Add bioacoustic clock and actuator proofs

artifacts/bioacoustic-training/bioacoustic-20260524-150729/baseline-mfcc-index/blur-light/summary.json

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artifacts/bioacoustic-training/bioacoustic-20260524-150729/baseline-mfcc-index/warp-heavy-blur/summary.json

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artifacts/bioacoustic-training/bioacoustic-20260524-150729/baseline-mfcc-index/warp-light-blur/summary.json

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+  "StartedAtUtc": "2026-05-24T15:07:29.1407348\u002B00:00",
+  "Toolchain": "Mimir.BufferSmoke --bioacoustic-train",
+  "HypothesisId": "robust-wide-index",
+  "HypothesisNotes": "More cepstra and a wider augmentation bank. It pays more CPU for warped-room survival.",
+  "DegradationName": "warp-heavy-blur",
+  "SampleRate": 48000,
+  "Seconds": 0.75,
+  "ExpectedEvents": 5,
+  "ObservationCount": 5,
+  "CorrectAnchors": 4,
+  "Precision": 0.8,
+  "Recall": 0.8,
+  "Confidence": 0.8572875750018513,
+  "TimingMeanAbsoluteErrorSamples": 46176,
+  "DecodeMilliseconds": 265.865,
+  "RealtimeFactor": 2.820980572847122,
+  "IdentityScore": 0.8057287575001852,
+  "TimingScore": 0.19413521186402094,
+  "SpeedScore": 0.05641961145694244,
+  "TotalScore": 0.5457800900770271,
+  "RoundTripMelBins": 48,
+  "RoundTripCepstralCoefficients": 20,
+  "RoundTripFrameCount": 67,
+  "RoundTripRmsRatio": 0.0004699618910325598,
+  "ClockHypothesis": {
+    "AnchorCount": 1,
+    "SourceOffsetSamples": -480,
+    "EffectiveSampleRate": 48000,
+    "MeanAbsoluteErrorSamples": 0,
+    "Confidence": 0.19413521186402094,
+    "Score": 0.19413521186402094
+  },
+  "Decoder": {
+    "FftSize": 1024,
+    "HopSize": 192,
+    "MelBins": 48,
+    "CepstralCoefficients": 18,
+    "TableCount": 5,
+    "HashBits": 15,
+    "NearHashRadius": 4,
+    "DenseStepSeconds": 0.03,
+    "ProposalBudgetMultiplier": 9,
+    "TemplateAugmentations": [
+      "template-clean",
+      "template-blur",
+      "template-warp-light",
+      "template-warp-blur",
+      "template-warp-heavy"
+    ]
+  },
+  "Artifacts": [
+    {
+      "Kind": "pre-warp-audio",
+      "Uri": "robust-wide-index/warp-heavy-blur/pre-warp.wav",
+      "ContentHash": "4862d46ea00c1d387387028f90147b98bd6e36fe594eefdcfa1b6360f7348f29"
+    },
+    {
+      "Kind": "post-warp-audio",
+      "Uri": "robust-wide-index/warp-heavy-blur/post-warp.wav",
+      "ContentHash": "3ca3e9bbaa0ae0fc00ebe085f968f53d45e1a2b71511e851c1aa0bbf6cd34410"
+    },
+    {
+      "Kind": "reconstructed-from-detections",
+      "Uri": "robust-wide-index/warp-heavy-blur/reconstructed-from-detections.wav",
+      "ContentHash": "22ef7a9bcc0718435942a3c66af3174a24c196bd8b36c5bdfa467507ea59bed4"
+    },
+    {
+      "Kind": "summary-json",
+      "Uri": "robust-wide-index/warp-heavy-blur/summary.json",
+      "ContentHash": ""
+    }
+  ],
+  "Observations": [
+    {
+      "EventIndex": 0,
+      "SampleOffset": 0,
+      "Confidence": 0.9527487074713425
+    },
+    {
+      "EventIndex": 1,
+      "SampleOffset": 10080,
+      "Confidence": 0.886024872438331
+    },
+    {
+      "EventIndex": 30,
+      "SampleOffset": 11520,
+      "Confidence": 0.539710964976235
+    },
+    {
+      "EventIndex": 2,
+      "SampleOffset": 18720,
+      "Confidence": 0.9706760593201047
+    },
+    {
+      "EventIndex": 3,
+      "SampleOffset": 24480,
+      "Confidence": 0.9372772708032441
+    }
+  ]
+}

artifacts/bioacoustic-training/bioacoustic-20260524-150729/robust-wide-index/warp-light-blur/summary.json

@@ -0,0 +1,103 @@
+{
+  "ResultId": "bioacoustic-20260524-150729:robust-wide-index:warp-light-blur",
+  "RunId": "bioacoustic-20260524-150729",
+  "StartedAtUtc": "2026-05-24T15:07:29.1407348\u002B00:00",
+  "Toolchain": "Mimir.BufferSmoke --bioacoustic-train",
+  "HypothesisId": "robust-wide-index",
+  "HypothesisNotes": "More cepstra and a wider augmentation bank. It pays more CPU for warped-room survival.",
+  "DegradationName": "warp-light-blur",
+  "SampleRate": 48000,
+  "Seconds": 0.75,
+  "ExpectedEvents": 5,
+  "ObservationCount": 5,
+  "CorrectAnchors": 4,
+  "Precision": 0.8,
+  "Recall": 0.8,
+  "Confidence": 0.9141968184634031,
+  "TimingMeanAbsoluteErrorSamples": 13920,
+  "DecodeMilliseconds": 224.6397,
+  "RealtimeFactor": 3.338679672382041,
+  "IdentityScore": 0.8114196818463404,
+  "TimingScore": 0.525243980114753,
+  "SpeedScore": 0.06677359344764082,
+  "TotalScore": 0.6109788378549148,
+  "RoundTripMelBins": 48,
+  "RoundTripCepstralCoefficients": 20,
+  "RoundTripFrameCount": 67,
+  "RoundTripRmsRatio": 0.00014185500315276218,
+  "ClockHypothesis": {
+    "AnchorCount": 3,
+    "SourceOffsetSamples": -2618.3634428642745,
+    "EffectiveSampleRate": 48207.36165285075,
+    "MeanAbsoluteErrorSamples": 204.5918436878574,
+    "Confidence": 0.525243980114753,
+    "Score": 0.5490109724317167
+  },
+  "Decoder": {
+    "FftSize": 1024,
+    "HopSize": 192,
+    "MelBins": 48,
+    "CepstralCoefficients": 18,
+    "TableCount": 5,
+    "HashBits": 15,
+    "NearHashRadius": 4,
+    "DenseStepSeconds": 0.03,
+    "ProposalBudgetMultiplier": 9,
+    "TemplateAugmentations": [
+      "template-clean",
+      "template-blur",
+      "template-warp-light",
+      "template-warp-blur",
+      "template-warp-heavy"
+    ]
+  },
+  "Artifacts": [
+    {
+      "Kind": "pre-warp-audio",
+      "Uri": "robust-wide-index/warp-light-blur/pre-warp.wav",
+      "ContentHash": "4862d46ea00c1d387387028f90147b98bd6e36fe594eefdcfa1b6360f7348f29"
+    },
+    {
+      "Kind": "post-warp-audio",
+      "Uri": "robust-wide-index/warp-light-blur/post-warp.wav",
+      "ContentHash": "c3bb1eb61dd15004724160816be15f8462944106a1a3f2077c5f97c7901342b9"
+    },
+    {
+      "Kind": "reconstructed-from-detections",
+      "Uri": "robust-wide-index/warp-light-blur/reconstructed-from-detections.wav",
+      "ContentHash": "a6c2e9146b6d201492c12275700726d4cb489d3030ba605cc1c190bad871e84e"
+    },
+    {
+      "Kind": "summary-json",
+      "Uri": "robust-wide-index/warp-light-blur/summary.json",
+      "ContentHash": ""
+    }
+  ],
+  "Observations": [
+    {
+      "EventIndex": 0,
+      "SampleOffset": 1440,
+      "Confidence": 0.9568438177382963
+    },
+    {
+      "EventIndex": 1,
+      "SampleOffset": 8640,
+      "Confidence": 0.9299836885720597
+    },
+    {
+      "EventIndex": 9,
+      "SampleOffset": 11520,
+      "Confidence": 0.7589848046963656
+    },
+    {
+      "EventIndex": 2,
+      "SampleOffset": 18720,
+      "Confidence": 0.9809965027688126
+    },
+    {
+      "EventIndex": 3,
+      "SampleOffset": 24480,
+      "Confidence": 0.9441752785414812
+    }
+  ]
+}

artifacts/bioacoustic-training/bioacoustic-20260524-150729/robust-wide-index/warp-light/summary.json

@@ -0,0 +1,113 @@
+{
+  "ResultId": "bioacoustic-20260524-150729:robust-wide-index:warp-light",
+  "RunId": "bioacoustic-20260524-150729",
+  "StartedAtUtc": "2026-05-24T15:07:29.1407348\u002B00:00",
+  "Toolchain": "Mimir.BufferSmoke --bioacoustic-train",
+  "HypothesisId": "robust-wide-index",
+  "HypothesisNotes": "More cepstra and a wider augmentation bank. It pays more CPU for warped-room survival.",
+  "DegradationName": "warp-light",
+  "SampleRate": 48000,
+  "Seconds": 0.75,
+  "ExpectedEvents": 5,
+  "ObservationCount": 7,
+  "CorrectAnchors": 4,
+  "Precision": 0.5714285714285714,
+  "Recall": 0.8,
+  "Confidence": 0.6973654063470601,
+  "TimingMeanAbsoluteErrorSamples": 240610.2857142857,
+  "DecodeMilliseconds": 228.0981,
+  "RealtimeFactor": 3.2880589535818143,
+  "IdentityScore": 0.6868793977775632,
+  "TimingScore": 0.18675545569962676,
+  "SpeedScore": 0.06576117907163628,
+  "TotalScore": 0.47263344446234923,
+  "RoundTripMelBins": 48,
+  "RoundTripCepstralCoefficients": 20,
+  "RoundTripFrameCount": 67,
+  "RoundTripRmsRatio": 1.062162293139017E-06,
+  "ClockHypothesis": {
+    "AnchorCount": 1,
+    "SourceOffsetSamples": -960,
+    "EffectiveSampleRate": 48000,
+    "MeanAbsoluteErrorSamples": 0,
+    "Confidence": 0.18675545569962676,
+    "Score": 0.18675545569962676
+  },
+  "Decoder": {
+    "FftSize": 1024,
+    "HopSize": 192,
+    "MelBins": 48,
+    "CepstralCoefficients": 18,
+    "TableCount": 5,
+    "HashBits": 15,
+    "NearHashRadius": 4,
+    "DenseStepSeconds": 0.03,
+    "ProposalBudgetMultiplier": 9,
+    "TemplateAugmentations": [
+      "template-clean",
+      "template-blur",
+      "template-warp-light",
+      "template-warp-blur",
+      "template-warp-heavy"
+    ]
+  },
+  "Artifacts": [
+    {
+      "Kind": "pre-warp-audio",
+      "Uri": "robust-wide-index/warp-light/pre-warp.wav",
+      "ContentHash": "4862d46ea00c1d387387028f90147b98bd6e36fe594eefdcfa1b6360f7348f29"
+    },
+    {
+      "Kind": "post-warp-audio",
+      "Uri": "robust-wide-index/warp-light/post-warp.wav",
+      "ContentHash": "64a7e1a53b5529fa38a039055838449e4f4d7da9c96ea47c72e8e159006d6549"
+    },
+    {
+      "Kind": "reconstructed-from-detections",
+      "Uri": "robust-wide-index/warp-light/reconstructed-from-detections.wav",
+      "ContentHash": "caf69797d6f43f5f5a3eee41515923dcd3b25563a54692427c8227df0233b2f1"
+    },
+    {
+      "Kind": "summary-json",
+      "Uri": "robust-wide-index/warp-light/summary.json",
+      "ContentHash": ""
+    }
+  ],
+  "Observations": [
+    {
+      "EventIndex": 0,
+      "SampleOffset": 2880,
+      "Confidence": 0.9337772784981337
+    },
+    {
+      "EventIndex": 1,
+      "SampleOffset": 8640,
+      "Confidence": 0.9175578116476332
+    },
+    {
+      "EventIndex": 73,
+      "SampleOffset": 12960,
+      "Confidence": 0.3417469526010798
+    },
+    {
+      "EventIndex": 30,
+      "SampleOffset": 14400,
+      "Confidence": 0.5427608991372147
+    },
+    {
+      "EventIndex": 119,
+      "SampleOffset": 15840,
+      "Confidence": 0.24756166742074842
+    },
+    {
+      "EventIndex": 2,
+      "SampleOffset": 23040,
+      "Confidence": 0.9512111997475022
+    },
+    {
+      "EventIndex": 3,
+      "SampleOffset": 30192,
+      "Confidence": 0.946942035377108
+    }
+  ]
+}

artifacts/bioacoustic-training/bioacoustic-20260524-150729/run-summary.json

@@ -0,0 +1,19 @@
+{
+  "runId": "bioacoustic-20260524-150729",
+  "startedAtUtc": "2026-05-24T15:07:29.1407348\u002B00:00",
+  "sampleRate": 48000,
+  "seconds": 0.75,
+  "resultCount": 20,
+  "cache": "bioacoustic-training.cc",
+  "best": {
+    "ResultId": "bioacoustic-20260524-150729:compact-fast-index:clean-roundtrip",
+    "HypothesisId": "compact-fast-index",
+    "DegradationName": "clean-roundtrip",
+    "TotalScore": 0.752180275653637,
+    "IdentityScore": 0.9076804919634105,
+    "TimingScore": 0.905360983926792,
+    "SpeedScore": 0.13226696764749973,
+    "DecodeMilliseconds": 113.407,
+    "RealtimeFactor": 6.613348382374986
+  }
+}

docs/bioacoustic-timeline-watermark.md

@@ -135,3 +135,11 @@ The latest training receipt says identity can survive the current
 mel-cepstral degradation panel, but timing collapses under warped domains. That
 points the next cut at a global delay/clock/path hypothesis over detected words,
 not a larger brute-force dictionary.
+
+That cut has started in the harness. `--bioacoustic-train` now persists a
+global clock hypothesis per result and scores timing through its confidence
+rather than raw absolute word offset. The latest corrected receipt is
+`artifacts/bioacoustic-training/bioacoustic-20260524-150729/`; it shows compact
+indexing still wins clean throughput, baseline indexing remains steadier under
+degradation, highband indexing has real warped-path value, and anchor density
+must become its own benchmark metric.

docs/bioacoustic-training-harness.md

@@ -51,6 +51,42 @@ Speed tells whether the receiver shape can plausibly become a streaming machine.
 
 Run:
 
+```text
+artifacts/bioacoustic-training/bioacoustic-20260524-150729/
+```
+
+This run adds global clock hypothesis scoring. Timing is no longer raw
+per-word absolute error; it is the confidence of a coherent clock/path fit over
+detected words. Single-anchor fits are deliberately weak, and sample-rate slope
+is only allowed when enough anchors exist.
+
+Best aggregate result:
+
+```text
+compact-fast-index / clean-roundtrip
+total=0.752 identity=0.908 timing=0.905 speed=0.132 realtime=6.6x
+```
+
+Useful observations:
+
+- `compact-fast-index` is still the clean-throughput winner when enough anchors
+  survive.
+- `baseline-mfcc-index` is still the steadier degraded-path receiver.
+- `highband-room-index` shows real value under warp, but needs more anchor
+  density before it can own timing.
+- `compact-fast-index` remains faster, but its warped-path identity and timing
+  confidence are weaker.
+- `robust-wide-index` still costs too much for the current panel, but it gives
+  useful pressure under heavier degradation.
+- The global clock hypothesis is the right scoring authority, but short windows
+  and weak degradations can still starve it. Future training runs should score
+  time-to-lock separately from identity. Anchor coverage is now persisted in the
+  CultCache clock snapshot so optimization can target lock density directly.
+
+Previous receipt:
+
+Run:
+
 ```text
 artifacts/bioacoustic-training/bioacoustic-20260524-103438/
 ```

docs/implementation-plan.md

@@ -91,6 +91,9 @@ a named invariant that the native runtime cannot protect yet.
 - `MimirAudioSynchronizationStateTracker` owns the first smoothed per-source
   sync state: latest fractional delay, smoothed delay, confidence, per-band
   response evidence, and delay-slope/SRO estimate in ppm.
+- `faust/mimir_alignment_actuator.dsp` is the first Faust-owned sample movement
+  surface: six channels of bounded fractional delay and gain controls. Mimir
+  estimates delay/SRO; Faust/native DSP applies correction.
 - `MimirRuntime` updates audio sync analysis online as a bounded rotating
   service and can emit live sync telemetry with
   `MIMIR_SYNC_TELEMETRY_SECONDS`. UI and telemetry read cached reports/states;
@@ -153,7 +156,9 @@ a named invariant that the native runtime cannot protect yet.
   sync report.
   The active bioacoustic standalone receiver test recovers a delayed audio
   stream to below printed microsecond precision using only the motif codebook
-  and schedule state.
+  and schedule state. `--bioacoustic-actuator-self-test` estimates a synthetic
+  317.375-sample bioacoustic delay, applies the fractional correction, and
+  remeasures the residual below printed microsecond precision.
 - `MimirVideoFrameDescriptor` for dimensions, pixel format, stride, device
   timestamp, and native/GPU handle metadata.
 - `IMimirVideoCaptureDriver` and `MimirVideoCaptureDriverSource` as the live

faust/mimir_alignment_actuator.dsp

@@ -0,0 +1,37 @@
+declare name "Mimir Alignment Actuator";
+declare version "0.1";
+declare author "Mimir";
+declare license "MIT";
+
+import("stdfaust.lib");
+
+// Faust/native DSP owns sample movement. Mimir.Runtime estimates delay/SRO and
+// writes control values; this graph applies bounded fractional delay per source.
+// Positive delay controls hold a source back. Upstream runtime policy should
+// choose which sources are reference and which need correction.
+
+maxDelay = 4096;
+
+delay0 = hslider("source0/delay_samples", 0.0, 0.0, maxDelay, 0.001);
+delay1 = hslider("source1/delay_samples", 0.0, 0.0, maxDelay, 0.001);
+delay2 = hslider("source2/delay_samples", 0.0, 0.0, maxDelay, 0.001);
+delay3 = hslider("source3/delay_samples", 0.0, 0.0, maxDelay, 0.001);
+delay4 = hslider("source4/delay_samples", 0.0, 0.0, maxDelay, 0.001);
+delay5 = hslider("source5/delay_samples", 0.0, 0.0, maxDelay, 0.001);
+
+gain0 = hslider("source0/gain", 1.0, 0.0, 2.0, 0.001);
+gain1 = hslider("source1/gain", 1.0, 0.0, 2.0, 0.001);
+gain2 = hslider("source2/gain", 1.0, 0.0, 2.0, 0.001);
+gain3 = hslider("source3/gain", 1.0, 0.0, 2.0, 0.001);
+gain4 = hslider("source4/gain", 1.0, 0.0, 2.0, 0.001);
+gain5 = hslider("source5/gain", 1.0, 0.0, 2.0, 0.001);
+
+align(delay, gain, input) = input : de.fdelay(maxDelay, delay) : *(gain);
+
+process =
+    align(delay0, gain0),
+    align(delay1, gain1),
+    align(delay2, gain2),
+    align(delay3, gain3),
+    align(delay4, gain4),
+    align(delay5, gain5);

notes/current-system-map.md

@@ -191,6 +191,12 @@ artifact, but it leaves a concrete response profile: 14 frames, 12 anchors,
 0.865 clock confidence, and strongest bins around 4525, 4075, and 7225 Hz.
 Acoustic robustness is now the open problem, not clean loopback timing,
 standalone decoder shape, or basic response evidence.
+The first actuator proof now exists: `faust/mimir_alignment_actuator.dsp` owns
+six channels of bounded fractional delay/gain controls for Faust/native DSP,
+and `Mimir.BufferSmoke --bioacoustic-actuator-self-test --sample-rate 48000
+--delay-samples 317.375` proves the control loop shape by estimating a
+bioacoustic delay, applying fractional correction, and remeasuring residual
+below printed microsecond precision.
 
 ## Visual Fusion
 

src/Mimir.BufferSmoke/Program.cs

@@ -44,6 +44,13 @@ if (args.Any(arg => string.Equals(arg, "--bioacoustic-train", StringComparison.O
         ParseStringOption(args, "--output", "artifacts/bioacoustic-training")).ConfigureAwait(false);
 }
 
+if (args.Any(arg => string.Equals(arg, "--bioacoustic-actuator-self-test", StringComparison.OrdinalIgnoreCase)))
+{
+    return RunBioacousticActuatorSelfTest(
+        ParseIntOption(args, "--sample-rate", MimirBioacousticTimeline.SampleRate),
+        ParseDoubleOption(args, "--delay-samples", 317.375));
+}
+
 if (args.Any(arg => string.Equals(arg, "--standalone-chirp-bin-self-test", StringComparison.OrdinalIgnoreCase)))
 {
     return RunStandaloneChirpBinSelfTest(
@@ -470,6 +477,7 @@ static int RunBioacousticCepstralSmoke(int sampleRate, double seconds)
         var recall = expectedEvents.Count == 0 ? 0.0 : correctAnchors / (double)expectedEvents.Count;
         var confidence = observations.Count == 0 ? 0.0 : observations.Average(observation => observation.Confidence);
         var mae = MeanCepstralTimingError(observations, timeline, sampleRate);
+        var clock = FitBioacousticClockHypothesis(observations, timeline, sampleRate);
         var passed = correctAnchors >= Math.Min(3, expectedEvents.Count) &&
             precision >= 0.50 &&
             recall >= 0.35 &&
@@ -477,6 +485,7 @@ static int RunBioacousticCepstralSmoke(int sampleRate, double seconds)
         Console.WriteLine(
             $"bioacoustic-cepstral-smoke decoder=indexed-mfcc-word setting={setting.Name} observations={observations.Count} correct={correctAnchors}/{expectedEvents.Count} " +
             $"precision={precision:0.000} recall={recall:0.000} confidence={confidence:0.000} mae={mae:0.000} " +
+            $"globalAnchors={clock?.AnchorCount ?? 0} globalMae={clock?.MeanAbsoluteErrorSamples ?? double.PositiveInfinity:0.000} globalOffset={clock?.SourceOffsetSamples ?? double.NaN:0.000} globalRate={clock?.EffectiveSampleRate ?? double.NaN:0.000} " +
             $"melBins={analysis.MelBins} cepstra={analysis.CepstralCoefficients} stftFrames={analysis.FrameCount} rmsRatio={analysis.RmsRatio:0.000} pass={passed}");
         if (!passed)
         {
@@ -557,11 +566,12 @@ static async Task<int> RunBioacousticTrainingAsync(int sampleRate, double second
             var recall = expectedEvents.Count == 0 ? 0.0 : correctAnchors / (double)expectedEvents.Count;
             var confidence = observations.Count == 0 ? 0.0 : observations.Average(observation => observation.Confidence);
             var timingMae = MeanCepstralTimingError(observations, timeline, sampleRate);
+            var clock = FitBioacousticClockHypothesis(observations, timeline, sampleRate);
+            var globalTimingMae = clock?.MeanAbsoluteErrorSamples ?? double.PositiveInfinity;
+            var anchorCoverage = expectedEvents.Count == 0 || clock == null ? 0.0 : clock.AnchorCount / (double)expectedEvents.Count;
             var realtime = decodeMilliseconds <= 0.0 ? double.PositiveInfinity : seconds * 1000.0 / decodeMilliseconds;
             var identityScore = precision * 0.45 + recall * 0.45 + confidence * 0.10;
-            var timingScore = double.IsFinite(timingMae)
-                ? 1.0 / (1.0 + timingMae / Math.Max(1.0, sampleRate * 0.005))
-                : 0.0;
+            var timingScore = clock?.Confidence ?? 0.0;
             var speedScore = Math.Clamp(realtime / 50.0, 0.0, 1.0);
             var totalScore = identityScore * 0.62 + timingScore * 0.18 + speedScore * 0.20;
             var reconstructed = ReconstructDetectedBioacousticSong(observations, source.Length, sampleRate);
@@ -595,6 +605,16 @@ static async Task<int> RunBioacousticTrainingAsync(int sampleRate, double second
                 analysis.CepstralCoefficients,
                 analysis.FrameCount,
                 analysis.RmsRatio,
+                clock == null
+                    ? new BioacousticClockHypothesisSnapshot(0, 0, 0, 0, 0, 0, 0)
+                    : new BioacousticClockHypothesisSnapshot(
+                        clock.AnchorCount,
+                        clock.SourceOffsetSamples,
+                        clock.EffectiveSampleRate,
+                        clock.MeanAbsoluteErrorSamples,
+                        clock.Confidence,
+                        clock.Score,
+                        anchorCoverage),
                 new BioacousticTrainingDecoderSnapshot(
                     hypothesis.Decoder.FftSize,
                     hypothesis.Decoder.HopSize,
@@ -633,7 +653,7 @@ static async Task<int> RunBioacousticTrainingAsync(int sampleRate, double second
             results.Add(result);
             Console.WriteLine(
                 $"bioacoustic-train hypothesis={hypothesis.Id} degradation={degradation.Name} total={totalScore:0.000} identity={identityScore:0.000} timing={timingScore:0.000} speed={speedScore:0.000} " +
-                $"precision={precision:0.000} recall={recall:0.000} confidence={confidence:0.000} mae={timingMae:0.000} decodeMs={decodeMilliseconds:0.000} realtime={realtime:0.0}x artifacts={RelativePath(Directory.GetCurrentDirectory(), settingDirectory)}");
+                $"precision={precision:0.000} recall={recall:0.000} confidence={confidence:0.000} mae={timingMae:0.000} globalMae={globalTimingMae:0.000} globalAnchors={clock?.AnchorCount ?? 0} anchorCoverage={anchorCoverage:0.000} decodeMs={decodeMilliseconds:0.000} realtime={realtime:0.0}x artifacts={RelativePath(Directory.GetCurrentDirectory(), settingDirectory)}");
         }
     }
 
@@ -784,6 +804,66 @@ static int RunActiveSyncSelfTest(int sampleRate, MimirAudioSyncMode mode)
             : 1;
 }
 
+static int RunBioacousticActuatorSelfTest(int sampleRate, double delaySamples)
+{
+    var segments = Enumerable.Range(0, 4)
+        .Select(segment => MimirBioacousticTimeline.Default.RenderSegmentMonoFloat((ulong)segment, sampleRate))
+        .ToArray();
+    var reference = new float[segments.Sum(segment => segment.Length)];
+    var writeOffset = 0;
+    foreach (var segment in segments)
+    {
+        Array.Copy(segment, 0, reference, writeOffset, segment.Length);
+        writeOffset += segment.Length;
+    }
+
+    var delayed = ApplyFractionalDelay(reference, delaySamples);
+    var before = EstimateBioacousticDelay(reference, delayed, sampleRate);
+    if (before == null)
+    {
+        Console.Error.WriteLine("bioacoustic actuator self-test failed: analyzer could not estimate the delayed candidate");
+        return 1;
+    }
+
+    var corrected = ApplyFractionalDelay(delayed, -before.FractionalDelaySamples);
+    var after = EstimateBioacousticDelay(reference, corrected, sampleRate);
+    if (after == null)
+    {
+        Console.Error.WriteLine("bioacoustic actuator self-test failed: analyzer could not estimate the corrected candidate");
+        return 1;
+    }
+
+    var beforeError = Math.Abs(before.FractionalDelaySamples - delaySamples);
+    var afterResidual = Math.Abs(after.FractionalDelaySamples);
+    Console.WriteLine(
+        $"bioacoustic-actuator-self-test estimatedDelay={before.FractionalDelaySamples:0.000000} expected={delaySamples:0.000000} beforeErrorSamples={beforeError:0.000000} " +
+        $"correctedResidualSamples={afterResidual:0.000000} correctedResidualUs={afterResidual * 1_000_000.0 / sampleRate:0.000} beforeConfidence={before.Confidence:0.000} afterConfidence={after.Confidence:0.000}");
+    return beforeError < 0.05 &&
+        afterResidual * 1_000_000.0 / sampleRate < 1.0 &&
+        after.Confidence > 0.70
+            ? 0
+            : 1;
+}
+
+static MimirAudioSynchronizationReport? EstimateBioacousticDelay(float[] reference, float[] candidate, int sampleRate)
+{
+    const string referenceSourceId = "actuator-reference";
+    const string candidateSourceId = "actuator-candidate";
+    var referenceBuffer = new MimirRollingStreamBuffer(
+        new MimirStreamDescriptor(referenceSourceId, MimirStreamKind.Audio, MimirStreamOrigin.LocalDevice),
+        TimeSpan.FromSeconds(5));
+    var candidateBuffer = new MimirRollingStreamBuffer(
+        new MimirStreamDescriptor(candidateSourceId, MimirStreamKind.Audio, MimirStreamOrigin.LocalDevice),
+        TimeSpan.FromSeconds(5));
+    AppendFloatBlock(referenceBuffer, referenceSourceId, reference, sampleRate);
+    AppendFloatBlock(candidateBuffer, candidateSourceId, candidate, sampleRate);
+
+    var analyzer = new MimirAudioSynchronizationAnalyzer();
+    return analyzer
+        .Analyze([referenceBuffer, candidateBuffer], referenceSourceId, MimirAudioSyncMode.ChirpOnly)
+        .SingleOrDefault();
+}
+
 static string SyncModeLabel(MimirAudioSyncMode mode)
 {
     return mode == MimirAudioSyncMode.ChirpOnly ? "chirp-only" : "hybrid";
@@ -1543,6 +1623,151 @@ static double MeanCepstralTimingError(
         Math.Abs(observation.SampleOffset - timeline.EventForIndex(observation.EventIndex).StartSeconds * sampleRate));
 }
 
+static BioacousticClockHypothesis? FitBioacousticClockHypothesis(
+    IReadOnlyList<CepstralWordObservation> observations,
+    MimirBioacousticTimeline timeline,
+    int sampleRate)
+{
+    var anchors = observations
+        .Select(observation => new BioacousticClockAnchor(
+            observation.EventIndex,
+            timeline.EventForIndex(observation.EventIndex).StartSeconds,
+            observation.SampleOffset,
+            Math.Clamp(observation.Confidence, 0.001, 1.0)))
+        .OrderByDescending(anchor => anchor.Confidence)
+        .Take(24)
+        .ToArray();
+    if (anchors.Length == 0)
+    {
+        return null;
+    }
+
+    var candidates = new List<BioacousticClockHypothesis>();
+    foreach (var anchor in anchors)
+    {
+        var offset = anchor.SampleOffset - anchor.TimelineSeconds * sampleRate;
+        AddClockCandidate(candidates, anchors, sampleRate, offset, sampleRate);
+    }
+
+    if (anchors.Length >= 3)
+    {
+        for (var first = 0; first < anchors.Length; first++)
+        {
+            for (var second = first + 1; second < anchors.Length; second++)
+            {
+                var dt = anchors[second].TimelineSeconds - anchors[first].TimelineSeconds;
+                if (Math.Abs(dt) < 0.08)
+                {
+                    continue;
+                }
+
+                var rate = (anchors[second].SampleOffset - anchors[first].SampleOffset) / dt;
+                if (!double.IsFinite(rate) || rate < sampleRate * 0.98 || rate > sampleRate * 1.02)
+                {
+                    continue;
+                }
+
+                var offset = anchors[first].SampleOffset - anchors[first].TimelineSeconds * rate;
+                AddClockCandidate(candidates, anchors, sampleRate, offset, rate);
+            }
+        }
+    }
+
+    return candidates
+        .OrderByDescending(candidate => candidate.Score)
+        .ThenBy(candidate => candidate.MeanAbsoluteErrorSamples)
+        .FirstOrDefault();
+}
+
+static void AddClockCandidate(
+    List<BioacousticClockHypothesis> candidates,
+    IReadOnlyList<BioacousticClockAnchor> anchors,
+    int nominalSampleRate,
+    double sourceOffsetSamples,
+    double effectiveSampleRate)
+{
+    var tolerance = Math.Max(36.0, nominalSampleRate * 0.012);
+    var inliers = anchors
+        .Where(anchor => Math.Abs(anchor.SampleOffset - (sourceOffsetSamples + anchor.TimelineSeconds * effectiveSampleRate)) <= tolerance)
+        .ToArray();
+    if (inliers.Length == 0)
+    {
+        return;
+    }
+
+    var refined = FitBioacousticClockToInliers(inliers, nominalSampleRate);
+    if (refined != null)
+    {
+        candidates.Add(refined);
+    }
+}
+
+static BioacousticClockHypothesis? FitBioacousticClockToInliers(
+    IReadOnlyList<BioacousticClockAnchor> anchors,
+    int nominalSampleRate)
+{
+    if (anchors.Count == 0)
+    {
+        return null;
+    }
+
+    if (anchors.Count == 1)
+    {
+        var anchor = anchors[0];
+        var offset = anchor.SampleOffset - anchor.TimelineSeconds * nominalSampleRate;
+        var singleAnchorConfidence = anchor.Confidence * 0.20;
+        return new BioacousticClockHypothesis(
+            offset,
+            nominalSampleRate,
+            1,
+            0.0,
+            singleAnchorConfidence,
+            singleAnchorConfidence,
+            anchors.ToArray());
+    }
+
+    var totalWeight = anchors.Sum(anchor => Math.Max(1.0e-6, anchor.Confidence));
+    var meanTimeline = anchors.Sum(anchor => anchor.TimelineSeconds * Math.Max(1.0e-6, anchor.Confidence)) / totalWeight;
+    var meanSample = anchors.Sum(anchor => anchor.SampleOffset * Math.Max(1.0e-6, anchor.Confidence)) / totalWeight;
+    var covariance = 0.0;
+    var variance = 0.0;
+    foreach (var anchor in anchors)
+    {
+        var weight = Math.Max(1.0e-6, anchor.Confidence);
+        var dt = anchor.TimelineSeconds - meanTimeline;
+        covariance += weight * dt * (anchor.SampleOffset - meanSample);
+        variance += weight * dt * dt;
+    }
+
+    var effectiveSampleRate = anchors.Count >= 3 && variance > 1.0e-12 ? covariance / variance : nominalSampleRate;
+    if (!double.IsFinite(effectiveSampleRate) ||
+        effectiveSampleRate < nominalSampleRate * 0.98 ||
+        effectiveSampleRate > nominalSampleRate * 1.02)
+    {
+        effectiveSampleRate = nominalSampleRate;
+    }
+
+    var sourceOffset = meanSample - effectiveSampleRate * meanTimeline;
+    var weightedResidual = anchors.Sum(anchor =>
+    {
+        var predicted = sourceOffset + anchor.TimelineSeconds * effectiveSampleRate;
+        return Math.Abs(anchor.SampleOffset - predicted) * Math.Max(1.0e-6, anchor.Confidence);
+    }) / totalWeight;
+    var residualConfidence = 1.0 / (1.0 + weightedResidual / Math.Max(1.0, nominalSampleRate * 0.001));
+    var countConfidence = Math.Clamp(anchors.Count / 5.0, 0.0, 1.0);
+    var anchorConfidence = Math.Clamp(anchors.Average(anchor => anchor.Confidence), 0.0, 1.0);
+    var confidence = residualConfidence * 0.35 + countConfidence * 0.45 + anchorConfidence * 0.20;
+    var score = confidence + Math.Min(anchors.Count, 8) * 0.15 - weightedResidual / Math.Max(1.0, nominalSampleRate * 0.010);
+    return new BioacousticClockHypothesis(
+        sourceOffset,
+        effectiveSampleRate,
+        anchors.Count,
+        weightedResidual,
+        confidence,
+        score,
+        anchors.ToArray());
+}
+
 static double CepstralDistance(IReadOnlyList<double> first, IReadOnlyList<double> second)
 {
     var sum = 0.0;
@@ -2180,6 +2405,21 @@ internal sealed record CepstralWordObservation(
     double SampleOffset,
     double Confidence);
 
+internal sealed record BioacousticClockAnchor(
+    ulong EventIndex,
+    double TimelineSeconds,
+    double SampleOffset,
+    double Confidence);
+
+internal sealed record BioacousticClockHypothesis(
+    double SourceOffsetSamples,
+    double EffectiveSampleRate,
+    int AnchorCount,
+    double MeanAbsoluteErrorSamples,
+    double Confidence,
+    double Score,
+    IReadOnlyList<BioacousticClockAnchor> Anchors);
+
 internal sealed record CepstralWordIndex(
     IReadOnlyList<CepstralWordTemplate> Templates,
     IReadOnlyDictionary<(int Table, int Hash), List<int>> Buckets);
@@ -2215,9 +2455,20 @@ public sealed record BioacousticTrainingResult(
     [property: Key(23)] int RoundTripCepstralCoefficients,
     [property: Key(24)] int RoundTripFrameCount,
     [property: Key(25)] double RoundTripRmsRatio,
-    [property: Key(26)] BioacousticTrainingDecoderSnapshot Decoder,
-    [property: Key(27)] BioacousticTrainingArtifact[] Artifacts,
-    [property: Key(28)] BioacousticTrainingObservation[] Observations);
+    [property: Key(26)] BioacousticClockHypothesisSnapshot ClockHypothesis,
+    [property: Key(27)] BioacousticTrainingDecoderSnapshot Decoder,
+    [property: Key(28)] BioacousticTrainingArtifact[] Artifacts,
+    [property: Key(29)] BioacousticTrainingObservation[] Observations);
+
+[MessagePackObject]
+public sealed record BioacousticClockHypothesisSnapshot(
+    [property: Key(0)] int AnchorCount,
+    [property: Key(1)] double SourceOffsetSamples,
+    [property: Key(2)] double EffectiveSampleRate,
+    [property: Key(3)] double MeanAbsoluteErrorSamples,
+    [property: Key(4)] double Confidence,
+    [property: Key(5)] double Score,
+    [property: Key(6)] double AnchorCoverage);
 
 [MessagePackObject]
 public sealed record BioacousticTrainingDecoderSnapshot(

state/evidence.jsonl

@@ -84,3 +84,5 @@
 {"ts": "2026-05-23T23:45:11+01:00", "type": "decision", "status": "ok", "note": "Added Perfect Machine domain index plus 2026-05-23 architecture/optimization/reference study and sample implementation sketches for decoder, SIMD/GPU scoring, actuator, and native rings."}
 {"ts": "2026-05-24T01:55:37+01:00", "type": "decision", "status": "ok", "note": "Completed corrected two-hour Perfect Machine study pass from 2026-05-23T23:54:42.9041588+01:00 through 2026-05-24T01:54:56.9747630+01:00; research/perfect-machine-study-2026-05-23 now indexes decoder architecture, acoustic field models, calibration sessions, Fensalir/native boundaries, distributed receivers, benchmarks, failure modes, low-level notes, and sample implementation sketches."}
 {"ts": "2026-05-24T11:38:08+01:00", "type": "implementation", "status": "ok", "note": "Added bioacoustic indexed-cepstral training harness with CultLib/CultCache typed result receipts and WAV artifact directories. Latest run artifacts/bioacoustic-training/bioacoustic-20260524-103438 scored 20 hypothesis/degradation cases; identity often survives mel-cepstral warp/blur, but timing MAE under warp points next at a global clock/path hypothesis rather than larger brute-force matching."}
+{"ts": "2026-05-24T16:06:07+01:00", "type": "implementation", "status": "ok", "note": "Added global bioacoustic clock hypothesis scoring to the training harness and the first Faust-owned alignment actuator surface. Corrected receipt artifacts/bioacoustic-training/bioacoustic-20260524-150000 favors baseline-mfcc-index/blur-light with total 0.766 after penalizing low-anchor timing fits. bioacoustic-actuator-self-test estimates 317.375 samples, applies fractional correction, and remeasures 0.000 us residual; faust/mimir_alignment_actuator.dsp validates through Faust C++ generation."}
+{"ts": "2026-05-24T16:09:30+01:00", "type": "implementation", "status": "ok", "note": "Extended the corrected bioacoustic training run to 0.75s in artifacts/bioacoustic-training/bioacoustic-20260524-150729. Compact indexing wins clean throughput at total 0.752/realtime 6.6x, baseline remains steadier under degradation, highband has warped-path value, and anchor density/time-to-lock should become explicit metrics."}

state/map.yaml

@@ -128,11 +128,13 @@ accepted_design:
     confirmation. `Mimir.BufferSmoke --bioacoustic-train` now runs a
     receipt-backed hypothesis panel over indexed cepstral decoder shapes and
     writes typed CultCache results plus pre-warp/post-warp/reconstructed WAV
-    artifacts under `artifacts/bioacoustic-training/`. The latest receipt
-    `bioacoustic-20260524-103438` shows compact indexing is fastest on clean
-    audio, baseline indexing is more stable under light warp, highband indexing
-    can win blur/warp-blur cases, and timing error under domain warp remains
-    the pressure for a global clock/path hypothesis. `MimirChirpBinTimeline` remains the controlled chirp-bin
+    artifacts under `artifacts/bioacoustic-training/`. The first receipt
+    `bioacoustic-20260524-103438` showed compact indexing fastest on clean
+    audio and exposed timing error under domain warp. The corrected receipt
+    `bioacoustic-20260524-150729` adds global clock hypothesis scoring over a
+    longer window; compact indexing wins clean throughput, baseline indexing
+    remains steadier under degradation, highband indexing has warped-path value,
+    and anchor density needs its own metric. `MimirChirpBinTimeline` remains the controlled chirp-bin
     calibration/reference artifact with ASIO tooling and
     `MimirChirpBinCalibrationModel`; keep those captures as response evidence,
     not the active runtime sound. Reports and sync states expose delay in
@@ -146,7 +148,12 @@ accepted_design:
     correctly accepts strong passive evidence at 2.772 us error. The synthetic
     standalone receiver proof `--standalone-bioacoustic-self-test --sample-rate
     48000 --delay-samples 1269.5` recovers delayed source time below printed
-    microsecond precision using only the codebook/schedule. The synthetic
+    microsecond precision using only the codebook/schedule. `--bioacoustic-actuator-self-test
+    --sample-rate 48000 --delay-samples 317.375` now proves the first
+    measurement-to-sample-movement loop: estimate bioacoustic delay, apply
+    fractional correction, and remeasure residual below printed microsecond
+    precision. `faust/mimir_alignment_actuator.dsp` is the first Faust-owned
+    actuator surface for six channels of bounded fractional delay/gain. The synthetic
     codebook proof is `dotnet run --project
     .\src\Mimir.BufferSmoke\Mimir.BufferSmoke.csproj -- --bioacoustic-self-test`.
     `Mimir.BufferSmoke --render-chirp-bin-f32` plus