6dcf60b995f7c67b056b3c90a4f33c814f7caf9ec9"
+    },
+    "fixture": {
+      "bytes": 2081321,
+      "sha256": "dcbaf1391ca914d4bbccc1cb384daf05666e771d9cdae90c4f069ec8fcf05368"
+    },
+    "device_output": {
+      "bytes": 185871,
+      "sha256": "69647f9fe7207dacc054330c7174d08bdcb4ecf8611f91efc1eb70065418b389"
+    }
+  },
+  "output_dtype": "float32",
+  "source_vs_device": {
+    "relative_l2": 0.0005243597769688565,
+    "snr_db": 65.60741259861898,
+    "max_abs_error": 0.007274627685546875,
+    "cosine": 0.999999865135908
+  },
+  "limits": [
+    "The fixture is synthetic; no real-observation or action-quality tolerance is established.",
+    "Requested NPU is not actual per-node placement without a device profile.",
+    "The action policy, state lifecycle, admission and complete request were not run on this device."
+  ]
+}
@@ -0,0 +1,33 @@
+{
+  "timestamp_utc": "2026-09-23T23:48:37.390932+00:00",
+  "qai_hub_version": "0.55.0",
+  "facility": "Qualcomm AI Hub Workbench",
+  "job_id": "jp8ex43zp",
+  "url": "https://workbench.aihub.qualcomm.com/jobs/jp8ex43zp/",
+  "status": "SUCCESS",
+  "status_message": "",
+  "options": "--compute_unit npu",
+  "hub_version": "aihub-2026.09.11.0",
+  "device": {
+    "name": "Samsung Galaxy S25",
+    "os": "15",
+    "attributes": [
+      "os:android",
+      "framework:tflite",
+      "framework:onnx",
+      "abi:aarch64-android",
+      "vendor:samsung",
+      "format:phone",
+      "chipset:qualcomm-snapdragon-8-elite-for-galaxy",
+      "chipset:sm8750-ac",
+      "hexagon:v79",
+      "soc-model:69",
+      "htp-supports-fp16:true",
+      "htp-supports-weight-sharing:true",
+      "framework:qnn"
+    ]
+  },
+  "model_id": "mqkp12rxm",
+  "input_dataset_id": "d26qo8p57",
+  "outputs": "device_output.npz"
+}
@@ -0,0 +1,13 @@
+{
+  "timestamp_utc": "2026-09-23T23:51:08.958776+00:00",
+  "facility": "Qualcomm AI Hub Workbench",
+  "qai_hub_version": "0.55.0",
+  "source_compile_job_id": "jpxl4d1jp",
+  "target_model_id": "mqkp12rxm",
+  "device": "Samsung Galaxy S25",
+  "options": "--compute_unit npu",
+  "kind": "profile",
+  "status": "SUBMITTED",
+  "job_id": "jp1njvm8g",
+  "url": "https://workbench.aihub.qualcomm.com/jobs/jp1njvm8g/"
+}
@@ -0,0 +1,11 @@
+# MiniCPM-o 4.5 speech-head decode step across Qualcomm devices
+
+This is a **single fixed-cache 20-layer speech-head step**, not the complete thinker, audio/vision encoders, vocoder or streaming MiniCPM-o pipeline. The retained Workbench source ONNX model `mn4gjjpvn` was recovered outside Git as `/home/zhout/project/edge_infer/models/minicpmo45_tts_step_L256_20260924.onnx` (776,341,465 bytes, SHA256 `78cf64804f11ad269ee4180da61588ccc5eefe2942773227345499756e4cc229`). It has FP32 hidden input `[1,1,768]`, rotary/mask inputs, 20 K/V cache pairs at length 256, and 6,562 logits plus 40 new-cache outputs. The historical export does not attest its exact checkpoint revision.
+
+The [S25 historical inference](s25/inference_report.json) used dataset `d7gwxe3y2`; the [S24 historical inference](s24/inference_report.json) used `d7zn8rk57`. Downloaded inputs from both dataset IDs produced the same 44-array `.npz` SHA256 `65919ea1f351feec33c1415a3e3db76d60bc9c2beb3d49a72af75dcb418e83b4`. The retained fixture is outside Git under `/home/zhout/project/edge_infer/models/minicpmo45_tts_step_fixture_s25_20260924.npz`. ONNX Runtime 1.29 CPU produced a 41-output reference (SHA256 `902adcceadbe761d938ef2ade51cf953c8917ea1abfaee33a10c84e300c1bd96`), also outside Git.
+
+The historical [S24](s24/compile_report.json) and [S25](s25/compile_report.json) FP16 QNN context-binary compiles and inference jobs succeeded. Against the **same original ONNX CPU fixture**, [S24](s24/audit_report.json) logits relative L2 was **0.3459%** and max K/V relative L2 **1.235%**; [S25](s25/audit_report.json) logits relative L2 was **0.3605%** and max K/V relative L2 **1.311%**. Both preserved the CPU top-1 speech token **1867** on this one synthetic step. The raw [S24](s24/device_output.npz) and [S25](s25/device_output.npz) arrays are retained. These values are numerical component evidence, without a generated-audio quality threshold or actual NPU placement profile.
+
+New FP16 QNN DLC [SA8775P](sa8775p/compile_report.json), [Snapdragon X Elite](xelite/compile_report.json) and [RB3 Gen 2](rb3/compile_report.json) compiles passed from the same source. Same-fixture NPU-requested inference jobs were [submitted for SA8775P](sa8775p/inference_submission.json), [X Elite](xelite/inference_submission.json) and [RB3](rb3/inference_submission.json), but were pending at this snapshot. A compile cannot establish device execution or numerical parity. The [collector](../../../../experiments/collect_aihub_component.py) retrieves terminal jobs, and the [auditor](../../../../experiments/audit_minicpmo_tts_qualcomm.py) pins hashes and checks all 41 output tensors.
+
+Next: audit those device outputs and placement, then qualify the entire model with real multimodal inputs, stateful speech generation, task/audio quality, memory admission, cancellation and sustained power/thermal behavior. AI Hub is the test facility, not a deployment dependency.
@@ -0,0 +1,31 @@
+{
+  "retrieved_utc": "2026-09-23T23:42:08.019012+00:00",
+  "facility": "Qualcomm AI Hub Workbench",
+  "qai_hub_version": "0.55.0",
+  "job_id": "j5wl71l6p",
+  "url": "https://workbench.aihub.qualcomm.com/jobs/j5wl71l6p/",
+  "status": "SUCCESS",
+  "status_message": "",
+  "source_model_id": "mn4gjjpvn",
+  "target_model_id": "mnwv5rj3q",
+  "device": {
+    "name": "Dragonwing RB3 Gen 2 Vision Kit",
+    "os": "1.6",
+    "attributes": [
+      "os:qc_linux",
+      "framework:tflite",
+      "framework:onnx",
+      "vendor:qualcomm",
+      "abi:aarch64-oe-linux-gcc11",
+      "format:iot",
+      "chipset:qualcomm-qcs6490",
+      "chipset:qcs6490",
+      "chipset:qualcomm-dragonwing-qcs6490",
+      "hexagon:v68",
+      "soc-model:93",
+      "framework:qnn"
+    ]
+  },
+  "options": "--target_runtime qnn_dlc --qnn_options default_graph_htp_precision=FLOAT16",
+  "hub_version": "aihub-2026.09.11.0"
+}
@@ -0,0 +1,13 @@
+{
+  "timestamp_utc": "2026-09-23T23:38:27.062001+00:00",
+  "facility": "Qualcomm AI Hub Workbench",
+  "qai_hub_version": "0.55.0",
+  "source_model_id": "mn4gjjpvn",
+  "source_sha256": "78cf64804f11ad269ee4180da61588ccc5eefe2942773227345499756e4cc229",
+  "device": "Dragonwing RB3 Gen 2 Vision Kit",
+  "options": "--target_runtime qnn_dlc --qnn_options default_graph_htp_precision=FLOAT16",
+  "kind": "compile",
+  "status": "SUBMITTED",
+  "job_id": "j5wl71l6p",
+  "url": "https://workbench.aihub.qualcomm.com/jobs/j5wl71l6p/"
+}
@@ -0,0 +1,14 @@
+{
+  "timestamp_utc": "2026-09-23T23:42:09.154844+00:00",
+  "facility": "Qualcomm AI Hub Workbench",
+  "qai_hub_version": "0.55.0",
+  "source_compile_job_id": "j5wl71l6p",
+  "target_model_id": "mnwv5rj3q",
+  "input_dataset_id": "d7gwxe3y2",
+  "device": "Dragonwing RB3 Gen 2 Vision Kit",
+  "options": "--compute_unit npu",
+  "kind": "inference",
+  "status": "SUBMITTED",
+  "job_id": "jpvld92j5",
+  "url": "https://workbench.aihub.qualcomm.com/jobs/jpvld92j5/"
+}
@@ -0,0 +1,305 @@
+{
+  "scope": "one fixed-cache 20-layer MiniCPM-o speech-head decode step; no complete multimodal stream",
+  "status": "component_inference_numeric_measured",
+  "device": {
+    "name": "Samsung Galaxy S24",
+    "os": "14",
+    "attributes": [
+      "os:android",
+      "framework:tflite",
+      "framework:onnx",
+      "abi:aarch64-android",
+      "vendor:samsung",
+      "format:phone",
+      "chipset:qualcomm-snapdragon-8gen3",
+      "chipset:sm8650",
+      "hexagon:v75",
+      "soc-model:57",
+      "htp-supports-fp16:true",
+      "htp-supports-weight-sharing:true",
+      "framework:qnn"
+    ]
+  },
+  "compile_job_id": "jp2ryv3qg",
+  "inference_job_id": "jp0m0646g",
+  "source_model_id": "mn4gjjpvn",
+  "target_model_id": "mqp411wgq",
+  "input_dataset_id": "d7zn8rk57",
+  "requested_run_options": "",
+  "files": {
+    "source_onnx": {
+      "bytes": 776341465,
+      "sha256": "78cf64804f11ad269ee4180da61588ccc5eefe2942773227345499756e4cc229"
+    },
+    "fixture": {
+      "bytes": 29247541,
+      "sha256": "65919ea1f351feec33c1415a3e3db76d60bc9c2beb3d49a72af75dcb418e83b4"
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+    "cpu_reference": {
+      "bytes": 148471,
+      "sha256": "902adcceadbe761d938ef2ade51cf953c8917ea1abfaee33a10c84e300c1bd96"
+    },
+    "device_output": {
+      "bytes": 105697,
+      "sha256": "e4e9818b0eb2edb48d569f64de5fe80a79b9b49a89ab6983425d1b33cf89f3bb"
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+  "tensor_comparison": {
+    "logits": {
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+      "device_top1": 1867,
+      "top1_matches": true
+    },
+    "k_new_0": {
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+      "max_abs_error": 0.003048419952392578,
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+      "device_rms": 1.3070441579437349
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+    "v_new_0": {
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+    "attributes": [
+      "os:android",
+      "framework:tflite",
+      "framework:onnx",
+      "abi:aarch64-android",
+      "vendor:samsung",
+      "format:phone",
+      "chipset:qualcomm-snapdragon-8-elite-for-galaxy",
+      "chipset:sm8750-ac",
+      "hexagon:v79",
+      "soc-model:69",
+      "htp-supports-fp16:true",
+      "htp-supports-weight-sharing:true",
+      "framework:qnn"
+    ]
+  },
+  "model_id": "mn7zvvw8m",
+  "input_dataset_id": "d7gwxe3y2",
+  "outputs": "device_output.npz"
+}
@@ -0,0 +1,14 @@
+{
+  "timestamp_utc": "2026-09-23T23:37:18.432489+00:00",
+  "facility": "Qualcomm AI Hub Workbench",
+  "qai_hub_version": "0.55.0",
+  "source_compile_job_id": "jp4yr9j1p",
+  "target_model_id": "mn7zvvw8m",
+  "input_dataset_id": "d7gwxe3y2",
+  "device": "Samsung Galaxy S25",
+  "options": "--compute_unit npu",
+  "kind": "inference",
+  "status": "HISTORICAL",
+  "job_id": "jpe7l8r75",
+  "url": "https://workbench.aihub.qualcomm.com/jobs/jpe7l8r75/"
+}
@@ -0,0 +1,31 @@
+{
+  "retrieved_utc": "2026-09-23T23:42:03.580022+00:00",
+  "facility": "Qualcomm AI Hub Workbench",
+  "qai_hub_version": "0.55.0",
+  "job_id": "jpe7zlz15",
+  "url": "https://workbench.aihub.qualcomm.com/jobs/jpe7zlz15/",
+  "status": "SUCCESS",
+  "status_message": "",
+  "source_model_id": "mn4gjjpvn",
+  "target_model_id": "mn1lvg1zq",
+  "device": {
+    "name": "SA8775P ADP",
+    "os": "14",
+    "attributes": [
+      "os:android",
+      "framework:tflite",
+      "framework:onnx",
+      "vendor:qualcomm",
+      "abi:aarch64-android",
+      "format:auto",
+      "chipset:qualcomm-sa8775p",
+      "hexagon:v73",
+      "soc-model:52",
+      "htp-supports-fp16:true",
+      "htp-supports-weight-sharing:true",
+      "framework:qnn"
+    ]
+  },
+  "options": "--target_runtime qnn_dlc --qnn_options default_graph_htp_precision=FLOAT16",
+  "hub_version": "aihub-2026.09.11.0"
+}
@@ -0,0 +1,13 @@
+{
+  "timestamp_utc": "2026-09-23T23:38:22.603955+00:00",
+  "facility": "Qualcomm AI Hub Workbench",
+  "qai_hub_version": "0.55.0",
+  "source_model_id": "mn4gjjpvn",
+  "source_sha256": "78cf64804f11ad269ee4180da61588ccc5eefe2942773227345499756e4cc229",
+  "device": "SA8775P ADP",
+  "options": "--target_runtime qnn_dlc --qnn_options default_graph_htp_precision=FLOAT16",
+  "kind": "compile",
+  "status": "SUBMITTED",
+  "job_id": "jpe7zlz15",
+  "url": "https://workbench.aihub.qualcomm.com/jobs/jpe7zlz15/"
+}
@@ -0,0 +1,14 @@
+{
+  "timestamp_utc": "2026-09-23T23:42:04.724186+00:00",
+  "facility": "Qualcomm AI Hub Workbench",
+  "qai_hub_version": "0.55.0",
+  "source_compile_job_id": "jpe7zlz15",
+  "target_model_id": "mn1lvg1zq",
+  "input_dataset_id": "d7gwxe3y2",
+  "device": "SA8775P ADP",
+  "options": "--compute_unit npu",
+  "kind": "inference",
+  "status": "SUBMITTED",
+  "job_id": "jp3z9wol5",
+  "url": "https://workbench.aihub.qualcomm.com/jobs/jp3z9wol5/"
+}
@@ -0,0 +1,31 @@
+{
+  "retrieved_utc": "2026-09-23T23:42:05.821498+00:00",
+  "facility": "Qualcomm AI Hub Workbench",
+  "qai_hub_version": "0.55.0",
+  "job_id": "jgzlm4mk5",
+  "url": "https://workbench.aihub.qualcomm.com/jobs/jgzlm4mk5/",
+  "status": "SUCCESS",
+  "status_message": "",
+  "source_model_id": "mn4gjjpvn",
+  "target_model_id": "mmd04pj3n",
+  "device": {
+    "name": "Snapdragon X Elite CRD",
+    "os": "11",
+    "attributes": [
+      "os:windows",
+      "format:compute",
+      "framework:onnx",
+      "vendor:qualcomm",
+      "abi:aarch64-windows",
+      "chipset:qualcomm-snapdragon-x-elite",
+      "chipset:sc8380xp",
+      "hexagon:v73",
+      "soc-model:60",
+      "htp-supports-fp16:true",
+      "htp-supports-weight-sharing:true",
+      "framework:qnn"
+    ]
+  },
+  "options": "--target_runtime qnn_dlc --qnn_options default_graph_htp_precision=FLOAT16",
+  "hub_version": "aihub-2026.09.11.0"
+}
@@ -0,0 +1,13 @@
+{
+  "timestamp_utc": "2026-09-23T23:38:24.348058+00:00",
+  "facility": "Qualcomm AI Hub Workbench",
+  "qai_hub_version": "0.55.0",
+  "source_model_id": "mn4gjjpvn",
+  "source_sha256": "78cf64804f11ad269ee4180da61588ccc5eefe2942773227345499756e4cc229",
+  "device": "Snapdragon X Elite CRD",
+  "options": "--target_runtime qnn_dlc --qnn_options default_graph_htp_precision=FLOAT16",
+  "kind": "compile",
+  "status": "SUBMITTED",
+  "job_id": "jgzlm4mk5",
+  "url": "https://workbench.aihub.qualcomm.com/jobs/jgzlm4mk5/"
+}
@@ -0,0 +1,14 @@
+{
+  "timestamp_utc": "2026-09-23T23:42:06.955300+00:00",
+  "facility": "Qualcomm AI Hub Workbench",
+  "qai_hub_version": "0.55.0",
+  "source_compile_job_id": "jgzlm4mk5",
+  "target_model_id": "mmd04pj3n",
+  "input_dataset_id": "d7gwxe3y2",
+  "device": "Snapdragon X Elite CRD",
+  "options": "--compute_unit npu",
+  "kind": "inference",
+  "status": "SUBMITTED",
+  "job_id": "jgolr4dxg",
+  "url": "https://workbench.aihub.qualcomm.com/jobs/jgolr4dxg/"
+}
@@ -4,8 +4,10 @@ This is **one fixed-cache-bucket talker decode step**, not complete Qwen3-TTS ge

 The deterministic synthetic input dataset `d7dw6pgy2` has SHA256 `0bf538e1b64dfc9737dc4f925d61c6e4ffa5075a5dcd7afb1e85ac3bd2d9afea`. A local ONNX Runtime 1.29 CPU reference output has SHA256 `ee0ead70e33c02c7d3d6bcb757c5cf0fcbb9eb6fc75c34abfbe8a4479657d5be`. The fixture exercises the stated tensor shapes, but does not represent a real text-to-speech request or establish token/audio quality.

-Historical FP16 QNN context-binary [S24](s24/compile_report.json), [S25](s25/compile_report.json), [SA8775P](sa8775p/compile_report.json), and [Snapdragon X Elite](xelite/compile_report.json) compile jobs succeeded. Their same-fixture NPU inference jobs were [submitted](s24/inference_submission.json), [submitted](s25/inference_submission.json), [submitted](sa8775p/inference_submission.json), and [submitted](xelite/inference_submission.json), respectively; none had a terminal result at this snapshot. The [RB3 compile](rb3/compile_report.json) rejected the floating-point input on this device. These are artifact-specific compile dispositions, not full-model support claims.
+Historical FP16 QNN context-binary [S24](s24/compile_report.json), [S25](s25/compile_report.json), [SA8775P](sa8775p/compile_report.json), and [Snapdragon X Elite](xelite/compile_report.json) compile jobs succeeded. Same-fixture NPU-requested inference succeeded on all four, but **all changed the CPU reference top-1 logit** on this synthetic step: CPU token 80 versus S24/SA/X Elite 1720 and S25 412. The [S24](s24/audit_report.json), [S25](s25/audit_report.json), [SA8775P](sa8775p/audit_report.json) and [X Elite](xelite/audit_report.json) audits report logits relative L2 **9.33%/8.99%/9.43%/8.57%**, respectively, with maximum K/V relative L2 **11.23%/11.71%/11.54%/11.50%**. These FP16 artifacts are numerically unqualified for this fixture; a successful graph execution is not speech-token parity. The [RB3 compile](rb3/compile_report.json) rejected floating-point input on this device. These are artifact-specific outcomes, not full-model support claims.

-The [collector](../../../../experiments/collect_aihub_component.py) retrieves terminal inference outputs without resubmission. The [tensor auditor](../../../../experiments/audit_qwen_tts_talker_qualcomm.py) enforces the source, fixture, compile target, exact device, output shape and finiteness contracts, and reports logits top-1 plus relative error for all 56 new K/V tensors. It does not convert those metrics to a quality pass without an explicit token/audio tolerance. Its 57-output contract was exercised against the pinned CPU reference as a local self-check; that self-check is not device evidence.
+Explicit [X Elite ONNX CPU](xelite/cpu_fallback/compile_submission.json) and [SA8775P TFLite CPU](sa8775p/cpu_fallback/compile_submission.json) fallback compiles were submitted from the same source to test separate numerical routes. Their results were pending at this snapshot; neither is an NPU pass or a full TTS deployment.

-Next: retrieve terminal inference tensors, compare logits and every K/V output against the pinned CPU reference with an explicit tolerance, inspect actual placement, then profile a quality-passing decode step. Full device-local talker/predictor/vocoder streaming and power/memory qualification remain separate gates.
+The [collector](../../../../experiments/collect_aihub_component.py) retrieves terminal inference outputs without resubmission. The [tensor auditor](../../../../experiments/audit_qwen_tts_talker_qualcomm.py) enforces the source, fixture, compile target, exact device, output shape and finiteness contracts, and reports logits top-1 plus relative error for all 56 new K/V tensors. It does not convert those metrics to a quality pass without an explicit token/audio tolerance. Its 57-output contract was exercised against the pinned CPU reference as a local self-check; that self-check is not device evidence. Requested NPU placement still needs a profile.
+
+Next: investigate FP16 numerical drift using realistic generated-cache fixtures or a higher-precision/mixed-precision artifact, and inspect placement only for a quality-passing decode step. Full device-local talker/predictor/vocoder streaming and power/memory qualification remain separate gates.
@@ -0,0 +1,401 @@
+{
+  "scope": "one synthetic fixed-cache 28-layer Qwen3-TTS talker decode step; no complete TTS stream",
+  "status": "component_executed_numeric_measured",
+  "device": {
+    "name": "Samsung Galaxy S24",
+    "os": "14",
+    "attributes": [
+      "os:android",
+      "framework:tflite",
+      "framework:onnx",
+      "abi:aarch64-android",
+      "vendor:samsung",
+      "format:phone",
+      "chipset:qualcomm-snapdragon-8gen3",
+      "chipset:sm8650",
+      "hexagon:v75",
+      "soc-model:57",
+      "htp-supports-fp16:true",
+      "htp-supports-weight-sharing:true",
+      "framework:qnn"
+    ]
+  },
+  "compile_job_id": "jpvdx28zp",
+  "inference_job_id": "j5687dyng",
+  "source_model_id": "mq3xre4rq",
+  "target_model_id": "mnowr3epq",
+  "input_dataset_id": "d7dw6pgy2",
+  "requested_run_options": "--compute_unit npu",
+  "files": {
+    "source_onnx": {
+      "bytes": 1776377476,
+      "sha256": "41feb08517678e94d9301e992d0df610fc60e7c9faba5600703298c86554fd22"
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+    "The historical source export does not attest an exact checkpoint revision.",
+    "The fixture is synthetic and has no token-to-audio quality tolerance.",
+    "Requested NPU is not actual placement without a device profile.",
+    "No predictor, vocoder, persistent cache loop or complete TTS stream was tested."
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+}
@@ -0,0 +1,31 @@
+{
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+  "facility": "Qualcomm AI Hub Workbench",
+  "qai_hub_version": "0.55.0",
+  "job_id": "jp1njv18g",
+  "url": "https://workbench.aihub.qualcomm.com/jobs/jp1njv18g/",
+  "status": "SUCCESS",
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+  "source_model_id": "mq3xre4rq",
+  "target_model_id": "mnwv5rp3q",
+  "device": {
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+      "format:compute",
+      "framework:onnx",
+      "vendor:qualcomm",
+      "abi:aarch64-windows",
+      "chipset:qualcomm-snapdragon-x-elite",
+      "chipset:sc8380xp",
+      "hexagon:v73",
+      "soc-model:60",
+      "htp-supports-fp16:true",
+      "htp-supports-weight-sharing:true",
+      "framework:qnn"
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+  "options": "--target_runtime onnx",
+  "hub_version": "aihub-2026.09.11.0"
+}
@@ -0,0 +1,12 @@
+{
+  "timestamp_utc": "2026-09-23T23:45:36.274399+00:00",
+  "facility": "Qualcomm AI Hub Workbench",
+  "qai_hub_version": "0.55.0",
+  "source_model_id": "mq3xre4rq",
+  "device": "Snapdragon X Elite CRD",
+  "options": "--target_runtime onnx",
+  "kind": "compile",
+  "status": "SUBMITTED",
+  "job_id": "jp1njv18g",
+  "url": "https://workbench.aihub.qualcomm.com/jobs/jp1njv18g/"
+}
@@ -0,0 +1,14 @@
+{
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+  "facility": "Qualcomm AI Hub Workbench",
+  "qai_hub_version": "0.55.0",
+  "source_compile_job_id": "jp1njv18g",
+  "target_model_id": "mnwv5rp3q",
+  "input_dataset_id": "d7dw6pgy2",
+  "device": "Snapdragon X Elite CRD",
+  "options": "--compute_unit cpu",
+  "kind": "inference",
+  "status": "SUBMITTED",
+  "job_id": "jgzlm40o5",
+  "url": "https://workbench.aihub.qualcomm.com/jobs/jgzlm40o5/"
+}
@@ -0,0 +1,32 @@
+{
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+  "qai_hub_version": "0.55.0",
+  "facility": "Qualcomm AI Hub Workbench",
+  "job_id": "j5qlym6ep",
+  "url": "https://workbench.aihub.qualcomm.com/jobs/j5qlym6ep/",
+  "status": "SUCCESS",
+  "status_message": "",
+  "options": "--compute_unit npu",
+  "hub_version": "aihub-2026.09.11.0",
+  "device": {
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+    "os": "11",
+    "attributes": [
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+      "format:compute",
+      "framework:onnx",
+      "vendor:qualcomm",
+      "abi:aarch64-windows",
+      "chipset:qualcomm-snapdragon-x-elite",
+      "chipset:sc8380xp",
+      "hexagon:v73",
+      "soc-model:60",
+      "htp-supports-fp16:true",
+      "htp-supports-weight-sharing:true",
+      "framework:qnn"
+    ]
+  },
+  "model_id": "mqv3y1g0m",
+  "input_dataset_id": "d7dw6pgy2",
+  "outputs": "device_output.npz"
+}
@@ -10,12 +10,12 @@ The fresh [RB3 inference](rb3/inference_report.json) on the same fixture returne

 A separate [local LiteRT 2.2.0 CPU control](local_litert_cpu/report.json) ran that **same pinned TFLite binary** on the WSL x86-64 HX370 host with four interpreter threads. Its [waveform](local_litert_cpu/device_output.npz) was finite and unsaturated, within **3.45e-6 relative L2 / 109.23 dB SNR** of the original ONNX Runtime CPU output. The [reproducer](../../../../experiments/probe_qwen_tts_vocoder_litert_cpu.py) checks binary/fixture/reference hashes and I/O contracts. This narrows the SA saturation to hosted runtime/device behavior rather than a universally bad TFLite conversion. The one local invoke is not a comparable performance profile; it does not prove either embedded device's CPU route or identify the specific delegate operation at fault.

-On the **exact hosted SA8775P**, a [CPU-requested inference](sa8775p/cpu_control/inference_report.json) of that binary also returned a finite unsaturated waveform. The [same-fixture comparison](sa8775p/cpu_control/inference_comparison.json) was **4.13e-6 relative L2 / 107.67 dB SNR** against original ONNX CPU. The GPU-requested SA output differed from this SA CPU-requested output by **11.832 relative L2** and saturated 96.63% of samples. This is strong evidence that the tested GPU-requested route causes the waveform failure on SA, while its exact delegate operation and all CPU placement details still require the [CPU profile](sa8775p/cpu_control/profile_submission.json) and FP32-preserving GPU control.
+On the **exact hosted SA8775P**, a [CPU-requested inference](sa8775p/cpu_control/inference_report.json) of that binary also returned a finite unsaturated waveform. The [same-fixture comparison](sa8775p/cpu_control/inference_comparison.json) was **4.13e-6 relative L2 / 107.67 dB SNR** against original ONNX CPU. The GPU-requested SA output differed from this SA CPU-requested output by **11.832 relative L2** and saturated 96.63% of samples. A fresh [FP32-preserving GPU-option inference](sa8775p/gpu_fp32/inference_report.json) also failed numerically: its [comparison](sa8775p/gpu_fp32/inference_comparison.json) had **11.389 relative L2 / −21.13 dB SNR** versus original ONNX CPU and **84.89%** saturated samples. Disabling the FP32-to-FP16 allowance does not rescue this tested GPU route. The exact faulty delegate operation and all CPU placement details still require further isolation and the [CPU profile](sa8775p/cpu_control/profile_submission.json).

 The re-fetched *historical* [SA8775P profile](sa8775p/historical_profile_report.json) has 100 component samples, nearest-rank p50/p95 **1.751/1.874 s**, and 403 CPU plus 335 GPU execution-detail rows. The *historical* [RB3 profile](rb3/historical_profile_report.json) has 100 samples, p50/p95 **3.143/3.189 s**, also with 403 CPU plus 335 GPU rows. Their previously quoted 1.701/3.107 s estimated-time fields are sample minima, not medians. Both profiles exclude talker/predictor, stage handoff, streaming and full-request time.

 The X Elite [ONNX CPU inference](xelite/cpu/inference_report.json) succeeded on the pinned fixture. Its [waveform comparison](xelite/cpu/inference_comparison.json) to the original local ONNX Runtime CPU output was **8.09e-7 relative L2 / 121.84 dB SNR**, with no saturation. The fresh [CPU profile](xelite/cpu/profile_report.json) returned **94** component samples, nearest-rank p50/p95 **6.384/6.644 s**, with all **634** execution-detail rows on CPU; the service's 5.917 s estimated-time field is the sample minimum. The [full audit](xelite/cpu/audit_report.json) pins source, fixture, target, waveform, placement and percentiles. This is near-source component parity on one fixture, without a listening-quality or complete-stream gate. The first X Elite [GPU inference](xelite/gpu/inference_report.json) and [profile](xelite/gpu/profile_report.json) jobs failed because Workbench rejects specifying both compute unit and ONNX execution provider. The corrected [DirectML inference](xelite/directml/inference_report.json) and [profile](xelite/directml/profile_report.json) both failed during DML provider initialization with Windows error `80070057` (`The parameter is incorrect`), without an output or timing samples. A separate [FP16 QNN DLC compile](xelite/qnn_npu/compile_report.json) succeeded and [NPU-requested same-fixture inference](xelite/qnn_npu/inference_submission.json) was pending. The [X Elite ONNX CPU compile](xelite/compile_report.json) passed after the older TFLite target was rejected on Windows ARM. These failures constrain the tested DirectML route, not all GPU or NPU artifacts.

-The [SA CPU profile](sa8775p/cpu_control/profile_submission.json), [FP32-preserving GPU](sa8775p/gpu_fp32/inference_submission.json) control, and X Elite [QNN NPU inference](xelite/qnn_npu/inference_submission.json) remained pending in this snapshot. Pending jobs have no numerical or latency conclusion. The [collector](../../../../experiments/collect_aihub_component.py) retrieves their terminal reports without resubmitting jobs, and the [audit script](../../../../experiments/audit_qwen_tts_vocoder_qualcomm.py) checks source/fixture hashes, device and model IDs, actual placement, waveform error, saturation and sample percentiles.
+The [SA CPU profile](sa8775p/cpu_control/profile_submission.json) and X Elite [QNN NPU inference](xelite/qnn_npu/inference_submission.json) remained pending in this snapshot. Pending jobs have no numerical or latency conclusion. The [collector](../../../../experiments/collect_aihub_component.py) retrieves their terminal reports without resubmitting jobs, and the [audit script](../../../../experiments/audit_qwen_tts_vocoder_qualcomm.py) checks source/fixture hashes, device and model IDs, actual placement, waveform error, saturation and sample percentiles.

 This is one fixed vocoder component with a retained input, not complete Qwen3-TTS. The next gates are a quality-passing SA/RB3/X Elite target, talker and code predictor numerical checks, device-resident handoff/co-residency, playable streaming, cancellation, memory admission and sustained power/thermal profiling.
@@ -0,0 +1,51 @@
+{
+  "scope": "one Qwen3-TTS Code2Wav inference with FP32-preserving GPU option on SA8775P; no complete TTS stream",
+  "status": "component_gpu_requested_numeric_fail",
+  "compile_job_id": "jp410o3vp",
+  "inference_job_id": "jgdd38lzg",
+  "target_model_id": "mnze95don",
+  "input_dataset_id": "d7m80jl32",
+  "requested_options": "--compute_unit gpu --tflite_options allow_fp32_as_fp16=false",
+  "device": {
+    "name": "SA8775P ADP",
+    "os": "14",
+    "attributes": [
+      "os:android",
+      "framework:tflite",
+      "framework:onnx",
+      "vendor:qualcomm",
+      "abi:aarch64-android",
+      "format:auto",
+      "chipset:qualcomm-sa8775p",
+      "hexagon:v73",
+      "soc-model:52",
+      "htp-supports-fp16:true",
+      "htp-supports-weight-sharing:true",
+      "framework:qnn"
+    ]
+  },
+  "output_sha256": "9196d254564a1286f56da5d415f69cb0b85d6dbbc5550f6d31b818ab2eab5b42",
+  "source_onnx_cpu_vs_sa_gpu_fp32": {
+    "relative_l2": 11.389093101414934,
+    "snr_db": -21.129782863088515,
+    "max_abs": 1.4882186353206635,
+    "cosine": -0.018527488603516624,
+    "reference_rms": 0.08373439205485074,
+    "output_rms": 0.9484254750023428,
+    "output_saturated_fraction_abs_ge_0_999": 0.8488958333333333
+  },
+  "sa_cpu_requested_vs_sa_gpu_fp32": {
+    "relative_l2": 11.389092347977442,
+    "snr_db": -21.12978228847954,
+    "max_abs": 1.4882185459136963,
+    "cosine": -0.01852748991918297,
+    "reference_rms": 0.08373439765610562,
+    "output_rms": 0.9484254750023428,
+    "output_saturated_fraction_abs_ge_0_999": 0.8488958333333333
+  },
+  "limits": [
+    "No actual per-node placement profile for this control.",
+    "The output is severely saturated, so component timing would not be a usable TTS measure.",
+    "The option result does not identify a specific faulty GPU operation."
+  ]
+}
@@ -0,0 +1,32 @@
+{
+  "timestamp_utc": "2026-09-23T23:38:58.225134+00:00",
+  "qai_hub_version": "0.55.0",
+  "facility": "Qualcomm AI Hub Workbench",
+  "job_id": "jgdd38lzg",
+  "url": "https://workbench.aihub.qualcomm.com/jobs/jgdd38lzg/",
+  "status": "SUCCESS",
+  "status_message": "",
+  "options": "--compute_unit gpu --tflite_options allow_fp32_as_fp16=false",
+  "hub_version": "aihub-2026.09.11.0",
+  "device": {
+    "name": "SA8775P ADP",
+    "os": "14",
+    "attributes": [
+      "os:android",
+      "framework:tflite",
+      "framework:onnx",
+      "vendor:qualcomm",
+      "abi:aarch64-android",
+      "format:auto",
+      "chipset:qualcomm-sa8775p",
+      "hexagon:v73",
+      "soc-model:52",
+      "htp-supports-fp16:true",
+      "htp-supports-weight-sharing:true",
+      "framework:qnn"
+    ]
+  },
+  "model_id": "mnze95don",
+  "input_dataset_id": "d7m80jl32",
+  "outputs": "device_output.npz"
+}
@@ -16,9 +16,11 @@ A [Qwen3-TTS Code2Wav follow-up](../../../e2e_expansion_20260924/evidence/qwen_t

 The identical SA/RB3 TFLite binary matched original ONNX CPU within 3.45e-6 relative L2 in a separate local HX370 x86-64 LiteRT CPU control. This narrows the SA waveform saturation to hosted device/runtime behavior, without proving either embedded CPU route or a specific GPU-delegate fault.

-A separate [Qwen3-TTS talker decode-step attempt](../../../e2e_expansion_20260924/evidence/qwen_tts_talker_qualcomm/README.md) recovered a 28-layer ONNX export and synthetic fixed-cache fixture. Historical FP16 QNN context-binary compiles passed for S24, S25, SA8775P and X Elite, but the same-fixture inference jobs were pending. An RB3 compile rejected floating-point input. No talker runtime or numerical pass is implied by those compile outcomes.
+A separate [Qwen3-TTS talker decode-step attempt](../../../e2e_expansion_20260924/evidence/qwen_tts_talker_qualcomm/README.md) recovered a 28-layer ONNX export and synthetic fixed-cache fixture. Historical FP16 QNN context-binary inference executed on S24, S25, SA8775P and X Elite, but all four changed the CPU reference top-1 speech token with 8.57–9.43% logits relative L2; they are numerically unqualified on this fixture. An RB3 compile rejected floating-point input. No complete talker loop or TTS stream follows.

-A [Galaxy S25 InternVLA Cosmos attempt](../../../e2e_expansion_20260924/evidence/internvla_cosmos_qualcomm/README.md) packaged the real Place_Markpen image encoder into one ONNX file after two CPU parity fixtures. FLOAT16 QNN DLC compilation passed, while same-fixture NPU-requested inference was pending. This changes the artifact investigation for that cell, not its NPU or full-policy qualification.
+A [Galaxy S25 InternVLA Cosmos attempt](../../../e2e_expansion_20260924/evidence/internvla_cosmos_qualcomm/README.md) packaged the real Place_Markpen image encoder into one ONNX file after two CPU parity fixtures. FLOAT16 QNN DLC inference returned a latent within 0.0524% relative L2 of the source on one pattern; device placement profile was pending. Injecting that latent into the real policy on local CPU changed synthetic actions by 0.742% relative L2, without a task tolerance. This advances component and action-sensitivity evidence, not device-local full-policy qualification.
+
+A [MiniCPM-o speech-head study](../../../e2e_expansion_20260924/evidence/minicpmo_tts_qualcomm/README.md) recovered the historical 20-layer source and matching S24/S25 fixtures. Both FP16 context-binary outputs preserved the ONNX CPU top-1 speech token on this one decode step with logits relative L2 of 0.346%/0.360%. New FP16 QNN DLC compiles passed for SA8775P, X Elite and RB3; same-fixture NPU-requested inference was pending. These are speech-head component results, not multimodal MiniCPM-o requests.

 ## Configuration × model

@@ -85,29 +87,29 @@ NOT E2E means no complete device/model workload was verified. “Scoped E2E” i
 | PC CPU+iGPU+NPU +/- discrete GPU, joint model execution | MiniCPM-o 4.5 | NOT_E2E_PROFILEABLE | No qualifying same-model multi-accelerator deployment/artifact plan or overlap/recovery evidence. Individual routes are not a joint pipeline. | Supply and validate the missing matching artifact/backend, then run the complete specified workload. |
 | PC CPU+iGPU+NPU +/- discrete GPU, joint model execution | InternVLA-A1 | NOT_E2E_PROFILEABLE | No qualifying same-model multi-accelerator deployment/artifact plan or overlap/recovery evidence. Individual routes are not a joint pipeline. | Supply and validate the missing matching artifact/backend, then run the complete specified workload. |
 | PC Snapdragon X Elite CRD / AI Hub | Spark-X2.5 | COMPONENT_NPU_INFERENCE_PROFILED; NOT_E2E_PROFILEABLE | A real Spark-X2.5-1.7B one-layer W8A16 QDQ attention artifact, compiled as QNN DLC, ran on the exact hosted Snapdragon X Elite CRD (Windows 11 ARM64). Inference returned finite hidden/new-K/new-V tensors bitwise identical to the prior S24/S25 NPU outputs on the same fixture; the profile attributed all 73 execution-detail rows to NPU. Across 100 component samples, nearest-rank p50/p95 was 1.433/1.606 ms; the 1.346 ms estimated-time field is the minimum. Hidden/K/V relative L2 versus original FP32 ONNX CPU was 1.351%/0.377%/0.650%, without a next-token quality tolerance. There is no complete 28-layer prefill/decode/KV-ring/sampling loop, resident Omni controller or complete-request timing. | Qualify a complete X Elite resident artifact/controller with state, token quality, memory admission/cancel and sustained power; [X Elite NPU raw evidence](../../../e2e_expansion_20260923/evidence/spark_xelite_full_attention/README.md). |
-| PC Snapdragon X Elite CRD / AI Hub | Qwen3-TTS 0.6B CustomVoice | COMPONENT_ONNX_CPU_VOCODER_PARITY_PROFILED; DIRECTML_RUNTIME_FAILED; QNN_NPU_COMPILE_PASS; NOT_E2E_PROFILEABLE | The fresh ONNX vocoder inference on the exact Windows 11 ARM64 X Elite CRD requested CPU and returned an unsaturated waveform on the pinned fixture, matching local ONNX Runtime CPU with 8.09e-7 relative L2 and 121.84 dB SNR. Its 94-sample component profile had nearest-rank p50/p95 6.384/6.644 s and all 634 execution-detail rows on CPU; 5.917 s estimated time is the sample minimum. This is one component, without a listening-quality gate. Corrected DirectML inference and profile both failed at provider initialization with Windows error 80070057, producing no output or timing. A separate FP16 QNN DLC compile passed, but same-fixture NPU-requested inference was pending. Historical QNN talker profiling lacks a same-fixture numerical or full-pipeline gate. The older TFLite vocoder target was rejected on Windows ARM; no complete stream exists. | Audit the explicit QNN NPU inference, investigate the tested DirectML provider failure, and qualify talker/predictor plus complete local streaming, memory and sustained behavior; [vocoder evidence](../../../e2e_expansion_20260924/evidence/qwen_tts_vocoder_qualcomm/README.md). |
+| PC Snapdragon X Elite CRD / AI Hub | Qwen3-TTS 0.6B CustomVoice | COMPONENT_ONNX_CPU_VOCODER_PARITY_PROFILED; DIRECTML_RUNTIME_FAILED; TALKER_FP16_NUMERIC_FAIL; QNN_NPU_VOCODER_COMPILE_PASS; NOT_E2E_PROFILEABLE | The fresh ONNX vocoder inference on the exact Windows 11 ARM64 X Elite CRD requested CPU and returned an unsaturated waveform on the pinned fixture, matching local ONNX Runtime CPU with 8.09e-7 relative L2 and 121.84 dB SNR. Its 94-sample component profile had nearest-rank p50/p95 6.384/6.644 s and all 634 execution-detail rows on CPU; 5.917 s estimated time is the sample minimum. This is one component, without a listening-quality gate. Corrected DirectML inference and profile both failed at provider initialization with Windows error 80070057. A separate FP16 QNN DLC vocoder compile passed, but NPU-requested inference was pending. The 28-layer FP16 QNN talker step executed, yet changed CPU top-1 token 80 to 1720 with 8.57% logits relative L2 on a synthetic cache fixture; a separate ONNX CPU talker compile was submitted. The older TFLite vocoder target was rejected on Windows ARM; no complete stream exists. | Audit QNN vocoder and CPU talker controls, investigate DirectML failure, then qualify complete local streaming, memory and sustained behavior; [vocoder](../../../e2e_expansion_20260924/evidence/qwen_tts_vocoder_qualcomm/README.md) and [talker](../../../e2e_expansion_20260924/evidence/qwen_tts_talker_qualcomm/README.md) evidence. |
 | PC Snapdragon X Elite CRD / AI Hub | Qwen3.8-27B | NOT_E2E_PROFILEABLE | No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device. | Supply and validate the missing matching artifact/backend, then run the complete specified workload. |
-| PC Snapdragon X Elite CRD / AI Hub | MiniCPM-o 4.5 | NOT_E2E_PROFILEABLE | No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device. | Supply and validate the missing matching artifact/backend, then run the complete specified workload. |
+| PC Snapdragon X Elite CRD / AI Hub | MiniCPM-o 4.5 | COMPONENT_SPEECH_HEAD_QNN_COMPILE_PASS; INFERENCE_PENDING; NOT_E2E_PROFILEABLE | The real 20-layer MiniCPM-o speech-head source compiled to FP16 QNN DLC for the exact X Elite CRD. Same-fixture NPU-requested inference was pending; no numerical output or placement profile exists. Thinker/encoders/vocoder and a device-local stateful Omni backend remain unqualified. | Audit the [X Elite speech-head inference](../../../e2e_expansion_20260924/evidence/minicpmo_tts_qualcomm/README.md), then validate the complete multimodal pipeline, state, quality, memory and sustained behavior. |
 | PC Snapdragon X Elite CRD / AI Hub | InternVLA-A1 | NOT_E2E_PROFILEABLE | No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device. | Supply and validate the missing matching artifact/backend, then run the complete specified workload. |
 | Mobile Galaxy S25 / Snapdragon 8 Elite for Galaxy | Spark-X2.5 | NOT_E2E_PROFILEABLE | Fresh 2026-09-22 W8A16 full-attention component inference/profile passed; full1024 denotes one cache bucket. No full prefill/decode/KV/ring/sampling loop. | Implement and validate local 128-token generation, 512/1024 transitions, cancellation and resident memory. |
-| Mobile Galaxy S25 / Snapdragon 8 Elite for Galaxy | Qwen3-TTS 0.6B CustomVoice | NOT_E2E_PROFILEABLE | Fresh FP16 predictor inference passed; its one output exactly matches the same compiled artifact/dataset reference. Historical decoder parity exists. No complete co-resident NPU/GPU stream. | Validate the device-local talker/predictor/vocoder pipeline, output quality, recovery and 30-minute thermal behavior. |
+| Mobile Galaxy S25 / Snapdragon 8 Elite for Galaxy | Qwen3-TTS 0.6B CustomVoice | TALKER_FP16_NUMERIC_FAIL; NOT_E2E_PROFILEABLE | Fresh FP16 predictor inference passed; its one output exactly matches the same compiled artifact/dataset reference. The real 28-layer FP16 QNN talker step executed on the synthetic cache fixture, but changed CPU top-1 speech token 80 to 412 with 8.99% logits relative L2 and maximum new-K/V relative L2 11.71%. This tested talker artifact is numerically unqualified; no complete co-resident NPU/GPU stream exists. | Improve and revalidate the [talker artifact](../../../e2e_expansion_20260924/evidence/qwen_tts_talker_qualcomm/README.md), then qualify the device-local predictor/vocoder pipeline, output quality, recovery and 30-minute thermal behavior. |
 | Mobile Galaxy S25 / Snapdragon 8 Elite for Galaxy | Qwen3.8-27B | NOT_E2E_PROFILEABLE | No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device. | Supply and validate the missing matching artifact/backend, then run the complete specified workload. |
-| Mobile Galaxy S25 / Snapdragon 8 Elite for Galaxy | MiniCPM-o 4.5 | NOT_E2E_PROFILEABLE | Fresh FP16 speech-head inference passed; all 41 outputs exactly match the same artifact/dataset reference. Historical FP16 numerical parity passed; tested W8A16 top-1 parity failed. Thinker/encoders/vocoder are not qualified together. | Supply a qualified complete mobile checkpoint/export and validate the full multimodal/stateful pipeline. |
-| Mobile Galaxy S25 / Snapdragon 8 Elite for Galaxy | InternVLA-A1 | COMPONENT_NPU_ARTIFACT_COMPILE_PASS; INFERENCE_PENDING; NOT_E2E_PROFILEABLE | The real Place_Markpen Cosmos image encoder FP32 ONNX export matched its source on two six-frame fixtures (relative L2 8.74e-7/2.29e-6) after packaging external weights into a single file. Galaxy S25 FP16 QNN DLC compilation passed, but same-fixture NPU-requested inference was pending. There is no measured NPU placement, latent/action-policy parity, complete qualified target bundle or device-local controller; ADB has no attached device. | Audit the [S25 encoder inference](../../../e2e_expansion_20260924/evidence/internvla_cosmos_qualcomm/README.md), then validate action quality, state, admission, cancellation and sustained behavior. |
+| Mobile Galaxy S25 / Snapdragon 8 Elite for Galaxy | MiniCPM-o 4.5 | COMPONENT_FP16_SPEECH_HEAD_NUMERIC_MEASURED; NOT_E2E_PROFILEABLE | Fresh FP16 speech-head inference passed; all 41 outputs exactly matched the same artifact/dataset reference. Against the recovered original ONNX CPU source on the same fixture, logits relative L2 was 0.3605%, maximum new-K/V relative L2 1.311%, and top-1 speech token 1867 matched. Historical FP16 parity passed; tested W8A16 top-1 parity failed. No actual NPU placement profile, generated-audio quality gate, or qualified thinker/encoders/vocoder bundle exists. | Qualify device placement, a complete mobile checkpoint/export and the full multimodal/stateful pipeline; [speech-head evidence](../../../e2e_expansion_20260924/evidence/minicpmo_tts_qualcomm/README.md). |
+| Mobile Galaxy S25 / Snapdragon 8 Elite for Galaxy | InternVLA-A1 | COMPONENT_COSMOS_NUMERIC_MEASURED; SYNTHETIC_ACTION_SENSITIVITY_MEASURED; NOT_E2E_PROFILEABLE | The real Place_Markpen Cosmos image encoder FP32 ONNX export matched its source on two six-frame fixtures. Galaxy S25 FP16 QNN DLC inference returned a finite latent within 0.0524% relative L2 / 65.61 dB SNR of the pinned pattern source. Injecting this device latent into the same real policy on local CPU changed synthetic `[1,50,32]` actions by 0.742% relative L2; the CPU source repeated bitwise identically. No real-observation task tolerance or measured NPU placement exists (profile pending), and the policy was not resident on S25. ADB has no attached device. | Audit the [S25 encoder profile](../../../e2e_expansion_20260924/evidence/internvla_cosmos_qualcomm/README.md), then validate real action quality, device-local state, admission, cancellation and sustained behavior. |
 | Mobile Galaxy S24 / Snapdragon 8 Gen 3 | Spark-X2.5 | COMPONENT_NPU_INFERENCE_PROFILED; NOT_E2E_PROFILEABLE | The real 1.7B checkpoint's fixed-shape full-attention decoder **one-layer** W8A16 QDQ artifact compiled as QNN DLC and ran on an exact hosted Galaxy S24 (Android 14). A same-dataset inference returned finite hidden/new-K/new-V outputs identical to the prior S25 NPU outputs; the profile attributed all 73 execution-detail rows to NPU. Across 100 component samples, nearest-rank p50/p95 was 1.109/1.205 ms; the 1.098 ms `estimated_inference_time` field is the minimum, not a median. Relative L2 versus original FP32 ONNX CPU was 1.351%/0.377%/0.650% for hidden/K/V; no token-quality tolerance is known. This is one cache bucket, not 28-layer prefill/decode, KV-ring, sampling, a resident local controller or complete-request timing. ADB has no attached device. | Qualify a complete device-resident artifact/controller with state and token-quality gates, 128-token generation across 512/1024 context transitions, admission/cancel and sustained memory/power; [S24 NPU raw evidence](../../../e2e_expansion_20260923/evidence/spark_s24_full_attention/README.md). |
-| Mobile Galaxy S24 / Snapdragon 8 Gen 3 | Qwen3-TTS 0.6B CustomVoice | NOT_E2E_PROFILEABLE | Historical FP16 decoder parity has SNR 39.13 dB. Tested calibrated INT8 decoder parity failed with negative SNR. Neither result is a full local TTS stream. | Validate the complete local pipeline and a quality-passing artifact set. |
+| Mobile Galaxy S24 / Snapdragon 8 Gen 3 | Qwen3-TTS 0.6B CustomVoice | TALKER_FP16_NUMERIC_FAIL; NOT_E2E_PROFILEABLE | The real 28-layer FP16 QNN talker step executed on the synthetic cache fixture, but changed CPU top-1 speech token 80 to 1720 with 9.33% logits relative L2 and maximum new-K/V relative L2 11.23%. This differs from an earlier FP16 decoder parity test (39.13 dB SNR) on a different component/fixture; the tested INT8 decoder parity failed with negative SNR. No full local TTS stream exists. | Resolve the [talker numerical difference](../../../e2e_expansion_20260924/evidence/qwen_tts_talker_qualcomm/README.md), then validate the complete local pipeline and a quality-passing artifact set. |
 | Mobile Galaxy S24 / Snapdragon 8 Gen 3 | Qwen3.8-27B | NOT_E2E_PROFILEABLE | No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device. | Supply and validate the missing matching artifact/backend, then run the complete specified workload. |
-| Mobile Galaxy S24 / Snapdragon 8 Gen 3 | MiniCPM-o 4.5 | NOT_E2E_PROFILEABLE | Historical FP16 speech-head job re-fetched successfully; SNR 49.22 dB/top-1 match in the recorded parity test. W8A16 top-1 failed. Not complete MiniCPM-o. | Qualify thinker/encoders/vocoder and stateful local integration. |
+| Mobile Galaxy S24 / Snapdragon 8 Gen 3 | MiniCPM-o 4.5 | COMPONENT_FP16_SPEECH_HEAD_NUMERIC_MEASURED; NOT_E2E_PROFILEABLE | The retained FP16 speech-head job used a dataset with identical tensors to the recovered S25 fixture. Against original ONNX CPU, logits relative L2 was 0.3459%, maximum new-K/V relative L2 1.235%, and top-1 speech token 1867 matched. Earlier parity reported 49.22 dB SNR/top-1 match; tested W8A16 top-1 failed. Actual NPU placement, generated-audio quality and the rest of the model remain unqualified. | Qualify placement, thinker/encoders/vocoder and stateful local integration; [speech-head evidence](../../../e2e_expansion_20260924/evidence/minicpmo_tts_qualcomm/README.md). |
 | Mobile Galaxy S24 / Snapdragon 8 Gen 3 | InternVLA-A1 | NOT_E2E_PROFILEABLE | No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device. | Supply and validate the missing matching artifact/backend, then run the complete specified workload. |
 | Embedded SA8775P ADP / AI Hub | Spark-X2.5 | COMPONENT_NPU_INFERENCE_PROFILED; NOT_E2E_PROFILEABLE | A real Spark-X2.5-1.7B one-layer W8A16 QDQ attention artifact, compiled as QNN DLC, ran on the exact hosted SA8775P ADP (Android 14). Inference returned finite hidden/new-K/new-V tensors bitwise identical to the prior S24/S25 NPU outputs on the same fixture; the profile attributed all 73 execution-detail rows to NPU. Across 100 component samples, nearest-rank p50/p95 was 1.837/2.079 ms; the 1.749 ms estimated-time field is the minimum. Hidden/K/V relative L2 versus original FP32 ONNX CPU was 1.351%/0.377%/0.650%, without a next-token quality tolerance. There is no complete 28-layer prefill/decode/KV-ring/sampling loop, resident Omni controller or complete-request timing. | Qualify a complete embedded resident artifact/controller with state, token quality, memory admission/cancel and sustained power; [SA8775P NPU raw evidence](../../../e2e_expansion_20260923/evidence/spark_sa8775_full_attention/README.md). |
-| Embedded SA8775P ADP / AI Hub | Qwen3-TTS 0.6B CustomVoice | COMPONENT_CPU_REQUESTED_VOCODER_PARITY_PASS; COMPONENT_GPU_NUMERIC_FAIL; NOT_E2E_PROFILEABLE | On the exact SA8775P, the same TFLite binary's CPU-requested Code2Wav inference matched original ONNX CPU on the retained fixture within 4.13e-6 relative L2 / 107.67 dB SNR, with no saturation. Actual CPU placement/timing profile was pending. The GPU-requested output saturated 96.63% of samples, with 11.832 relative L2 / -21.46 dB SNR; its fresh 100-sample profile p50/p95 was 1.776/1.880 s with 403 CPU and 335 GPU execution-detail rows, timing a numerically failed waveform. Historical NPU talker profiling lacks a full pipeline. | Audit the pending CPU profile and FP32-preserving GPU control, establish listening quality, then validate co-resident talker/predictor/vocoder, streaming, memory and sustained behavior; [raw vocoder evidence](../../../e2e_expansion_20260924/evidence/qwen_tts_vocoder_qualcomm/README.md). |
+| Embedded SA8775P ADP / AI Hub | Qwen3-TTS 0.6B CustomVoice | COMPONENT_CPU_REQUESTED_VOCODER_PARITY_PASS; COMPONENT_GPU_NUMERIC_FAIL; TALKER_FP16_NUMERIC_FAIL; NOT_E2E_PROFILEABLE | On the exact SA8775P, the same TFLite binary's CPU-requested Code2Wav inference matched original ONNX CPU within 4.13e-6 relative L2 / 107.67 dB SNR, without saturation; CPU placement/timing profile was pending. The GPU-requested output saturated 96.63% of samples with 11.832 relative L2; its fresh 100-sample mixed CPU/GPU profile p50/p95 was 1.776/1.880 s, timing failed audio. Even with FP32-preserving GPU options, 84.89% of samples saturated with 11.389 relative L2. The 28-layer FP16 QNN talker step executed but changed CPU top-1 token 80 to 1720 with 9.43% logits relative L2. No complete pipeline exists. | Isolate GPU delegate and talker numerical failures, audit pending CPU profile, then establish listening quality, co-resident streaming, memory and sustained behavior; [vocoder](../../../e2e_expansion_20260924/evidence/qwen_tts_vocoder_qualcomm/README.md) and [talker](../../../e2e_expansion_20260924/evidence/qwen_tts_talker_qualcomm/README.md) evidence. |
 | Embedded SA8775P ADP / AI Hub | Qwen3.8-27B | NOT_E2E_PROFILEABLE | No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device. | Supply and validate the missing matching artifact/backend, then run the complete specified workload. |
-| Embedded SA8775P ADP / AI Hub | MiniCPM-o 4.5 | NOT_E2E_PROFILEABLE | No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device. | Supply and validate the missing matching artifact/backend, then run the complete specified workload. |
+| Embedded SA8775P ADP / AI Hub | MiniCPM-o 4.5 | COMPONENT_SPEECH_HEAD_QNN_COMPILE_PASS; INFERENCE_PENDING; NOT_E2E_PROFILEABLE | The real 20-layer MiniCPM-o speech-head source compiled to FP16 QNN DLC for the exact SA8775P ADP. Same-fixture NPU-requested inference was pending, so output parity and actual placement are unknown. No complete thinker/encoders/vocoder bundle or device-local stateful controller exists. | Audit the [SA8775P speech-head inference](../../../e2e_expansion_20260924/evidence/minicpmo_tts_qualcomm/README.md), then qualify the complete multimodal pipeline and sustained behavior. |
 | Embedded SA8775P ADP / AI Hub | InternVLA-A1 | NOT_E2E_PROFILEABLE | No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device. | Supply and validate the missing matching artifact/backend, then run the complete specified workload. |
 | Embedded RB3 Gen 2 / QCS6490 / AI Hub | Spark-X2.5 | COMPONENT_CPU_PARITY_PASS; COMPONENT_NPU_NUMERIC_UNQUALIFIED; NOT_E2E_PROFILEABLE | On the exact hosted Dragonwing RB3 Gen 2 Vision Kit (QCS6490, QC Linux 1.6), the reused S24 W8A16 QNN DLC and a separately RB3-compiled W8A16 DLC each failed inference/profile at QnnModel_composeGraphsFromDlc: MODEL_GRAPH_ERROR. An FP32 ONNX one-layer CPU route passed with all 56 profile rows on CPU, source-relative hidden/K/V L2 of 8.73e-7/2.98e-7/3.37e-7 and 100-sample component p50/p95 19.304/21.113 ms. A calibrated W8A8 QNN DLC ran with all 61 rows on NPU and p50/p95 5.610/5.761 ms, but hidden/K/V relative L2 was 20.914%/1.702%/4.413% versus FP32 CPU, without a next-token quality gate. These are different precisions, not a qualified speedup; no complete 28-layer prefill/decode/KV-ring/sampling, local Omni controller or complete-request timing exists. | Improve representative W8A8 calibration or find another compatible quality-passing artifact; validate token parity, then full device-resident generation, admission/cancel and sustained power; [RB3 raw evidence](../../../e2e_expansion_20260923/evidence/spark_rb3_full_attention/README.md). |
 | Embedded RB3 Gen 2 / QCS6490 / AI Hub | Qwen3-TTS 0.6B CustomVoice | COMPONENT_MIXED_VOCODER_PROFILED_NUMERIC_UNQUALIFIED; NOT_E2E_PROFILEABLE | Fresh same-fixture TFLite GPU-requested vocoder inference returned a finite unsaturated waveform, but relative L2 versus original ONNX CPU was 15.73% (16.06 dB SNR), without a listening-quality tolerance; it is not a qualified audio path. The fresh 100-sample component profile had nearest-rank p50/p95 3.099/3.133 s and 403 CPU plus 335 GPU execution-detail rows. Its 3.070 s estimated-time field is the sample minimum, not median. The historical NPU talker build was rejected for floating-point inputs; no full model pipeline exists. | Establish waveform quality and a compatible integer talker artifact, then validate complete local execution, memory and sustained behavior; [vocoder evidence](../../../e2e_expansion_20260924/evidence/qwen_tts_vocoder_qualcomm/README.md). |
 | Embedded RB3 Gen 2 / QCS6490 / AI Hub | Qwen3.8-27B | NOT_E2E_PROFILEABLE | No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device. | Supply and validate the missing matching artifact/backend, then run the complete specified workload. |
-| Embedded RB3 Gen 2 / QCS6490 / AI Hub | MiniCPM-o 4.5 | NOT_E2E_PROFILEABLE | No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device. | Supply and validate the missing matching artifact/backend, then run the complete specified workload. |
+| Embedded RB3 Gen 2 / QCS6490 / AI Hub | MiniCPM-o 4.5 | COMPONENT_SPEECH_HEAD_QNN_COMPILE_PASS; INFERENCE_PENDING; NOT_E2E_PROFILEABLE | The real 20-layer MiniCPM-o speech-head source compiled to FP16 QNN DLC for the exact RB3 Gen 2 kit. Same-fixture NPU-requested inference was pending; RB3's older Spark/Qwen graph failures cannot substitute for this artifact's outcome. No numerical output, placement profile, complete model bundle or local stateful controller exists. | Audit the [RB3 speech-head inference](../../../e2e_expansion_20260924/evidence/minicpmo_tts_qualcomm/README.md), then qualify the complete multimodal pipeline and sustained behavior. |
 | Embedded RB3 Gen 2 / QCS6490 / AI Hub | InternVLA-A1 | NOT_E2E_PROFILEABLE | No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device. | Supply and validate the missing matching artifact/backend, then run the complete specified workload. |

 ## Measured request timing
@@ -15,13 +15,15 @@ remain open; the 2026-09-22 audit remains the historical baseline.

 The [Spark hosted Qualcomm component expansion](../../benchmarks/edge_harness/results/e2e_expansion_20260923/evidence/spark_s24_full_attention/README.md) advances M1 only to one real fixed-shape decoder attention layer. The same W8A16 QNN DLC ran on S24, Snapdragon X Elite CRD and SA8775P ADP NPUs with identical outputs on one pinned fixture; S25 had prior separate component evidence. On [RB3 Gen 2](../../benchmarks/edge_harness/results/e2e_expansion_20260923/evidence/spark_rb3_full_attention/README.md), the tested W8A16 graph failed QNN loading even after an exact-device compile. FP32 ONNX ran on CPU with near-source parity, while calibrated W8A8 ran on NPU but had 20.9% hidden-state relative L2 and remains numerically unqualified. These are component C results, not full M1: device-local 28-layer prefill/continuous decode, KV/ring, sampling, token quality, admission/cancellation, complete-request timing and sustained power/thermal gates remain open. AI Hub is the test facility, not a deployment dependency.

-The [Qwen3-TTS Code2Wav Qualcomm follow-up](../../benchmarks/edge_harness/results/e2e_expansion_20260924/evidence/qwen_tts_vocoder_qualcomm/README.md) adds a retained same-fixture numerical check to M2. A fresh SA8775P GPU-requested output saturated 96.63% of samples and differs from original ONNX CPU by 11.832 relative L2, so that route fails the numerical gate despite profiling. The same binary's CPU-requested output on the exact SA device matched original ONNX CPU within 4.13e-6 relative L2; its placement profile remained pending. RB3 TFLite GPU-requested inference returned an unsaturated but 15.73%-relative-L2 divergent waveform; its fresh 100-sample mixed CPU/GPU component p50/p95 was 3.099/3.133 s. X Elite ONNX CPU inference matched the local ONNX Runtime output with 8.09e-7 relative L2; its 94-sample component profile was p50/p95 6.384/6.644 s with all execution-detail rows on CPU. Corrected DirectML inference and profile both failed during provider initialization; separate FP16 QNN DLC compilation passed with NPU-requested inference pending. SA FP32-preserving GPU control remained pending. M2 still requires a quality-passing talker/predictor/vocoder set, device-local co-residency and handoff, playable streaming and sustained memory/power validation.
+The [Qwen3-TTS Code2Wav Qualcomm follow-up](../../benchmarks/edge_harness/results/e2e_expansion_20260924/evidence/qwen_tts_vocoder_qualcomm/README.md) adds a retained same-fixture numerical check to M2. A fresh SA8775P GPU-requested output saturated 96.63% of samples and differs from original ONNX CPU by 11.832 relative L2, so that route fails the numerical gate despite profiling. The same binary's CPU-requested output on the exact SA device matched original ONNX CPU within 4.13e-6 relative L2; its placement profile remained pending. A FP32-preserving GPU-option control still saturated 84.89% of samples, so that option did not fix the tested route. RB3 TFLite GPU-requested inference returned an unsaturated but 15.73%-relative-L2 divergent waveform; its fresh 100-sample mixed CPU/GPU component p50/p95 was 3.099/3.133 s. X Elite ONNX CPU inference matched the local ONNX Runtime output with 8.09e-7 relative L2; its 94-sample component profile was p50/p95 6.384/6.644 s with all execution-detail rows on CPU. Corrected DirectML inference and profile both failed during provider initialization; separate FP16 QNN DLC compilation passed with NPU-requested inference pending. M2 still requires a quality-passing talker/predictor/vocoder set, device-local co-residency and handoff, playable streaming and sustained memory/power validation.

-The identical SA/RB3 TFLite vocoder binary also matched original ONNX CPU within 3.45e-6 relative L2 on a separate local HX370 x86-64 LiteRT 2.2.0 CPU control. Exact-SA CPU-requested parity now narrows the failure further to the GPU-requested route. Its per-node placement and the FP32-preserving GPU control are the next isolation gates; neither identifies the failing delegate operation yet.
+The identical SA/RB3 TFLite vocoder binary also matched original ONNX CPU within 3.45e-6 relative L2 on a separate local HX370 x86-64 LiteRT 2.2.0 CPU control. Exact-SA CPU-requested parity narrows the failure to the GPU-requested route. The FP32-preserving GPU option did not fix it, so per-node placement and delegate-operation isolation remain open.

-A separate [28-layer talker decode-step attempt](../../benchmarks/edge_harness/results/e2e_expansion_20260924/evidence/qwen_tts_talker_qualcomm/README.md) retains a synthetic fixed-cache fixture and CPU reference. Historical FP16 QNN context-binary compiles passed for S24, S25, SA8775P and X Elite; their same-fixture NPU inference was pending at this snapshot. RB3 rejected that floating-point-input artifact. None advances M2 to numerical talker or full-stream qualification yet.
+A separate [28-layer talker decode-step attempt](../../benchmarks/edge_harness/results/e2e_expansion_20260924/evidence/qwen_tts_talker_qualcomm/README.md) retains a synthetic fixed-cache fixture and CPU reference. Historical FP16 QNN context-binary inference executed on S24, S25, SA8775P and X Elite, but all four changed the CPU top-1 speech token with 8.57–9.43% logits relative L2. RB3 rejected that floating-point-input artifact. An explicit X Elite ONNX CPU fallback compile was pending. None advances M2 to numerical talker or full-stream qualification yet.

-For M4, a [Galaxy S25 Cosmos encoder attempt](../../benchmarks/edge_harness/results/e2e_expansion_20260924/evidence/internvla_cosmos_qualcomm/README.md) repackages the real Place_Markpen FP32 ONNX export after retaining two CPU parity fixtures. FLOAT16 QNN DLC compilation passed and same-fixture NPU-requested inference was pending. This is only an artifact feasibility gate, not an NPU execution or full-policy result.
+For M4, a [MiniCPM-o 4.5 speech-head study](../../benchmarks/edge_harness/results/e2e_expansion_20260924/evidence/minicpmo_tts_qualcomm/README.md) recovered the historical 20-layer ONNX source and matching S24/S25 fixtures. Both FP16 context-binary outputs preserved the ONNX CPU top-1 speech token on one decode step, with logits relative L2 0.346%/0.360%. New FP16 QNN DLC compiles passed for SA8775P, X Elite and RB3, while same-fixture NPU-requested inference remained pending. This is component C numerical evidence only, not thinker/encoder/vocoder or complete multimodal support.
+
+For M4, a [Galaxy S25 Cosmos encoder attempt](../../benchmarks/edge_harness/results/e2e_expansion_20260924/evidence/internvla_cosmos_qualcomm/README.md) repackages the real Place_Markpen FP32 ONNX export after retaining two CPU parity fixtures. FLOAT16 QNN DLC inference returned a latent within 0.0524% relative L2 of the source on one synthetic pattern. Injecting that output into the real policy on local CPU changed synthetic actions by 0.742% relative L2, without a task tolerance. NPU placement profile was pending, and the policy did not run on S25. This is component/action-sensitivity evidence, not device-local M4 qualification.

 The [WSL CPU expansion](../../benchmarks/edge_harness/results/e2e_expansion_20260923/README.md)
 adds constrained three-stage MiniCPM-o text-to-speech requests and a CPU-only