From 55810f5050c14e6442ff9d95461b2a94bdfc0ebf Mon Sep 17 00:00:00 2001
From: DVIDIA <hello@dvidia.org>
Date: Mon, 9 Nov 2026 10:47:22 -0500
Subject: [PATCH 1/3] Add reference-endpoint model training and simulation
 policy bridge

---
 docs/camera-action-reference-model-v01.md     | 144 ++++++++
 .../camera_action_reference_model.py          | 340 ++++++++++++++++++
 tests/test_camera_action_reference_model.py   | 262 ++++++++++++++
 3 files changed, 746 insertions(+)
 create mode 100644 docs/camera-action-reference-model-v01.md
 create mode 100644 src/dvidia_training/camera_action_reference_model.py
 create mode 100644 tests/test_camera_action_reference_model.py

diff --git a/docs/camera-action-reference-model-v01.md b/docs/camera-action-reference-model-v01.md
new file mode 100644
index 0000000..8ce3f85
--- /dev/null
+++ b/docs/camera-action-reference-model-v01.md
@@ -0,0 +1,144 @@
+# Reference-endpoint model bridge
+
+This additive Python module connects the verified training export to a trainable
+model and a callable local simulation policy. It implements the proposed Cabs
+and Cdelta comparison; **no real-study model has been fitted or evaluated with
+it**. The frozen v0.1 result remains 5/64 strict learned placements and
+unqualified. The common-scale parameterization and the 164-attempt v0.2 screen
+still need registration before real learning or rollouts.
+
+The module is `dvidia_training.camera_action_reference_model`. It changes none
+of the existing recorder, trainer, model, installer, actuator limits or scorer.
+It provides no hardware commands, new CLI command or installation qualification.
+Its policy has the same callable interface that the unchanged
+`camera_action_capture.run_rollout` already accepts. That interface compatibility
+is tested with synthetic contexts; no native rollout was run for this bridge.
+
+## From sealed training inputs to actions
+
+`train(sources, variant='absolute' | 'delta')` accepts one independently pinned
+demonstration reference export, optionally followed by the correction export.
+Each source includes its reference directory, parent cache and all independently
+retained manifest, protocol, stage, split and source pins. The existing reference
+loader verifies both derived artifacts. Development and confirmation data are
+refused; the API has no evaluation-data argument and does not accept substituted
+unverified feature/label arrays. Both stages must share a protocol. Duplicate
+recordings are rejected by the existing parent-data combiner.
+The combined 128,000-row cap is enforced before concatenation, normalization
+or parameter initialization, including when each input fits the cap separately.
+
+Training uses the original causal 269-dimensional full-feedback features. It
+keeps their missing-value masks, historical initial visual anchor and 160 ms
+history. Exact previous normal servo references and feasible endpoints are
+separate float64 arrays. Each training episode must start at the source-bound
+reset reference. Issued teacher targets and mixed-control teacher suggestions
+retain their separate parent provenance; a suggestion is not a claim of actual
+executed control. No original frames, native traces or query bodies are read by
+this module. Raw-data validation is inherited from the pinned parent cache.
+
+The fixed recipe is seed 17, two 64-unit tanh hidden layers, 50 epochs, batch size
+512 and Adam learning rate 0.003. Both variants initialize every parameter
+identically and use the same example shuffles, float32 weights and Adam moments,
+gradient clipping and parameter bounds. Feature mean and standard deviation
+come only from the loaded training set. Training and policy normalization both
+use float64 arithmetic and then cast the normalized features to float32; saving
+and loading preserves this arithmetic exactly.
+
+For network output `g(x)`, the two decoders are:
+
+```text
+r0 = [0, 1.57, -1.3485, 0, 0, 0, 0.035]
+D  = [0.03 rad × 6, 0.001 single-finger m]
+S  = [3 rad × 6, 0.035 single-finger m]
+Cabs endpoint   = r0 + D * g(x)
+Cdelta endpoint = r  + D * g(x)
+loss = mean(((unprojected_endpoint - feasible_target) / S)²)
+```
+
+The decoder, loss and backpropagation chain rule use float64. The final network
+output remains float32. Training differentiates the **unprojected** endpoint
+loss; it does not silently optimize a saturated/clipped output. At execution,
+both variants clip to the same original native joint/jaw bounds and apply the
+same exact 20-update normal-reference projection. The existing native runtime
+then retains its own rate, force and task checks. This bounds reference changes;
+it does not guarantee contact safety or bound actual physical joint motion.
+
+At initialization, for the same reset inputs and targets, both variants have
+the same decoded outputs, complete-network gradients and first Adam update.
+Later references intentionally change the
+decoder offset and can change losses, gradients and actions. This is a controlled
+parameterization, not evidence that one representation learns better.
+
+## Exact state and a separate model format
+
+`ModelPolicy` reads `context['previous_applied']` directly as double precision;
+it never reconstructs that state from normalized float32 features. The first
+reference must equal the original reset. Reduced-precision NumPy arrays are
+refused. The existing feature encoder enforces a fresh initial visual estimate,
+20 ms increasing observation times, fixed destination and causal camera/signal
+timestamps. Extra teacher labels, future snapshots and native object/contact
+truth do not enter model features or decoding.
+
+The reference's authority comes from the recorder's existing field and its
+source contract. The callable interface cannot authenticate an arbitrary caller
+that deliberately supplies rounded or false values as ordinary Python floats.
+Tests demonstrate that two exact references sharing the same float32 feature
+representation still produce distinct delta outputs. They do not pretend a
+number alone proves where it came from.
+
+The strict model schema is `dvidia.camera-action-reference-mlp/v1`. It stores the
+variant, complete contract and recipe, source identity, normalization, exact
+float32 parameters and training provenance. The latter binds parent/reference
+datasets, independent input pins, groups, sample counts, initialization and the
+fixed optimizer budget. Save uses exclusive creation; load rejects changed
+fields, sources, units, bounds, order, recipe or float32 parameter encoding.
+Models remain `physical_robot_ready=false`, `task_qualified=false`, with
+`scientific_registration=null`. A model file is not an experiment registration
+or a qualification receipt, and these hashes are not publisher authentication.
+
+For a future separately registered experiment, the API is:
+
+```python
+from dvidia_training import camera_action_reference_model as reference_model
+
+# sources: independently reviewed list of {directory, parent_cache, pins}.
+# Each pins object is the argument set for load_reference_export, including
+# expected_manifest_sha256; never replace independent pins with file contents.
+candidate = reference_model.train(sources, variant="delta")
+reference_model.save_model(candidate, "/absolute/new/model.json")
+policy = reference_model.ModelPolicy(
+    reference_model.load_model("/absolute/new/model.json")
+)
+# The unchanged recorder can invoke policy(context). Running a new simulation
+# is a separate registered experiment, not part of loading this model.
+```
+
+## Verification and remaining gate
+
+Run the focused synthetic suite locally:
+
+```sh
+python -B -m unittest tests.test_camera_action_reference_model -v
+```
+
+Each suite invocation performs exactly **two synthetic 12-row fits**, one per
+variant, at the complete 50-epoch recipe. It also checks complete-network
+gradient and single-update reset parity, finite-difference derivatives across
+all six parameter tensors, legitimate later-state differences, exact shared
+normalization, save/load policy parity, missing live observations, causal-input
+refusal, independent-pin refusal, label exclusion and native-reference
+projection against the original two numerical limiter statements. Those
+statements are evaluated as arithmetic only; no physics engine executes.
+
+The full-chain derivative check uses a smooth float64 finite-difference oracle.
+Production float32 forward/parameter/moment behavior is checked separately by
+bitwise cross-variant reset parity and saved-model inference parity. These are
+implementation tests, not task benchmarks. Synthetic fit losses are not
+evidence of manipulation progress.
+
+Before the real v0.2 screen: register this parameterization and source, verify
+shared initialization on the registered reset contexts, bind the exact dataset
+and resource budgets, and then run the declared fits and fresh closed-loop
+attempts with independent scoring. Existing confirmation data must remain
+untouched. The installer needs a separate explicit extension before this new
+model format can be offered as an installable simulation candidate.
diff --git a/src/dvidia_training/camera_action_reference_model.py b/src/dvidia_training/camera_action_reference_model.py
new file mode 100644
index 0000000..a62e547
--- /dev/null
+++ b/src/dvidia_training/camera_action_reference_model.py
@@ -0,0 +1,340 @@
+"""Unregistered reference-endpoint learning bridge; no hardware/skill qualification.
+
+Only independently pinned, train-only reference exports are training inputs.
+The callable policy fits the unchanged camera/action recorder's four-key context.
+Exact prior servo references are decoder state, never reconstructed from features.
+"""
+from __future__ import annotations
+
+import ast
+from copy import deepcopy
+from hashlib import sha256
+import math
+from pathlib import Path
+import time
+
+import numpy as np
+
+from . import camera_action_model as base
+from . import camera_action_export as export
+from . import camera_action_reference_export as ref
+
+SCHEMA = 'dvidia.camera-action-reference-mlp/v1'
+SCOPE = 'Unregistered simulation-only reference-endpoint research candidate; no skill or hardware qualification.'
+VARIANTS = ('absolute', 'delta')
+R0 = [0., 1.57, -1.3485, 0., 0., 0., .035]
+D = np.asarray([.03]*6+[.001], dtype=np.float64)
+S = np.asarray([3.]*6+[.035], dtype=np.float64)
+CONTRACT = dict(feature_contract=base.FEATURE_CONTRACT,
+    feature_contract_sha256=base.FEATURE_CONTRACT_SHA256,
+    reference_kind='previous_applied_normal_servo_reference', reset_reference=R0,
+    joint_order=[f'joint{i}' for i in range(1, 7)]+['single_finger_jaw'],
+    action_units=['rad']*6+['single_finger_m'], increment_scale=D.tolist(), loss_scale=S.tolist(),
+    absolute_decoder='r0 + D*g(x)', delta_decoder='r + D*g(x)',
+    loss='mean(((unprojected_endpoint-feasible_target)/S)**2)',
+    projection='native bounds then exact original 20 normal reference updates',
+    network_dtype='float32', decoder_loss_and_gradients_dtype='float64', adam_moments_dtype='float32',
+    history_seconds=.16, control_period_s=.02, hidden_units=[64, 64], blind=False)
+RECIPE = dict(seed=17, epochs=50, batch_size=512, learning_rate=.003,
+    beta1=.9, beta2=.999, epsilon=1e-8, gradient_clip=1., parameter_clip=base.core.MAX_WEIGHT)
+MODEL_FIELDS = {'schema', 'scope', 'variant', 'physical_robot_ready', 'task_qualified',
+    'scientific_registration', 'contract', 'recipe', 'source_identity', 'feature_mean',
+    'feature_scale', 'weights', 'biases', 'training'}
+TRAINING_FIELDS = {'algorithm', 'samples', 'groups', 'parent_dataset_sha256',
+    'parent_provenance', 'parent_provenance_sha256', 'reference_dataset_sha256',
+    'reference_inputs', 'initial_parameters_sha256', 'initial_loss', 'final_loss',
+    'optimizer_updates', 'normalization'}
+PIN_FIELDS = {'expected_manifest_sha256', 'expected_parent_manifest_sha256',
+    'expected_parent_source_sha256', 'expected_protocol_sha256', 'expected_stage',
+    'expected_split', 'expected_source_sha256'}
+
+
+def source_identity():
+    """Fail closed on the frozen parent, limiter or reset-source contract."""
+    identity = ref.source_identity()
+    tree = ast.parse(Path(base.__file__).with_name('piper_task.py').read_bytes())
+    cls = next(n for n in tree.body if isinstance(n, ast.ClassDef) and n.name == 'PiperTaskEnv')
+    reset = next(n for n in cls.body if isinstance(n, ast.FunctionDef) and n.name == 'reset')
+    assignments = {ast.unparse(n.targets[0]): n.value for n in ast.walk(reset)
+        if isinstance(n, ast.Assign) and len(n.targets) == 1}
+    initial = ast.literal_eval(assignments['self.data.qpos[:8]'])[:6]
+    initial += [ast.literal_eval(assignments['self.command_gripper'])]
+    if initial != R0 or ast.unparse(assignments['self.command_targets']) != 'self.data.qpos[:6].copy()':
+        raise ValueError('The original constant reset-reference assignment changed.')
+    return dict(reference_export=identity, reset_reference=initial,
+        model_source_sha256=sha256(Path(__file__).read_bytes()).hexdigest())
+
+
+def _variant(variant):
+    if type(variant) is not str or variant not in VARIANTS:
+        raise ValueError('Choose absolute or delta reference-endpoint decoding.')
+
+
+def _input_pins(value):
+    if type(value) is not dict or set(value) != PIN_FIELDS:
+        raise ValueError('Require all independent reference and parent pins.')
+    if value['expected_split'] != 'train' or value['expected_stage'] not in ('demonstrations', 'corrections'):
+        raise ValueError('Only sealed training reference exports may enter learning.')
+    for field in PIN_FIELDS-{'expected_split', 'expected_stage'}:
+        base._hash(value[field], field)
+    return deepcopy(value)
+
+
+def _load_training(sources):
+    """Load/check derived bytes only; do not open source recordings or query bodies."""
+    if type(sources) not in (list, tuple) or not 1 <= len(sources) <= 2:
+        raise ValueError('Use demonstrations, optionally followed by corrections.')
+    datasets, previous, endpoints, inputs = [], [], [], []
+    combined_rows = 0
+    for index, source in enumerate(sources):
+        if type(source) is not dict or set(source) != {'directory', 'parent_cache', 'pins'}:
+            raise ValueError('Reference inputs require directories and independent pins.')
+        pins = _input_pins(source['pins'])
+        if pins['expected_stage'] != ('demonstrations', 'corrections')[index]:
+            raise ValueError('Preserve demonstration then correction stage order.')
+        if inputs and pins['expected_protocol_sha256'] != inputs[0]['pins']['expected_protocol_sha256']:
+            raise ValueError('Training stages require the same registered parent protocol.')
+        loaded = ref.load_reference_export(source['directory'], source['parent_cache'], **pins)
+        combined_rows += len(loaded['previous_references'])
+        if not 1 <= combined_rows <= ref.MAX_SAMPLES:
+            raise ValueError('Combined training rows exceed the reference-model sample budget.')
+        offset = 0
+        for episode in loaded['parent_dataset']['provenance']['episodes']:
+            if not np.array_equal(loaded['previous_references'][offset], np.asarray(R0)):
+                raise ValueError('Every training episode must retain the source-bound reset reference.')
+            offset += episode['sample_count']
+        if offset != len(loaded['previous_references']):
+            raise ValueError('Episode offsets must cover every exact reference.')
+        datasets.append(loaded['parent_dataset'])
+        previous.append(loaded['previous_references']); endpoints.append(loaded['feasible_targets'])
+        inputs.append(dict(pins=pins, samples=len(previous[-1])))
+    parent = export.combine_datasets(datasets)
+    x, _, groups, provenance, parent_hash = base._dataset(parent, blind=False)
+    r = np.ascontiguousarray(np.concatenate(previous), dtype='<f8')
+    y = np.ascontiguousarray(np.concatenate(endpoints), dtype='<f8')
+    ref._matrix(r, 'Previous references'); ref._matrix(y, 'Feasible targets')
+    if len(x) != len(r) or len(y) != len(r):
+        raise ValueError('Parent features and exact-reference labels must stay row-aligned.')
+    identity = dict(parent_dataset_sha256=parent_hash, inputs=inputs,
+        previous_sha256=sha256(r.tobytes()).hexdigest(), endpoint_sha256=sha256(y.tobytes()).hexdigest())
+    return x, r, y, groups, provenance, parent_hash, base.digest(identity), inputs
+
+
+def _parameters():
+    rng = np.random.default_rng(RECIPE['seed'])
+    dims = (base.FEATURE_COUNT, 64, 64, 7)
+    weights = [rng.normal(0., math.sqrt(2./(a+b)), (a, b)).astype(np.float32)
+        for a, b in zip(dims[:-1], dims[1:])]
+    biases = [np.zeros(b, dtype=np.float32) for b in dims[1:]]
+    return weights, biases, rng
+
+
+def decode(head, previous, variant):
+    """Unprojected float64 endpoints. Both heads have the same Jacobian D."""
+    _variant(variant)
+    r = ref._matrix(previous, 'Exact previous references')
+    g = base.core._array(head, (len(r), 7), 'Network head', 1e6)
+    return (np.asarray(R0, dtype=np.float64) if variant == 'absolute' else r) + D*g
+
+
+def project(endpoint, previous):
+    """Identical native-bound and 20-tick reference projection for both variants."""
+    r = ref._matrix(previous, 'Exact previous references')
+    target = base.core._array(endpoint, (len(r), 7), 'Decoded endpoint', 1e6)
+    bounds = np.asarray(ref.CONTRACT['limits_native'], dtype=np.float64)
+    return ref.feasible_targets(r, np.ascontiguousarray(np.clip(target, bounds[:, 0], bounds[:, 1])))
+
+
+def _loss_gradients(x, previous, target, weights, biases, variant):
+    """Full-network gradients of the unprojected physical-scale endpoint loss."""
+    a, b, head = base._forward(x, weights, biases)
+    prediction = decode(head, previous, variant)
+    error = prediction-target
+    loss = float(np.mean((error/S)**2))
+    output_grad = 2.*error*D/(len(x)*7.*S*S)
+    b_grad = (output_grad@weights[2].T)*(1.-b.astype(np.float64)**2)
+    a_grad = (b_grad@weights[1].T)*(1.-a.astype(np.float64)**2)
+    gradients = [x.astype(np.float64).T@a_grad, a.astype(np.float64).T@b_grad,
+        b.astype(np.float64).T@output_grad, a_grad.sum(axis=0), b_grad.sum(axis=0), output_grad.sum(axis=0)]
+    return loss, gradients
+
+
+def _adam_update(parameters, gradients, first, second, step):
+    # Keep the original float32 parameter/moment storage and Adam constants;
+    # the endpoint chain rule/backprop above is explicitly float64.
+    for p, gradient, m, v in zip(parameters, gradients, first, second):
+        g = np.clip(gradient, -RECIPE['gradient_clip'], RECIPE['gradient_clip'])
+        m *= .9; m += .1*g
+        v *= .999; v += .001*g*g
+        p -= RECIPE['learning_rate']*(m/(1.-.9**step))/(np.sqrt(v/(1.-.999**step))+1e-8)
+        np.clip(p, -base.core.MAX_WEIGHT, base.core.MAX_WEIGHT, out=p)
+
+
+def _loss(x, previous, target, weights, biases, variant):
+    prediction = decode(base._forward(x, weights, biases)[2], previous, variant)
+    return float(np.mean(((prediction-target)/S)**2))
+
+
+def train(sources, *, variant):
+    """Fit the fixed recipe to validated training exports; no evaluation-data API.
+
+    This implementation is unregistered research. Calling it neither runs a
+    simulator nor qualifies a skill. Real experiments need separate registration.
+    """
+    _variant(variant)
+    identity = source_identity()
+    x, r, y, groups, provenance, parent_hash, data_hash, inputs = _load_training(sources)
+    mean, scale = x.mean(axis=0), np.maximum(x.std(axis=0), 1e-4)
+    x = np.clip((x-mean)/scale, -10., 10.).astype(np.float32)
+    weights, biases, rng = _parameters()
+    initial_parameters = base.digest(dict(weights=[w.tolist() for w in weights], biases=[b.tolist() for b in biases]))
+    parameters = weights+biases
+    first, second = ([np.zeros_like(p) for p in parameters] for _ in range(2))
+    initial_loss = _loss(x, r, y, weights, biases, variant)
+    step = 0
+    for _ in range(RECIPE['epochs']):
+        order = rng.permutation(len(x))
+        for start in range(0, len(x), RECIPE['batch_size']):
+            batch = order[start:start+RECIPE['batch_size']]
+            _, gradients = _loss_gradients(x[batch], r[batch], y[batch], weights, biases, variant)
+            step += 1; _adam_update(parameters, gradients, first, second, step)
+    final_loss = _loss(x, r, y, weights, biases, variant)
+    if source_identity() != identity:
+        raise ValueError('Source contract changed while fitting.')
+    document = dict(schema=SCHEMA, scope=SCOPE, variant=variant, physical_robot_ready=False,
+        task_qualified=False, scientific_registration=None, contract=deepcopy(CONTRACT), recipe=deepcopy(RECIPE),
+        source_identity=identity, feature_mean=mean.tolist(), feature_scale=scale.tolist(),
+        weights=[w.tolist() for w in weights], biases=[b.tolist() for b in biases],
+        training=dict(algorithm='numpy-tanh-2x64-adam-native-endpoint-mse/v1', samples=len(x),
+            groups=list(dict.fromkeys(groups)), parent_dataset_sha256=parent_hash,
+            parent_provenance=provenance, parent_provenance_sha256=base.digest(provenance),
+            reference_dataset_sha256=data_hash, reference_inputs=inputs,
+            initial_parameters_sha256=initial_parameters, initial_loss=initial_loss, final_loss=final_loss,
+            optimizer_updates=step, normalization='training features only; no label whitening'))
+    return validate_model(document)
+
+
+def validate_model(document):
+    value = base.core._decode(document)
+    if type(value) is not dict or set(value) != MODEL_FIELDS or value['schema'] != SCHEMA:
+        raise ValueError('Unsupported reference-model schema or fields.')
+    _variant(value['variant'])
+    if (value['scope'] != SCOPE or value['physical_robot_ready'] is not False
+            or value['task_qualified'] is not False or value['scientific_registration'] is not None
+            or base.canonical(value['contract']) != base.canonical(CONTRACT)
+            or base.canonical(value['recipe']) != base.canonical(RECIPE)
+            or value['source_identity'] != source_identity()):
+        raise ValueError('Reference-model contract, source, recipe or research scope changed.')
+    for name in ('feature_mean', 'feature_scale'):
+        array = base.core._array(value[name], (base.FEATURE_COUNT,), name, 20.)
+        if name == 'feature_scale' and np.any(array < 1e-4):
+            raise ValueError('Feature scales must retain the training-only floor.')
+    dims = (base.FEATURE_COUNT, 64, 64, 7)
+    if (type(value['weights']) is not list or type(value['biases']) is not list
+            or len(value['weights']) != 3 or len(value['biases']) != 3):
+        raise ValueError('Exactly three affine layers are required.')
+    for i, (a, b) in enumerate(zip(dims[:-1], dims[1:])):
+        for name, shape in (('weights', (a, b)), ('biases', (b,))):
+            p = base.core._array(value[name][i], shape, name, base.core.MAX_WEIGHT)
+            if not np.array_equal(p, p.astype(np.float32).astype(np.float64)):
+                raise ValueError('Serialized network parameters must preserve exact float32 values.')
+    training = value['training']
+    if type(training) is not dict or set(training) != TRAINING_FIELDS:
+        raise ValueError('Exact reference-training provenance is required.')
+    if (training['algorithm'] != 'numpy-tanh-2x64-adam-native-endpoint-mse/v1'
+            or training['normalization'] != 'training features only; no label whitening'):
+        raise ValueError('Unknown endpoint learning or normalization recipe.')
+    count = base.core._integer(training['samples'], 'training rows', 1, ref.MAX_SAMPLES)
+    base.core._case_ids(training['groups'])
+    if len(training['groups']) > count:
+        raise ValueError('Training groups cannot exceed samples.')
+    for name in ('parent_dataset_sha256', 'parent_provenance_sha256', 'reference_dataset_sha256', 'initial_parameters_sha256'):
+        base._hash(training[name], name)
+    if (type(training['parent_provenance']) is not dict or not training['parent_provenance']
+            or base.digest(training['parent_provenance']) != training['parent_provenance_sha256']):
+        raise ValueError('Parent provenance identity changed.')
+    items = training['reference_inputs']
+    if type(items) is not list or not 1 <= len(items) <= 2:
+        raise ValueError('Exact reference-input provenance is required.')
+    total = 0
+    for index, item in enumerate(items):
+        if type(item) is not dict or set(item) != {'pins', 'samples'}:
+            raise ValueError('Unknown reference-input fields.')
+        pins = _input_pins(item['pins'])
+        if (pins['expected_stage'] != ('demonstrations', 'corrections')[index]
+                or pins['expected_protocol_sha256'] != items[0]['pins']['expected_protocol_sha256']
+                or pins['expected_source_sha256'] != ref.source_digest()
+                or pins['expected_parent_source_sha256'] != ref.cache.source_digest()):
+            raise ValueError('Training input source, protocol or stage changed.')
+        total += base.core._integer(item['samples'], 'input samples', 1, ref.MAX_SAMPLES)
+    if total != count or type(training['optimizer_updates']) is not int or training['optimizer_updates'] != 50*math.ceil(count/512):
+        raise ValueError('Training sample count or fixed optimizer budget changed.')
+    for name in ('initial_loss', 'final_loss'):
+        base.core._number(training[name], name, 0., 1e12)
+    return value
+
+
+def save_model(document, path):
+    value = validate_model(document)
+    with Path(path).open('xb') as stream:
+        stream.write(base.canonical(value)+b'\n')
+    return base.digest(value)
+
+
+def load_model(path):
+    with Path(path).open('rb') as stream:
+        return validate_model(stream.read(base.core.MAX_MODEL_BYTES+1))
+
+
+def _previous(context):
+    """Trust the recorder's explicit field; reject already reduced-precision arrays.
+
+    Python floats preserve JSON doubles but cannot authenticate their provenance.
+    No arbitrary caller can prove reference authority just by naming this field.
+    """
+    value = context.get('previous_applied')
+    if type(value) is not dict or set(value) != {'joint_targets_rad', 'gripper_position_m'}:
+        raise ValueError('Require the recorder\'s exact previous_applied reference.')
+    joints = value['joint_targets_rad']; jaw = value['gripper_position_m']
+    if isinstance(joints, np.ndarray):
+        if joints.dtype != np.dtype('float64'):
+            raise ValueError('Previous references must not be reduced-precision arrays.')
+    elif type(joints) not in (list, tuple) or any(type(x) not in (float, int, np.float64) for x in joints):
+        raise ValueError('Previous references require ordinary double-precision coordinates.')
+    if type(jaw) not in (float, int, np.float64):
+        raise ValueError('The exact single-finger reference is required.')
+    return np.ascontiguousarray(base.action_vector(value)[None, :], dtype='<f8')
+
+
+class ModelPolicy:
+    """Fresh per episode; callable by unchanged camera_action_capture.run_rollout."""
+    def __init__(self, document):
+        self.model = validate_model(document)
+        self.encoder = base.CameraActionFeatures(blind=False)
+        self.mean = np.asarray(self.model['feature_mean'], dtype=np.float64)
+        self.scale = np.asarray(self.model['feature_scale'], dtype=np.float64)
+        self.weights = [np.asarray(w, dtype=np.float32) for w in self.model['weights']]
+        self.biases = [np.asarray(b, dtype=np.float32) for b in self.model['biases']]
+        self.inference_seconds = []
+
+    def __call__(self, context):
+        began = time.perf_counter()
+        if type(context) is not dict:
+            raise ValueError('A causal camera/action context is required.')
+        previous = _previous(context)
+        if self.encoder.last_time is None and not np.array_equal(previous[0], np.asarray(R0)):
+            raise ValueError('The first reference must equal the exact source-bound reset.')
+        raw = self.encoder(context)
+        x = np.clip((raw.astype(np.float64)-self.mean)/self.scale, -10., 10.).astype(np.float32)[None, :]
+        endpoint = decode(base._forward(x, self.weights, self.biases)[2], previous, self.model['variant'])
+        target = project(endpoint, previous)[0]
+        self.inference_seconds.append(time.perf_counter()-began)
+        return dict(joint_targets_rad=target[:6].tolist(), gripper_position_m=float(target[6]))
+
+    def diagnostics(self):
+        return dict(policy_origin='camera_encoder_reference_endpoint_numpy_mlp', variant=self.model['variant'],
+            model_sha256=base.digest(self.model), student_inference_calls=len(self.inference_seconds),
+            student_inference_seconds=sum(self.inference_seconds),
+            student_inference_p95_seconds=float(np.quantile(self.inference_seconds, .95)) if self.inference_seconds else None,
+            teacher_fallback_calls=0, runtime_teacher_available=False, physical_robot_ready=False, task_qualified=False,
+            timing_scope='Causal feature encoding, NumPy inference and reference projection; excludes camera/render/actuation.')
diff --git a/tests/test_camera_action_reference_model.py b/tests/test_camera_action_reference_model.py
new file mode 100644
index 0000000..e638a67
--- /dev/null
+++ b/tests/test_camera_action_reference_model.py
@@ -0,0 +1,262 @@
+"""Synthetic model fits only; no original recordings, trained models or physics."""
+from copy import deepcopy
+from hashlib import sha256
+import json
+from pathlib import Path
+import unittest
+from unittest.mock import patch
+
+import numpy as np
+
+from dvidia_training import camera_action_model as base
+from dvidia_training import camera_action_reference_model as m
+from tests.test_camera_action_model import context, missing_visual
+from tests import test_camera_action_reference_export as fixtures
+
+action, native_trace, write = fixtures.action, fixtures.native_trace, fixtures.write
+
+
+def reset_context(now=0., previous=None):
+    c = context(now)
+    c['previous_applied'] = action(m.R0 if previous is None else previous)
+    return c
+
+
+def set_reset(recording):
+    previous = np.asarray(m.R0, dtype=np.float64)
+    recording['initial_applied_targets'] = action(previous)
+    for step in recording['steps']:
+        previous = native_trace(previous, base.action_vector(step['command']['issued']))[-1]
+        step['applied']['ramped_targets'] = action(previous)
+
+
+class ReferenceModelTests(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        # These are authored six-row fixtures, never copies of study recordings.
+        cls.fixtures = fixtures.ReferenceExportTests(); cls.fixtures.setUp()
+        cls.demo = cls.fixtures.parent(mutation=set_reset)
+        cls.mixed = cls.fixtures.parent(mixed=True, mutation=set_reset)
+        # Both stages must bind one protocol. Rebuild only synthetic parent caches.
+        protocol = json.loads((cls.demo['root']/'protocol.json').read_bytes())
+        protocol['rosters'].update(json.loads((cls.mixed['root']/'protocol.json').read_bytes())['rosters'])
+        cls.sources = []
+        for f, stage in ((cls.demo, 'demonstrations'), (cls.mixed, 'corrections')):
+            ph = write(f['root']/'protocol.json', protocol)
+            write(f['root']/'registration.json', dict(schema=m.ref.cache.study.SCHEMA+'/registration',
+                protocol_sha256=ph, source_sha256=protocol['source_sha256']))
+            stage_file = f['root']/f'stages/{stage}.json'
+            doc = json.loads(stage_file.read_bytes()); doc['protocol_sha256'] = ph
+            sh = write(stage_file, doc)
+            write(f['root']/f'stages/{stage}.integrity.json', dict(protocol_sha256=ph, sha256=sh))
+            f['parent'] = f['parent'].with_name(f['parent'].name+'-paired')
+            with patch('dvidia_training.camera_action_capture.load_rollout', side_effect=lambda p, records=f['recordings']: records[str(p)]):
+                pins = m.ref.cache.build_cache(f['root'], f['parent'], expected_protocol_sha256=ph, stage=stage)
+            f['args'].update(expected_parent_manifest_sha256=pins['manifest_sha256'], expected_protocol_sha256=ph)
+            output = cls.fixtures.root/(stage+'-references')
+            directory, seal = cls.fixtures.build(f, output=output)
+            cls.sources.append(dict(directory=directory, parent_cache=f['parent'],
+                pins=dict(f['args'], expected_manifest_sha256=seal['manifest_sha256'])))
+        cls.models = {variant: m.train(cls.sources, variant=variant) for variant in m.VARIANTS}
+
+    @classmethod
+    def tearDownClass(cls):
+        cls.fixtures.tearDown(); cls.fixtures.doCleanups()
+
+    def test_fixed_synthetic_fits_share_data_initialization_and_normalization(self):
+        a, d = (self.models[v] for v in m.VARIANTS)
+        for field in ('reference_dataset_sha256', 'parent_dataset_sha256', 'parent_provenance_sha256', 'initial_parameters_sha256'):
+            self.assertEqual(a['training'][field], d['training'][field])
+        self.assertEqual(a['feature_mean'], d['feature_mean']); self.assertEqual(a['feature_scale'], d['feature_scale'])
+        for model in (a, d):
+            self.assertEqual(model['training']['samples'], 12)
+            self.assertEqual(model['training']['optimizer_updates'], 50)
+            self.assertEqual(model['recipe'], m.RECIPE)
+            self.assertLess(model['training']['final_loss'], model['training']['initial_loss'])
+            self.assertFalse(model['physical_robot_ready']); self.assertFalse(model['task_qualified'])
+            self.assertIsNone(model['scientific_registration'])
+        # Mixed row's executed joint0 reference decreases, its query label increases.
+        x, r, y, *_ = m._load_training(self.sources)
+        self.assertLess(r[7, 0], r[6, 0]); self.assertGreater(y[6, 0], r[6, 0])
+
+    def test_full_network_gradients_and_one_adam_update_match_at_reset(self):
+        rng = np.random.default_rng(4401)
+        x = rng.normal(0., .3, (5, base.FEATURE_COUNT)).astype(np.float32)
+        r = np.tile(np.asarray(m.R0, dtype='<f8'), (len(x), 1))
+        y = m.project(r+rng.uniform(-1., 1., r.shape)*m.D, r)
+        results = []
+        for variant in m.VARIANTS:
+            w, b, _ = m._parameters()
+            loss, gradients = m._loss_gradients(x, r, y, w, b, variant)
+            p = w+b; first = [np.zeros_like(v) for v in p]; second = [v.copy() for v in first]
+            m._adam_update(p, gradients, first, second, 1)
+            results.append((loss, gradients, p, first, second))
+        self.assertEqual(results[0][0], results[1][0])
+        for left, right in zip(results[0][1:], results[1][1:]):
+            for a, b in zip(left, right): np.testing.assert_array_equal(a, b)
+
+    def test_backprop_directional_finite_differences_cover_all_layers(self):
+        rng = np.random.default_rng(4402)
+        x = rng.normal(0., .2, (4, base.FEATURE_COUNT))
+        r = np.tile(np.asarray(m.R0, dtype='<f8'), (len(x), 1)); r[:, 0] = .15
+        y = m.project(r+.4*m.D, r)
+        w, b, _ = m._parameters()
+        # A smooth float64 oracle checks the analytic chain rule; production
+        # float32 parameters/updates are separately compared bit-for-bit above.
+        parameters = [p.astype(np.float64) for p in w+b]
+        for variant in m.VARIANTS:
+            _, gradients = m._loss_gradients(x, r, y, parameters[:3], parameters[3:], variant)
+            for layer, p in enumerate(parameters):
+                direction = rng.normal(size=p.shape); direction /= np.linalg.norm(direction)
+                step = 1e-5
+                plus, minus = [a.copy() for a in parameters], [a.copy() for a in parameters]
+                plus[layer] += step*direction; minus[layer] -= step*direction
+                finite = (m._loss(x, r, y, plus[:3], plus[3:], variant)-m._loss(x, r, y, minus[:3], minus[3:], variant))/(2*step)
+                analytic = float(np.sum(gradients[layer]*direction))
+                self.assertAlmostEqual(finite, analytic, delta=1e-9)
+
+    def test_later_reference_difference_is_intended_and_preprojection(self):
+        rng = np.random.default_rng(4403)
+        x = rng.normal(size=(3, base.FEATURE_COUNT)).astype(np.float32)
+        r = np.tile(np.asarray(m.R0, dtype='<f8'), (3, 1)); r[:, 0] = .3
+        w, b, _ = m._parameters(); head = base._forward(x, w, b)[2]
+        a, d = [m.decode(head, r, variant) for variant in m.VARIANTS]
+        np.testing.assert_allclose(d-a, r-np.asarray(m.R0), atol=1e-15, rtol=0.)
+        target = m.project(r-.4*m.D, r)
+        la, ga = m._loss_gradients(x, r, target, w, b, 'absolute')
+        ld, gd = m._loss_gradients(x, r, target, w, b, 'delta')
+        self.assertNotEqual(la, ld)
+        self.assertTrue(any(not np.array_equal(a, d) for a, d in zip(ga, gd)))
+
+    def test_normalization_matches_training_and_policy_exactly(self):
+        x, *_ = m._load_training(self.sources)
+        for model in self.models.values():
+            np.testing.assert_array_equal(np.asarray(model['feature_mean']), x.mean(axis=0))
+            np.testing.assert_array_equal(np.asarray(model['feature_scale']), np.maximum(x.std(axis=0), 1e-4))
+            c = reset_context(); raw = base.CameraActionFeatures()(c)
+            expected = np.clip((raw.astype(np.float64)-x.mean(axis=0))/np.maximum(x.std(axis=0), 1e-4), -10., 10.).astype(np.float32)[None, :]
+            forward = base._forward
+            seen = []
+            def spy(values, w, b):
+                seen.append(values.copy()); return forward(values, w, b)
+            with patch.object(base, '_forward', side_effect=spy): m.ModelPolicy(model)(c)
+            self.assertEqual(len(seen), 1); np.testing.assert_array_equal(seen[0], expected)
+
+    def test_save_load_policy_parity_preserves_existing_file(self):
+        for variant, model in self.models.items():
+            dest = self.fixtures.root/(variant+'-model.json')
+            digest = m.save_model(model, dest); restored = m.load_model(dest)
+            self.assertEqual(base.digest(restored), digest)
+            with self.assertRaises(FileExistsError): m.save_model(model, dest)
+            left, right = m.ModelPolicy(model), m.ModelPolicy(restored)
+            previous = np.asarray(m.R0)
+            for i in range(5):
+                c = reset_context(i*.02, previous)
+                if i == 1: c = missing_visual(c)
+                a, b = left(c), right(c); self.assertEqual(a, b)
+                previous = base.action_vector(a)
+            self.assertEqual(left.diagnostics()['student_inference_calls'], 5)
+            self.assertEqual(left.diagnostics()['teacher_fallback_calls'], 0)
+
+    def test_exact_double_reference_does_not_roundtrip_through_features(self):
+        model = deepcopy(self.models['delta'])
+        model['weights'] = [np.zeros_like(w).tolist() for w in model['weights']]
+        model['biases'] = [np.zeros_like(b).tolist() for b in model['biases']]
+        previous = np.asarray(m.R0); previous[0] = .10000000000000002
+        rounded = previous.copy(); rounded[0] = float(np.float32(previous[0]))
+        raw = []
+        for value in (previous, rounded):
+            encoder = base.CameraActionFeatures(); encoder(reset_context())
+            raw.append(encoder(reset_context(.02, value)))
+        np.testing.assert_array_equal(raw[0], raw[1])
+        a, b = m.ModelPolicy(model), m.ModelPolicy(model)
+        a(reset_context()); b(reset_context())
+        outa = a(reset_context(.02, previous)); outb = b(reset_context(.02, rounded))
+        self.assertEqual(outa['joint_targets_rad'][0], previous[0])
+        self.assertEqual(outb['joint_targets_rad'][0], rounded[0])
+        self.assertNotEqual(outa['joint_targets_rad'][0], outb['joint_targets_rad'][0])
+        # A caller can lie using Python floats; the bridge does not claim to
+        # authenticate it. Explicit float32 arrays are rejected before decoding.
+        c = reset_context(); c['previous_applied']['joint_targets_rad'] = np.asarray(m.R0[:6], dtype=np.float32)
+        with self.assertRaisesRegex(ValueError, 'reduced-precision'): m.ModelPolicy(model)(c)
+
+    def test_context_causality_reset_and_no_label_inputs(self):
+        for model in self.models.values():
+            clean = reset_context(); poisoned = deepcopy(clean)
+            poisoned.update(teacher_query={'action': [99]*7}, native_truth={'contacts': 999}, reference={'action': [99]*7}, after={'timestamp_seconds': 20})
+            poisoned['snapshot']['signals']['object_position_estimate_m'] = {'value': [99]*3}
+            self.assertEqual(m.ModelPolicy(model)(clean), m.ModelPolicy(model)(poisoned))
+            for bad in (context(), reset_context(.02), missing_visual(reset_context())):
+                with self.assertRaises(ValueError): m.ModelPolicy(model)(bad)
+            policy = m.ModelPolicy(model); policy(clean)
+            with self.assertRaises(ValueError): policy(clean)
+            policy = m.ModelPolicy(model); policy(clean)
+            with self.assertRaises(ValueError): policy(reset_context(.04))
+
+    def test_changed_source_schema_units_reset_and_recipe_are_refused(self):
+        document = self.models['delta']
+        mutations = [lambda v: v.update(task_qualified=True), lambda v: v.update(physical_robot_ready=True),
+            lambda v: v.update(scientific_registration={'claim': 'qualified'}),
+            lambda v: v.update(schema=base.SCHEMA), lambda v: v.update(variant='residual'),
+            lambda v: v['contract'].update(reset_reference=[0.]*7), lambda v: v['contract'].update(reference_kind='measured_q'),
+            lambda v: v['contract'].update(action_units=['metres']*7), lambda v: v['contract'].update(joint_order=list(reversed(v['contract']['joint_order']))),
+            lambda v: v['recipe'].update(epochs=51), lambda v: v['training'].update(optimizer_updates=49),
+            lambda v: v['training'].update(parent_provenance_sha256='0'*64), lambda v: v.update(future_teacher_command=[0.]*7)]
+        for mutate in mutations:
+            bad = deepcopy(document); mutate(bad)
+            with self.assertRaises(ValueError): m.validate_model(bad)
+        with patch.object(m.ref, 'source_identity', side_effect=ValueError('source drift')):
+            with self.assertRaises(ValueError): m.ModelPolicy(document)
+
+    def test_train_rejects_holdout_wrong_source_stage_order_and_array_substitution(self):
+        for field, bad_value in (('expected_split', 'confirmation'), ('expected_split', 'development'),
+                ('expected_source_sha256', '0'*64), ('expected_parent_manifest_sha256', '0'*64),
+                ('expected_manifest_sha256', '0'*64), ('expected_protocol_sha256', '0'*64)):
+            inputs = deepcopy(self.sources); inputs[0]['pins'][field] = bad_value
+            with self.assertRaises(ValueError): m.train(inputs, variant='delta')
+        with self.assertRaises(ValueError): m.train(self.sources[::-1], variant='delta')
+        with self.assertRaises(ValueError): m.train([{'features': np.zeros((2, 269))}], variant='delta')
+        with self.assertRaises(ValueError): m.train(self.sources, variant='unknown')
+        bad = deepcopy(self.sources); bad[1]['pins']['expected_protocol_sha256'] = '0'*64
+        with self.assertRaises(ValueError): m.train(bad, variant='delta')
+
+    def test_training_consumes_only_derived_inputs_and_not_raw_or_models(self):
+        with (patch('dvidia_training.camera_action_capture.load_rollout', side_effect=AssertionError('no native reader')),
+              patch.object(m.export, 'export_episode', side_effect=AssertionError('no raw export')),
+              patch.object(base, 'load_model', side_effect=AssertionError('no old model'))):
+            x, r, y, *_ = m._load_training(self.sources)
+        self.assertEqual(x.shape, (12, 269)); self.assertEqual(r.dtype, np.dtype('float64')); self.assertEqual(y.shape, r.shape)
+
+    def test_non_source_reset_is_refused_before_fitting(self):
+        f = self.fixtures.parent()  # Source reference fixture deliberately has a different reset.
+        directory, seal = self.fixtures.build(f)
+        inputs = [dict(directory=directory, parent_cache=f['parent'],
+            pins=dict(f['args'], expected_manifest_sha256=seal['manifest_sha256']))]
+        with patch.object(m, '_parameters', side_effect=AssertionError('must not fit')):
+            with self.assertRaisesRegex(ValueError, 'source-bound reset'):
+                m.train(inputs, variant='delta')
+
+    def test_aggregate_row_budget_refuses_before_combination_or_fitting(self):
+        # Each six-row sealed fixture fits this limit, but their sum does not.
+        with (patch.object(m.ref, 'MAX_SAMPLES', 11),
+              patch.object(m.export, 'combine_datasets', side_effect=AssertionError('must not concatenate')),
+              patch.object(m, '_parameters', side_effect=AssertionError('must not fit'))):
+            with self.assertRaisesRegex(ValueError, 'Combined training rows'):
+                m.train(self.sources, variant='delta')
+
+    def test_projection_uses_same_original_twenty_updates_and_native_bounds(self):
+        r = np.tile(np.asarray(m.R0), (2, 1)); r[1, 0] = .31
+        head = np.asarray([[1e4]*7, [-1e4]*7])
+        limits = np.asarray(m.ref.CONTRACT['limits_native'])
+        for variant in m.VARIANTS:
+            decoded = m.decode(head, r, variant)
+            result = m.project(decoded, r)
+            targets = np.clip(decoded, limits[:, 0], limits[:, 1])
+            expected = np.asarray([native_trace(a, b)[-1] for a, b in zip(r, targets)])
+            np.testing.assert_array_equal(result, expected)
+        with self.assertRaises(ValueError): m.decode(head, r.astype(np.float32), 'delta')
+        with self.assertRaises(ValueError): m.decode(head*np.nan, r, 'delta')
+
+
+if __name__ == '__main__':
+    unittest.main()

From 78ea4fbb4b2733e7738eb425eeecd7cb67a52195 Mon Sep 17 00:00:00 2001
From: DVIDIA <hello@dvidia.org>
Date: Mon, 9 Nov 2026 10:48:31 -0500
Subject: [PATCH 2/3] Verify reference model portability in the existing CI
 matrix

---
 .gitattributes           | 1 +
 .github/workflows/ci.yml | 2 ++
 2 files changed, 3 insertions(+)

diff --git a/.gitattributes b/.gitattributes
index b47787f..aae0277 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -16,6 +16,7 @@ src/dvidia_training/camera_action_capture.py text eol=lf
 src/dvidia_training/robot_profiles.lock.json text eol=lf
 src/dvidia_training/camera_action_cache.py text eol=lf
 src/dvidia_training/camera_action_reference_export.py text eol=lf
+src/dvidia_training/camera_action_reference_model.py text eol=lf
 src/dvidia_training/camera_action_study.py text eol=lf
 src/dvidia_training/piper_rgbd_tracker.py text eol=lf
 src/dvidia_training/action_demonstration.py text eol=lf
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 0243095..356fccb 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -84,6 +84,8 @@ jobs:
         run: |
           python -B -m unittest tests.test_camera_action_reference_export -v
           python -B -m unittest discover -s benchmarks/camera-action-v02-head-preflight -p test_preflight.py -v
+      - name: Verify reference-model training and inference on synthetic fixtures
+        run: python -B -m unittest tests.test_camera_action_reference_model -v
       - name: Test footage, visual training and loopback studio
         run: python -m unittest tests.test_footage_data tests.test_footage_train tests.test_footage_pipeline tests.test_footage_benchmark tests.test_training_studio tests.test_doctor tests.test_coverage tests.test_episode_tape tests.test_reaction tests.test_reaction_metrics tests.test_reaction_convergence tests.test_reaction_benchmark tests.test_motor_memory tests.test_motor_metrics tests.test_motor_benchmark tests.test_observations tests.test_observation_models tests.test_motion tests.test_motion_benchmark tests.test_transition_model tests.test_transition_pilot tests.test_transition_cli tests.test_embodiment_calibration tests.test_embodiment_cli tests.test_reference_sim -v
 

From 8774265fdf2a4516520cdf36ff2eb41dcd04dfda Mon Sep 17 00:00:00 2001
From: DVIDIA <hello@dvidia.org>
Date: Mon, 9 Nov 2026 11:50:54 -0500
Subject: [PATCH 3/3] Publish reference-model fits and eight failed development
 placements

---
 .../LICENSE                                   |   21 +
 .../README.md                                 |   39 +
 .../data/README.md                            |   26 +
 .../evidence/development-smoke-status.json    |  362 +++
 .../evidence/graphics-preflight.json          |   50 +
 .../evidence/initial-smoke-accounting.json    |   14 +
 .../evidence/integration-result.json          |  217 ++
 .../evidence/native-smoke-diagnosis.json      |  191 ++
 .../evidence/native-smoke-v3-audit.json       | 2238 +++++++++++++++++
 .../evidence/renderer-warnings.json           |   71 +
 .../evidence/training-input-identity.json     |  214 ++
 .../models/absolute/README.md                 |   81 +
 .../models/absolute/model.json                |    1 +
 .../models/delta/README.md                    |   81 +
 .../models/delta/model.json                   |    1 +
 ...-action-reference-model-integration-v01.md |   36 +
 docs/camera-action-reference-model-v01.md     |   22 +-
 17 files changed, 3658 insertions(+), 7 deletions(-)
 create mode 100644 benchmarks/camera-action-reference-model-integration-v01/LICENSE
 create mode 100644 benchmarks/camera-action-reference-model-integration-v01/README.md
 create mode 100644 benchmarks/camera-action-reference-model-integration-v01/data/README.md
 create mode 100644 benchmarks/camera-action-reference-model-integration-v01/evidence/development-smoke-status.json
 create mode 100644 benchmarks/camera-action-reference-model-integration-v01/evidence/graphics-preflight.json
 create mode 100644 benchmarks/camera-action-reference-model-integration-v01/evidence/initial-smoke-accounting.json
 create mode 100644 benchmarks/camera-action-reference-model-integration-v01/evidence/integration-result.json
 create mode 100644 benchmarks/camera-action-reference-model-integration-v01/evidence/native-smoke-diagnosis.json
 create mode 100644 benchmarks/camera-action-reference-model-integration-v01/evidence/native-smoke-v3-audit.json
 create mode 100644 benchmarks/camera-action-reference-model-integration-v01/evidence/renderer-warnings.json
 create mode 100644 benchmarks/camera-action-reference-model-integration-v01/evidence/training-input-identity.json
 create mode 100644 benchmarks/camera-action-reference-model-integration-v01/models/absolute/README.md
 create mode 100644 benchmarks/camera-action-reference-model-integration-v01/models/absolute/model.json
 create mode 100644 benchmarks/camera-action-reference-model-integration-v01/models/delta/README.md
 create mode 100644 benchmarks/camera-action-reference-model-integration-v01/models/delta/model.json
 create mode 100644 docs/camera-action-reference-model-integration-v01.md

diff --git a/benchmarks/camera-action-reference-model-integration-v01/LICENSE b/benchmarks/camera-action-reference-model-integration-v01/LICENSE
new file mode 100644
index 0000000..a7056f2
--- /dev/null
+++ b/benchmarks/camera-action-reference-model-integration-v01/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 DVIDIA contributors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/benchmarks/camera-action-reference-model-integration-v01/README.md b/benchmarks/camera-action-reference-model-integration-v01/README.md
new file mode 100644
index 0000000..9412fb4
--- /dev/null
+++ b/benchmarks/camera-action-reference-model-integration-v01/README.md
@@ -0,0 +1,39 @@
+# DVIDIA reference-model training and inference integration
+
+Two small PiPER research candidates now train and serialize successfully from the same 96,000 training rows. The absolute-reference fit took 5.230713 s and the prior-reference delta fit took 5.097628 s on the measured shared CPU host. Both saved models reproduced all 1,000 outputs in the recorded-trajectory replay. This validates the learning/inference bridge, not task completion.
+
+- [Absolute candidate card](models/absolute/README.md)
+- [Delta candidate card](models/delta/README.md)
+- [Derived-data card](data/README.md)
+- [Compact integration receipt](evidence/integration-result.json)
+- [Training reference identity](evidence/training-input-identity.json)
+- [Initial native smoke refusal accounting](evidence/initial-smoke-accounting.json)
+- [All three engineering batches](evidence/development-smoke-status.json)
+- [Independent eight-attempt native audit](evidence/native-smoke-v3-audit.json)
+- [Renderer warnings](evidence/renderer-warnings.json)
+- [Strict-state failure diagnosis](evidence/native-smoke-diagnosis.json)
+
+The third separately registered native batch completed eight attempts: **0/4 strict placements for each candidate**. Cabs made bilateral contact in 3/4 and a qualified lift in 1/4; Cdelta contacted in 4/4 and lifted in 0/4. Neither completed carry. Unexpected contacts occurred in 2/4 Cabs episodes and 4/4 Cdelta episodes. All four delta traces crossed the strict scorer’s finite TCP coordinate envelope of ±0.5 m; this was not a NaN or physics-warning failure. Full per-case evidence is retained, together with the sixteen earlier infrastructure refusals. No candidate was selected or qualified; the proposed 164-attempt screen and frozen v0.1 result are unchanged.
+
+The two candidate model files are included under `models/absolute/model.json` and `models/delta/model.json`. They are usable through the pinned Python API; this new model format is not supported by the qualified-skill CLI installer. Raw training recordings and derived reference arrays are not redistributed here. Original artifacts and source commit `78ea4fbb4b2733e7738eb425eeecd7cb67a52195` are unchanged. Exact original result SHA-256: `6d44d98488ef26f949412aadc94c32174bd5c4ce879442191b9784deff5b325f`.
+
+
+## Inspect a retained candidate
+
+Use a source checkout containing this benchmark and the unchanged runtime bytes identified below. Install the package and its existing NumPy dependency with `python -m pip install -e .`, then run Python from the repository root:
+
+```python
+from dvidia_training import camera_action_reference_model as reference_model
+
+model = reference_model.load_model(
+    "benchmarks/camera-action-reference-model-integration-v01/models/delta/model.json"
+)
+assert not model["task_qualified"]
+assert not model["physical_robot_ready"]
+policy = reference_model.ModelPolicy(model)
+print(model["variant"], model["training"]["samples"])
+```
+
+Loading does not start a simulator, read a camera or command hardware. Calling `policy(context)` requires the recorder's exact causal context and retained float64 previous reference, beginning at the registered reset; an arbitrary image or joint vector does not satisfy that contract. Any next native experiment requires its own registration; these two candidates remain unchanged. Do not infer operational task success from successful loading or a training-trajectory replay.
+
+Source at measurement: [`78ea4fbb4b2733e7738eb425eeecd7cb67a52195`](https://github.com/Dvidia-Inference/dvidia-training/tree/78ea4fbb4b2733e7738eb425eeecd7cb67a52195). See [the model bridge contract](../../docs/camera-action-reference-model-v01.md) and [integration notes](../../docs/camera-action-reference-model-integration-v01.md).
diff --git a/benchmarks/camera-action-reference-model-integration-v01/data/README.md b/benchmarks/camera-action-reference-model-integration-v01/data/README.md
new file mode 100644
index 0000000..96597c3
--- /dev/null
+++ b/benchmarks/camera-action-reference-model-integration-v01/data/README.md
@@ -0,0 +1,26 @@
+---
+license: mit
+language:
+- en
+tags:
+- robotics
+- simulation
+- demonstrations
+- research
+---
+
+# DVIDIA PiPER reference training data
+
+This card describes the derived training inputs for both retained reference-model candidates. This data directory contains an identity receipt; the benchmark does not redistribute raw videos, caches or reference arrays. Both candidate model files are included separately.
+
+The data comprises 96,000 control rows from 96 existing simulation episodes in 32 training starting-position groups: 64 authored-teacher demonstrations covering both destinations, followed by 32 previously collected mixed-control corrections. These are synchronized simulated camera/action data, not recovered actions from human video. Development and confirmation episodes are excluded from fitting.
+
+Each parent row contains 269 causal features, the issued-teacher or separately recorded teacher-query target, group ownership and provenance. Each reference export adds two C-order float64 `(N, 7)` arrays: exact previous normal servo references and feasible target endpoints. Six dimensions use radians; the seventh is the single-finger jaw coordinate in metres. Reset references come from the recording reset; later references come from the preceding step's actual normal ramp state. The original 20 updates are applied to teacher labels. For mixed control this is counterfactual normal-reference supervision, not a statement that those labels were executed.
+
+The parent cache was built through the original strict exporter. The reference-export construction verified sealed recording metadata and caller-pinned parent data; cache/reference loads validate derived bytes without re-reading raw RGB-D frames. This integration experiment did not revalidate raw recordings. Array, protocol, stage, source, parent dataset and manifest identities are in [training-input-identity.json](../evidence/training-input-identity.json).
+
+Source: [DVIDIA training at the benchmark commit](https://github.com/Dvidia-Inference/dvidia-training/tree/78ea4fbb4b2733e7738eb425eeecd7cb67a52195). Training uses only training-data normalization. Current teacher actions, future observations and native object/contact truth are excluded from features. Missing visual observations remain masked, and historical estimates retain their age semantics.
+
+These bounded synthetic demonstrations do not establish transfer to shoes, arbitrary videos, other arms or physical hardware. Model training losses and a single training-trajectory replay do not establish generalization. The original dataset ownership and failure accounting are preserved.
+
+DVIDIA-generated derived data and accompanying documentation use [MIT](../LICENSE). Upstream simulator and robot asset licenses remain separate. No raw human footage, credentials or upstream model weights are included in this benchmark.
diff --git a/benchmarks/camera-action-reference-model-integration-v01/evidence/development-smoke-status.json b/benchmarks/camera-action-reference-model-integration-v01/evidence/development-smoke-status.json
new file mode 100644
index 0000000..d029aaa
--- /dev/null
+++ b/benchmarks/camera-action-reference-model-integration-v01/evidence/development-smoke-status.json
@@ -0,0 +1,362 @@
+{
+  "critical_contact_metrics": "See per-variant critical_contact_ticks and episodes_with_critical_contact in native-smoke-v3-audit.json.",
+  "evidence_state": "locally_validated_unqualified; remote hosting not asserted",
+  "funnel_metrics": {
+    "absolute": {
+      "callback_including_guard": {
+        "calls": 4000,
+        "maximum_seconds": 0.008621292014140636,
+        "median_seconds": 0.00023227051133289933,
+        "p95_seconds": 0.00029546011064667254
+      },
+      "carry_complete": 0,
+      "complete_episodes": 4,
+      "critical_contact_ticks": 7606,
+      "episodes_with_critical_contact": 2,
+      "full_boundary": {
+        "calls": 4000,
+        "maximum_seconds": 0.051054958981694654,
+        "median_seconds": 0.013028145520365797,
+        "p95_seconds": 0.022362537491426335
+      },
+      "grasp_seen": 3,
+      "lift_seen": 1,
+      "qualified": false,
+      "retention_lost": 0,
+      "scheduled": 4,
+      "scored_control_trajectories": 4,
+      "strict_successes": 0,
+      "terminal_only_successes": 0
+    },
+    "delta": {
+      "callback_including_guard": {
+        "calls": 4000,
+        "maximum_seconds": 0.021589000010862947,
+        "median_seconds": 0.00023095900542102754,
+        "p95_seconds": 0.0002842972404323518
+      },
+      "carry_complete": 0,
+      "complete_episodes": 4,
+      "critical_contact_ticks": 19751,
+      "episodes_with_critical_contact": 4,
+      "full_boundary": {
+        "calls": 4000,
+        "maximum_seconds": 0.058383833995321766,
+        "median_seconds": 0.013461853988701478,
+        "p95_seconds": 0.02204904312093276
+      },
+      "grasp_seen": 4,
+      "lift_seen": 0,
+      "qualified": false,
+      "retention_lost": 0,
+      "scheduled": 4,
+      "scored_control_trajectories": 4,
+      "strict_successes": 0,
+      "terminal_only_successes": 0
+    }
+  },
+  "independently_scored_control_trajectories": 8,
+  "initial_infrastructure_batches": [
+    {
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+      "plan_sha256": "3818be616fe37f976e2b31f86a21f5c429f11b4e4131a43d5174ed1bb771e289",
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+        "sha256": "2e9a40eaa5799f3bc6d0d36ee10f5fe8a6cf0bfe387f428f399d66a4e9c5c6fe"
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+          "recorded_complete_intervals": 0,
+          "rollout_error": {
+            "message": "invalid CoreGraphics connection",
+            "type": "CGLError"
+          },
+          "startup_permission_closed": true,
+          "startup_probe_calls": 1,
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+          }
+        }
+      ],
+      "run_id": "reference-model-development-smoke-v2-20261109",
+      "scheduled_attempts": 8
+    }
+  ],
+  "interpretation": "Report0/4 strict placements per model on four reused development scenarios. The prior16 infrastructure refusals are retained separately, not pooled into a0/24 manipulation score. This is not a generalization or comparator study.",
+  "learned_callback_latency": "See per-variant callback_including_guard distributions in native-smoke-v3-audit.json.",
+  "native_audit_sha256": "098c3c7b8c678790cc089dbec50d5c7c874e0a54da72844e48c49222683f9d7f",
+  "native_initialization_scope": "First eight import refusals and second eight graphics refusals remain separate. Third batch used separately registered scoped graphics access and completed all8 native learned-control attempts; neither model succeeded.",
+  "native_plan_sha256": "9912b3b86abe889d81073b1fee1c9cad9655264fdb325abbbf60dbebd89a490b",
+  "native_result_file": {
+    "bytes": 7153,
+    "sha256": "597277fd6c4671a7d2791aa861beb5da896573fcbf51f8db08e30d4bc501284e"
+  },
+  "next_prerequisite": "A separately registered failure-correction experiment; no automatic retries, tuning or new qualification.",
+  "policy_callbacks": 8000,
+  "qualified": false,
+  "recorded_control_intervals": 8000,
+  "review_receipt_sha256": "2270d1043e8dd68286627d15f2c4f039a7fdda9913d843ebe5e7551e1523367e",
+  "scheduled_attempts_across_batches": 24,
+  "schema": "dvidia.reference-model-development-smoke-status/v2",
+  "selection_performed": false
+}
diff --git a/benchmarks/camera-action-reference-model-integration-v01/evidence/graphics-preflight.json b/benchmarks/camera-action-reference-model-integration-v01/evidence/graphics-preflight.json
new file mode 100644
index 0000000..f4e355a
--- /dev/null
+++ b/benchmarks/camera-action-reference-model-integration-v01/evidence/graphics-preflight.json
@@ -0,0 +1,50 @@
+{
+  "results": {
+    "default-sandbox.json": {
+      "original_file": {
+        "bytes": 498,
+        "sha256": "74c96d8856d24c3f89680202b63ab67efe5a45b42a97e17ff44429d347827d7d"
+      },
+      "result": {
+        "error": "invalid CoreGraphics connection",
+        "error_type": "CGLError",
+        "execution_context": "default-sandbox",
+        "machine": "arm64",
+        "mujoco": "3.15.0",
+        "numpy": "2.5.3",
+        "python": "3.12.9",
+        "scope": "Tiny authored static XML, forward kinematics only, one48x64RGB frame; no robot/assets/dataset/task/model weights or dynamics steps.",
+        "status": "failed",
+        "total_wall_seconds": 0.12905491699348204
+      }
+    },
+    "scoped-graphics-access.json": {
+      "original_file": {
+        "bytes": 674,
+        "sha256": "dbfaef196312bcd905ad97288a63732a9b57bca5eb3a4119e03a885f9af3af55"
+      },
+      "result": {
+        "execution_context": "scoped-graphics-access",
+        "frame_dtype": "uint8",
+        "frame_max": 185,
+        "frame_min": 0,
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+        "mujoco": "3.15.0",
+        "numpy": "2.5.3",
+        "python": "3.12.9",
+        "render_wall_seconds": 0.020181250001769513,
+        "scope": "Tiny authored static XML, forward kinematics only, one48x64RGB frame; no robot/assets/dataset/task/model weights or dynamics steps.",
+        "status": "passed",
+        "total_wall_seconds": 0.16178770799888298
+      }
+    }
+  },
+  "schema": "dvidia.reference-model-graphics-preflight-summary/v1",
+  "scope": "One static48x64frame, forward kinematics only; no robot/assets/data/model weights or dynamics. Interpreter path omitted from this public summary."
+}
diff --git a/benchmarks/camera-action-reference-model-integration-v01/evidence/initial-smoke-accounting.json b/benchmarks/camera-action-reference-model-integration-v01/evidence/initial-smoke-accounting.json
new file mode 100644
index 0000000..8a4e10b
--- /dev/null
+++ b/benchmarks/camera-action-reference-model-integration-v01/evidence/initial-smoke-accounting.json
@@ -0,0 +1,14 @@
+{
+  "cause": "The experiment wrapper prohibited MuJoCo macOS import-time sysctl subprocess; each attempt stopped before policy callbacks.",
+  "interpretation": "Infrastructure refusal, not measured manipulation failure. All eight scheduled attempts remain accounted for.",
+  "native_evaluation": "No valid closed-loop result yet. Any further attempt needs separate registration and must preserve these results.",
+  "native_initialization_refusals": 8,
+  "original_result_sha256": "2e9a40eaa5799f3bc6d0d36ee10f5fe8a6cf0bfe387f428f399d66a4e9c5c6fe",
+  "plan_sha256": "3818be616fe37f976e2b31f86a21f5c429f11b4e4131a43d5174ed1bb771e289",
+  "policy_callbacks": 0,
+  "qualified": false,
+  "scheduled_attempts": 8,
+  "schema": "dvidia.reference-model-initial-smoke-accounting/v1",
+  "selection_performed": false,
+  "strict_scored_rollouts": 0
+}
diff --git a/benchmarks/camera-action-reference-model-integration-v01/evidence/integration-result.json b/benchmarks/camera-action-reference-model-integration-v01/evidence/integration-result.json
new file mode 100644
index 0000000..6d3e80f
--- /dev/null
+++ b/benchmarks/camera-action-reference-model-integration-v01/evidence/integration-result.json
@@ -0,0 +1,217 @@
+{
+  "causal_replay_features_equal_parent": true,
+  "common_initialization_and_normalization": true,
+  "contract": {
+    "absolute_decoder": "r0 + D*g(x)",
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+      "rad",
+      "rad",
+      "rad",
+      "single_finger_m"
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+      "joint2",
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+      "joint4",
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+      "single_finger_jaw"
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+      3.0,
+      3.0,
+      3.0,
+      3.0,
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+    "Both candidates retained; frozenv01 results and unrun164-attemptv02 screen unchanged.",
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+}
diff --git a/benchmarks/camera-action-reference-model-integration-v01/evidence/native-smoke-diagnosis.json b/benchmarks/camera-action-reference-model-integration-v01/evidence/native-smoke-diagnosis.json
new file mode 100644
index 0000000..1225743
--- /dev/null
+++ b/benchmarks/camera-action-reference-model-integration-v01/evidence/native-smoke-diagnosis.json
@@ -0,0 +1,191 @@
+{
+  "delta_strict_violations": [
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+  "interpretation": {
+    "causality": "Own-state reference drift is a candidate mechanism; this read-only smoke does not establish its cause or isolate a treatment. No tuning or model ranking conclusion from four cases per variant.",
+    "contact_scope": "Critical ticks count 1ms simulated timesteps with at least one critical contact, not independent collision events. No real hardware force inference.",
+    "delta_state_failure": "All four delta trajectories contain finite TCP coordinates outside the unchanged strict scorer component envelope [-0.5,0.5] metres. Generic invalid_state_value is not a NaN or solver-warning diagnosis.",
+    "dominant_failure": "Seven cases never achieved the required sustained bilateral table-free lift; one absolute case lifted but did not complete supported placement, release or retreat by the 20-second budget. All eight failed retained carry completion.",
+    "floor_case": "development-00-goal0-delta first fails on TCP x while block still has one table contact. It later ends with four explicitly mapped block-floor contacts, no table support, and block centre z=0.019892m.",
+    "prior_batches": "Two distinct eight-attempt initialization-refusal batches are preserved and linked from the v3 result. Those had zero controller calls and are not control performance evaluations.",
+    "timing_scope": "Model diagnostic includes causal feature encoding, NumPy inference and feasible-reference projection. Pooled callback includes separate cooperative disk guard. Full boundary includes captured simulation and measurement work. Rendering estimates are offline, with depth-accuracy warning; no physical latency claim.",
+    "valid_semantics": "Outer valid checks native arrays, recomputed metrics and warning-free native physics; success additionally requires unchanged strict_v01 and native task success. Valid is not a task, workspace or safety qualification."
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+  "schema": "dvidia.reference-model-smoke-diagnosis-summary/v1",
+  "scope": "Compact read-only diagnosis derived from the separately retained full analysis, not new simulation or a causal treatment experiment.",
+  "source_bindings": {
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+}
diff --git a/benchmarks/camera-action-reference-model-integration-v01/evidence/native-smoke-v3-audit.json b/benchmarks/camera-action-reference-model-integration-v01/evidence/native-smoke-v3-audit.json
new file mode 100644
index 0000000..1af1e86
--- /dev/null
+++ b/benchmarks/camera-action-reference-model-integration-v01/evidence/native-smoke-v3-audit.json
@@ -0,0 +1,2238 @@
+{
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+        "id": "development-00-goal0",
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+        "scope": "Once per simulated20ms callback, not a wall-clock50Hz monitor; no task features changed.",
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+      "id": "development-00-goal0-absolute",
+      "inspection": {
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+        "reference_kind": "previous_applied_normal_servo_reference",
+        "reset_source": "initial_applied_targets",
+        "timestep_s": 0.001
+      },
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+      "parent_dataset_sha256": "07b175f78e4e77cbdde9a461a4bcae4dcb0db7d389ee4f2e702ff28db8bb3dfc",
+      "parent_provenance_sha256": "7103ad53111cb114cdcd164cfa15bbbf359ebc4ae66e7456ab7c1f38820b2864",
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+        "expected_source_sha256": "e5b26c86013732007c483a23ad7218271980f0f03563c4ffd95c01ad87fd3041",
+        "expected_split": "train",
+        "expected_stage": "demonstrations"
+      },
+      "raw_source_reverified": false,
+      "samples": 64000
+    },
+    {
+      "arrays": {
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+          "shape": [
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+        "action_units": [
+          "rad",
+          "rad",
+          "rad",
+          "rad",
+          "rad",
+          "rad",
+          "single_finger_m"
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+        "actual_pulse_overridden_controls_are_not_references": true,
+        "clock_tolerance_s": 1e-09,
+        "control_dt_s": 0.02,
+        "endpoint_algorithm": "20 original float64 reference updates, not closed-form projection",
+        "jaw_rate_m_s": 0.05,
+        "joint_order": [
+          "joint1",
+          "joint2",
+          "joint3",
+          "joint4",
+          "joint5",
+          "joint6"
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+        "joint_rate_rad_s": 1.5,
+        "later_source": "steps[i-1].applied.ramped_targets",
+        "limiter_ast_sha256": "a5356fb9e1bd8b1dc6e6b2b14d657fbe625e018ba8d1e5e668ba4d11191f7921",
+        "limits_native": [
+          [
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+        "numerical_equivalence_tolerance_native": 1e-12,
+        "reference_kind": "previous_applied_normal_servo_reference",
+        "reset_source": "initial_applied_targets",
+        "timestep_s": 0.001
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+      "parent_dataset_sha256": "0561f8793ac6f68318d28c99e15ee8755c85412f3e34c990e1c4f0da4a5c1789",
+      "parent_provenance_sha256": "0267af1dcc2035453f35a8fcb862bb190a6f2bdb50efb516fe7213f21d392b6a",
+      "parent_target_sha256": "49b2f30ed513b2f575186648b6aac169b24e07021adefaa8e3ace76e50733c1c",
+      "pins": {
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+        "expected_parent_manifest_sha256": "0997805831dfaacfe38d87468587f58c4e0b3f748165816e2167c4376676b96b",
+        "expected_parent_source_sha256": "0ab01145c66d651eaca96d763b4bec1706327f2cdd4bd8665d05056cbdd6f677",
+        "expected_protocol_sha256": "576851e321034a0f6d3cb4d0818b3a5ca97975fcc6f2c7fa30b07ffd203a2d76",
+        "expected_source_sha256": "e5b26c86013732007c483a23ad7218271980f0f03563c4ffd95c01ad87fd3041",
+        "expected_split": "train",
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+      "samples": 32000
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+  ]
+}
diff --git a/benchmarks/camera-action-reference-model-integration-v01/models/absolute/README.md b/benchmarks/camera-action-reference-model-integration-v01/models/absolute/README.md
new file mode 100644
index 0000000..969a2d3
--- /dev/null
+++ b/benchmarks/camera-action-reference-model-integration-v01/models/absolute/README.md
@@ -0,0 +1,81 @@
+---
+license: mit
+language:
+- en
+tags:
+- robotics
+- simulation
+- numpy
+- research-candidate
+---
+
+# DVIDIA PiPER reference model — Cabs
+
+**Research candidate; not a qualified skill or hardware controller.** This card documents the retained `absolute` candidate from the separately registered training/inference integration benchmark. A separately registered four-case development smoke completed for this candidate and achieved 0/4 strict placements. It remains unqualified.
+
+The two candidates share the same 269-input, two-layer 64-unit tanh network, feature normalization, seeded initialization and optimizer recipe. Training uses 96,000 rows from 64 authored-teacher simulation demonstrations and 32 earlier mixed-control correction episodes, all within the original training split. Both candidates are retained; no model selection was performed.
+
+Inputs include an explicitly historical initial visual estimate, destination, elapsed time, joint/gripper measurements, encoder-derived tool pose, previous applied commands and 160 ms of causal observation history with availability masks and age. Current teacher commands, teacher phase, native object/contact truth and future observations are excluded. Exact float64 prior normal servo references are separate decoder state; they are not reconstructed from float32 features.
+
+The labels are reachable normal-reference endpoints computed by the original 20 limiter updates. Mixed-control teacher queries are counterfactual supervision; they are not relabeled as commands that were actually executed. This experiment does not recover actions from ordinary human videos.
+
+The decoder is `r0 + D*g(x)`. `r0` is the fixed reset reference; `r` is the actual retained previous normal servo reference. `D` is 0.03 rad per joint and 0.001 m for the single-finger jaw coordinate. Runtime projection uses native bounds and the original 20 normal reference updates. Low training error does not establish stable feedback behavior.
+
+## Recorded integration measurements
+
+| Measurement | Value |
+|---|---:|
+| Training rows | 96,000 |
+| Fit wall time | 5.230713 s |
+| Model JSON bytes | 539,345 |
+| Initial training loss | 0.0400907266332 |
+| Final training loss | 7.9261088837e-06 |
+| Open-loop callback median | 0.181813 ms |
+| Open-loop callback p95 | 0.204840 ms |
+| Serialized policy outputs | Bitwise identical at all 1,000 replay steps |
+
+One fit used seed 17, 50 epochs, batch size 512 and Adam learning rate 0.003. Loss is the mean squared unprojected endpoint error normalized by scales of 3 rad per joint and 0.035 m for the jaw; it is a training-set descriptive metric. The loss includes no task-success scorer.
+
+The 1,000-step replay follows the first protocol-ordered training teacher trajectory. Every next observation and reference remains the recorded teacher value. Predictions never enter simulation or feed the next state. Callback timing includes causal encoding, NumPy inference and reference projection; it excludes camera acquisition, rendering, physics and transport. Fit time includes source/derived-input validation and normalization. The shared host ran macOS 26.5.2, arm64, Python 3.12.9 and NumPy 2.5.3 with declared BLAS threads 1. Whole-process peak RSS across both fits and replays was 1,345,404,928 bytes; this is not a hardware minimum or model-only memory requirement.
+
+## Artifact and source identity
+
+- Artifact: [model.json](model.json), copied byte-for-byte from the retained integration result.
+- File SHA-256: `3dbf6911d92a9072297c9c3bb7c09f45809808272455ffd5562f4553a173f362`.
+- Canonical model content SHA-256: `36af3fe1670494b04c48b5114d9be1a5f91632aa7992ba529d6587969ea37460`.
+- [Pinned source](https://github.com/Dvidia-Inference/dvidia-training/tree/78ea4fbb4b2733e7738eb425eeecd7cb67a52195); model source identity `12ea9757cb0d76573c2e67f80b56ab08271f95e43f18b344b6481cc9153d70bc`.
+- [Integration receipt](../../evidence/integration-result.json) and [training input identity](../../evidence/training-input-identity.json).
+
+The Python API is `dvidia_training.camera_action_reference_model.load_model` / `ModelPolicy` at the pinned source. Existing qualified-skill installers and arbitrary-arm deployment are not validated for this new model type. The model's `scientific_registration` is intentionally null; the external benchmark registration identifies this operational integration test.
+
+## Native evaluation and limitations
+
+Two earlier eight-attempt batches remain retained separately: import-guard refusals, then CoreGraphics renderer refusals. A tiny static-frame preflight isolated the graphics access requirement. A third, separately registered batch used the same models, source, cases, cadence and guards with scoped graphics access. All eight 20-second simulated attempts completed, four per candidate, with zero teacher queries or fallbacks. No training or parameter selection followed these outcomes.
+
+| Native development measurement | This candidate |
+|---|---:|
+| Complete independently checked attempts | 4/4 |
+| Strict placements | 0/4 |
+| Bilateral grasp contact seen | 3/4 |
+| Qualified lift seen | 1/4 |
+| Carry completed | 0/4 |
+| Episodes with unexpected contacts | 2/4 |
+| Unexpected-contact native ticks | 7,606 |
+| Available visual estimates | 2,396/4,000 (59.9%) |
+| Longest missing visual interval | 13.36 s |
+| Callback including storage guard p95 | 0.295460 ms |
+| Full offline boundary p95 | 22.362537 ms |
+
+The absolute candidate reached a qualified lift once, but no carry or placement. Two episodes had unexpected contacts.
+
+These are four development cases per model, with no fixed/teacher/blind comparator, negative controls or uncertainty intervals in this engineering batch. They cannot establish generalization or relative superiority. Zero recorded retention-loss events cannot establish grip reliability when so few episodes reach the scorer's post-lift window.
+
+The shared-host callback distribution includes the storage guard; the model's diagnostic timer excludes it. Full-boundary wall timing includes file writes and alternate camera/tracker work. The simulator pauses for that work; this is not a real-time robot delivery measurement. The renderer warned that `ARB_clip_control` was unavailable for the requested depth mode, limiting depth accuracy. This backend was not depth-calibrated by the smoke test.
+
+[Exact eight-attempt audit](../../evidence/native-smoke-v3-audit.json), [all three batches](../../evidence/development-smoke-status.json), [renderer warnings](../../evidence/renderer-warnings.json) and [graphics preflight](../../evidence/graphics-preflight.json) retain the evidence and limitations.
+
+No confirmation-set evaluation, generalization claim, candidate selection, physical robot action or qualification has occurred. The frozen v0.1 result and the proposed 164-attempt screen remain unchanged. This model applies only to the bounded simulated PiPER block-placement setup and its exact sensing/reference contracts.
+
+## License
+
+DVIDIA candidate weights and accompanying DVIDIA code/documentation are offered under [MIT](../../LICENSE). Upstream dependencies and robot assets retain their own licenses; this benchmark does not bundle them.
diff --git a/benchmarks/camera-action-reference-model-integration-v01/models/absolute/model.json b/benchmarks/camera-action-reference-model-integration-v01/models/absolute/model.json
new file mode 100644
index 0000000..60064fb
--- /dev/null
+++ b/benchmarks/camera-action-reference-model-integration-v01/models/absolute/model.json
@@ -0,0 +1 @@
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diff --git a/benchmarks/camera-action-reference-model-integration-v01/models/delta/README.md b/benchmarks/camera-action-reference-model-integration-v01/models/delta/README.md
new file mode 100644
index 0000000..fe4f6be
--- /dev/null
+++ b/benchmarks/camera-action-reference-model-integration-v01/models/delta/README.md
@@ -0,0 +1,81 @@
+---
+license: mit
+language:
+- en
+tags:
+- robotics
+- simulation
+- numpy
+- research-candidate
+---
+
+# DVIDIA PiPER reference model — Cdelta
+
+**Research candidate; not a qualified skill or hardware controller.** This card documents the retained `delta` candidate from the separately registered training/inference integration benchmark. A separately registered four-case development smoke completed for this candidate and achieved 0/4 strict placements. It remains unqualified.
+
+The two candidates share the same 269-input, two-layer 64-unit tanh network, feature normalization, seeded initialization and optimizer recipe. Training uses 96,000 rows from 64 authored-teacher simulation demonstrations and 32 earlier mixed-control correction episodes, all within the original training split. Both candidates are retained; no model selection was performed.
+
+Inputs include an explicitly historical initial visual estimate, destination, elapsed time, joint/gripper measurements, encoder-derived tool pose, previous applied commands and 160 ms of causal observation history with availability masks and age. Current teacher commands, teacher phase, native object/contact truth and future observations are excluded. Exact float64 prior normal servo references are separate decoder state; they are not reconstructed from float32 features.
+
+The labels are reachable normal-reference endpoints computed by the original 20 limiter updates. Mixed-control teacher queries are counterfactual supervision; they are not relabeled as commands that were actually executed. This experiment does not recover actions from ordinary human videos.
+
+The decoder is `r + D*g(x)`. `r0` is the fixed reset reference; `r` is the actual retained previous normal servo reference. `D` is 0.03 rad per joint and 0.001 m for the single-finger jaw coordinate. Runtime projection uses native bounds and the original 20 normal reference updates. Low training error does not establish stable feedback behavior.
+
+## Recorded integration measurements
+
+| Measurement | Value |
+|---|---:|
+| Training rows | 96,000 |
+| Fit wall time | 5.097628 s |
+| Model JSON bytes | 538,021 |
+| Initial training loss | 8.72223173973e-05 |
+| Final training loss | 3.53132778774e-06 |
+| Open-loop callback median | 0.182729 ms |
+| Open-loop callback p95 | 0.207383 ms |
+| Serialized policy outputs | Bitwise identical at all 1,000 replay steps |
+
+One fit used seed 17, 50 epochs, batch size 512 and Adam learning rate 0.003. Loss is the mean squared unprojected endpoint error normalized by scales of 3 rad per joint and 0.035 m for the jaw; it is a training-set descriptive metric. The loss includes no task-success scorer.
+
+The 1,000-step replay follows the first protocol-ordered training teacher trajectory. Every next observation and reference remains the recorded teacher value. Predictions never enter simulation or feed the next state. Callback timing includes causal encoding, NumPy inference and reference projection; it excludes camera acquisition, rendering, physics and transport. Fit time includes source/derived-input validation and normalization. The shared host ran macOS 26.5.2, arm64, Python 3.12.9 and NumPy 2.5.3 with declared BLAS threads 1. Whole-process peak RSS across both fits and replays was 1,345,404,928 bytes; this is not a hardware minimum or model-only memory requirement.
+
+## Artifact and source identity
+
+- Artifact: [model.json](model.json), copied byte-for-byte from the retained integration result.
+- File SHA-256: `fece6d056e50a1bde41bdb9960bf7cbc81d983c4bfdb03a07632e30b712b1489`.
+- Canonical model content SHA-256: `4545be31f50267c9da29b211510c7a32df500678ba107c7a100604bb0c4c18ab`.
+- [Pinned source](https://github.com/Dvidia-Inference/dvidia-training/tree/78ea4fbb4b2733e7738eb425eeecd7cb67a52195); model source identity `12ea9757cb0d76573c2e67f80b56ab08271f95e43f18b344b6481cc9153d70bc`.
+- [Integration receipt](../../evidence/integration-result.json) and [training input identity](../../evidence/training-input-identity.json).
+
+The Python API is `dvidia_training.camera_action_reference_model.load_model` / `ModelPolicy` at the pinned source. Existing qualified-skill installers and arbitrary-arm deployment are not validated for this new model type. The model's `scientific_registration` is intentionally null; the external benchmark registration identifies this operational integration test.
+
+## Native evaluation and limitations
+
+Two earlier eight-attempt batches remain retained separately: import-guard refusals, then CoreGraphics renderer refusals. A tiny static-frame preflight isolated the graphics access requirement. A third, separately registered batch used the same models, source, cases, cadence and guards with scoped graphics access. All eight 20-second simulated attempts completed, four per candidate, with zero teacher queries or fallbacks. No training or parameter selection followed these outcomes.
+
+| Native development measurement | This candidate |
+|---|---:|
+| Complete independently checked attempts | 4/4 |
+| Strict placements | 0/4 |
+| Bilateral grasp contact seen | 4/4 |
+| Qualified lift seen | 0/4 |
+| Carry completed | 0/4 |
+| Episodes with unexpected contacts | 4/4 |
+| Unexpected-contact native ticks | 19,751 |
+| Available visual estimates | 2,148/4,000 (53.7%) |
+| Longest missing visual interval | 6.16 s |
+| Callback including storage guard p95 | 0.284297 ms |
+| Full offline boundary p95 | 22.049043 ms |
+
+The delta candidate made bilateral contact in all four episodes but never reached a qualified lift. All four had unexpected contacts and the strict trajectory scorer returned `invalid_state_value`: finite tool-centre coordinates exceeded its unchanged component envelope of ±0.5 m. These were not NaNs or solver warnings. Native metric consistency alone does not imply that the strict trajectory contract passed. [Exact diagnosis](../../evidence/native-smoke-diagnosis.json).
+
+These are four development cases per model, with no fixed/teacher/blind comparator, negative controls or uncertainty intervals in this engineering batch. They cannot establish generalization or relative superiority. Zero recorded retention-loss events cannot establish grip reliability when so few episodes reach the scorer's post-lift window.
+
+The shared-host callback distribution includes the storage guard; the model's diagnostic timer excludes it. Full-boundary wall timing includes file writes and alternate camera/tracker work. The simulator pauses for that work; this is not a real-time robot delivery measurement. The renderer warned that `ARB_clip_control` was unavailable for the requested depth mode, limiting depth accuracy. This backend was not depth-calibrated by the smoke test.
+
+[Exact eight-attempt audit](../../evidence/native-smoke-v3-audit.json), [all three batches](../../evidence/development-smoke-status.json), [renderer warnings](../../evidence/renderer-warnings.json) and [graphics preflight](../../evidence/graphics-preflight.json) retain the evidence and limitations.
+
+No confirmation-set evaluation, generalization claim, candidate selection, physical robot action or qualification has occurred. The frozen v0.1 result and the proposed 164-attempt screen remain unchanged. This model applies only to the bounded simulated PiPER block-placement setup and its exact sensing/reference contracts.
+
+## License
+
+DVIDIA candidate weights and accompanying DVIDIA code/documentation are offered under [MIT](../../LICENSE). Upstream dependencies and robot assets retain their own licenses; this benchmark does not bundle them.
diff --git a/benchmarks/camera-action-reference-model-integration-v01/models/delta/model.json b/benchmarks/camera-action-reference-model-integration-v01/models/delta/model.json
new file mode 100644
index 0000000..78ef6d1
--- /dev/null
+++ b/benchmarks/camera-action-reference-model-integration-v01/models/delta/model.json
@@ -0,0 +1 @@
+{"biases":[[0.8344941735267639,0.06328237801790237,-1.3936142921447754,-0.1891794502735138,0.6053096055984497,-0.871947169303894,-0.5468690395355225,-0.6041911244392395,-0.5152680277824402,0.6523433923721313,-0.9618356823921204,-0.36622345447540283,0.5110186338424683,-0.09083669632673264,-0.21533982455730438,-0.38188716769218445,0.26444151997566223,0.8572641015052795,0.16807043552398682,0.4740249216556549,0.44550439715385437,-0.2643808126449585,-0.5029984712600708,-0.44186335802078247,1.1155720949172974,0.14021272957324982,-0.7486647963523865,0.4387836158275604,0.6133492588996887,0.5872907638549805,0.6645246148109436,0.6072654128074646,-0.06526551395654678,-0.15883544087409973,-0.580084502696991,0.03087075985968113,-0.3331230580806732,-0.137029767036438,0.03266145661473274,-0.3689347207546234,0.034594904631376266,-0.13498377799987793,0.2610134184360504,0.04801025241613388,0.50114506483078,0.4417096674442291,0.06653057783842087,0.15951454639434814,0.1778927594423294,-0.18314753472805023,0.12694545090198517,-0.13949382305145264,-0.11109958589076996,0.07651648670434952,-0.5495805144309998,-0.2471318542957306,0.08748144656419754,0.03653131425380707,-0.25078970193862915,0.7722663283348083,-0.6403623223304749,0.09202545881271362,-0.0268339142203331,-0.20263898372650146],[0.5144826173782349,-0.07748149335384369,0.028832007199525833,0.07797940820455551,-0.17254336178302765,0.02285468950867653,-0.0056102522648870945,-0.13990838825702667,0.17016035318374634,-0.08520711958408356,-0.02454543113708496,0.18643665313720703,-0.2728710174560547,0.1744621992111206,-0.3754167854785919,-0.3515677750110626,0.09368984401226044,0.2524408996105194,-0.3548276126384735,0.4777960777282715,-0.3011910021305084,0.12320384383201599,0.0565575510263443,0.22645026445388794,-0.10156633704900742,-0.07545154541730881,-0.1038484051823616,-0.021801691502332687,0.5970119833946228,-0.28763216733932495,-0.170108824968338,-0.10921932011842728,0.05920444428920746,0.10764876753091812,0.1129513680934906,0.042329564690589905,-0.6890668272972107,0.050356216728687286,-1.1141653060913086,0.4348828196525574,0.32719793915748596,0.17547449469566345,0.20878371596336365,-0.003216942073777318,-0.08243503421545029,-0.17994379997253418,0.042545221745967865,0.09492696076631546,0.0006631085998378694,0.3182961046695709,-0.0840076357126236,-0.04080016911029816,0.5733478665351868,-0.06309384852647781,-0.2574348449707031,-0.40506142377853394,0.5252352356910706,-0.1488911360502243,0.07967004179954529,-0.8686919212341309,-0.16724459826946259,0.35251155495643616,-0.029310740530490875,0.18478815257549286],[0.07683466374874115,0.015300491824746132,0.02172534540295601,-0.03835979476571083,0.13131830096244812,0.04406687617301941,0.0023407859262079]],"contract":{"absolute_decoder":"r0 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diff --git a/docs/camera-action-reference-model-integration-v01.md b/docs/camera-action-reference-model-integration-v01.md
new file mode 100644
index 0000000..1c72864
--- /dev/null
+++ b/docs/camera-action-reference-model-integration-v01.md
@@ -0,0 +1,36 @@
+# Reference-model integration results
+
+Two reference-model candidates now complete the demonstration-to-action training, serialization and native-policy interface. Neither completes this task reliably: the separately registered development smoke achieved **0/4 strict placements for each candidate**. Both files and all failures are retained as unqualified research artifacts.
+
+| Measurement | Absolute reference (Cabs) | Prior-reference delta (Cdelta) |
+|---|---:|---:|
+| Training rows | 96,000 | 96,000 |
+| Fit wall time | 5.230713 s | 5.097628 s |
+| Model JSON bytes | 539,345 | 538,021 |
+| Recorded-trajectory callback p95 | 0.204840 ms | 0.207383 ms |
+| Save/load output parity | 1,000/1,000 bitwise equal | 1,000/1,000 bitwise equal |
+| Complete native development episodes | 4/4 | 4/4 |
+| Bilateral grasp contact seen | 3/4 | 4/4 |
+| Qualified lift seen | 1/4 | 0/4 |
+| Carry completed / strict placement | 0/4 / 0/4 | 0/4 / 0/4 |
+| Episodes with unexpected contacts | 2/4 | 4/4 |
+| Unexpected-contact native ticks | 7,606 | 19,751 |
+| Visual estimate coverage | 2,396/4,000 (59.9%) | 2,148/4,000 (53.7%) |
+| Longest missing visual interval | 13.36 s | 6.16 s |
+| Native callback plus guard p95 | 0.295460 ms | 0.284297 ms |
+| Full offline boundary p95 | 22.362537 ms | 22.049043 ms |
+| Qualification | No | No |
+
+The common recipe is seed 17, two 64-unit tanh hidden layers, 50 epochs, batch 512 and Adam learning rate 0.003. Both fits use the same sealed 64,000 authored-teacher demonstration rows plus 32,000 earlier mixed-control correction rows, normalization and initial parameters. No new demonstrations, confirmation data, model selection or parameter tuning were used.
+
+The 1,000-step integration replay is open loop along one recorded **training** teacher trajectory. Predictions never determine its later observations or references. The separate native smoke uses the first two protocol-ordered development groups, both destinations and both unchanged models, with no teacher query, intervention or fallback. All eight completed 1,000 control intervals with 501 RGB-D pairs per episode. Full artifact bytes, authority, timing contracts and strict scores were independently checked offline.
+
+Native metric consistency passed for all eight episodes. That is distinct from the strict trajectory contract: all four delta episodes additionally return `invalid_state_value` because finite tool-centre coordinates cross its unchanged ±0.5 m component envelope. No NaN or native physics warning caused those rejections. Cabs achieved one qualified lift, but neither model completed carry or placement. Zero post-lift retention-loss events is not evidence of dependable grip when only one episode reaches qualified lift. Long visual dropouts and unexpected contacts remain important observed failure signals; these observations do not establish a causal diagnosis or justify selecting a model from four cases.
+
+The original first eight attempts were refused during MuJoCo's macOS `sysctl` import probe. A separately registered startup correction passed that probe but the next eight were refused by CoreGraphics. A tiny static-frame preflight isolated graphics-session access; the third batch changed only the execution access, preserving source/models/cases/guards. All sixteen infrastructure refusals remain separate, rather than becoming a misleading 0/24 manipulation score.
+
+Timing is measured on a shared macOS 26.5.2 arm64 host with Python 3.12.9, NumPy 2.5.3 and declared BLAS threads 1. Fit includes source/derived-input validation and normalization. Whole-process integration peak RSS was 1,345,404,928 bytes. Callback timing includes encoding, inference and reference projection; the native callback also includes its storage guard. Full-boundary timing includes file writes and alternate camera/tracker work, excluding the separate initial acquisition. The simulator pauses for this work. Its 22 ms p95 boundary is not a 50 Hz real-time delivery claim or a physical hardware measurement. The renderer warns that missing `ARB_clip_control` limits depth accuracy for its requested depth mode; no backend depth calibration was established.
+
+This is an eight-attempt engineering smoke, not the proposed 164-attempt v0.2 screen. It contains no fixed, teacher or blind comparator; no negative-control qualification; and no generalization or confidence-interval claim. Frozen v0.1 remains 5/64 strict learned placements, unqualified.
+
+[Benchmark, both model files, cards and evidence](../benchmarks/camera-action-reference-model-integration-v01/README.md) contain source, model and dataset identities. The candidates load through the Python `camera_action_reference_model` API. They are not installable through the qualified-skill CLI and provide no hardware interface. Training reproduction additionally requires the original independently pinned data exports; the compact benchmark does not redistribute those arrays or raw recordings.
diff --git a/docs/camera-action-reference-model-v01.md b/docs/camera-action-reference-model-v01.md
index 8ce3f85..391dddc 100644
--- a/docs/camera-action-reference-model-v01.md
+++ b/docs/camera-action-reference-model-v01.md
@@ -2,17 +2,24 @@
 
 This additive Python module connects the verified training export to a trainable
 model and a callable local simulation policy. It implements the proposed Cabs
-and Cdelta comparison; **no real-study model has been fitted or evaluated with
-it**. The frozen v0.1 result remains 5/64 strict learned placements and
-unqualified. The common-scale parameterization and the 164-attempt v0.2 screen
-still need registration before real learning or rollouts.
+and Cdelta comparison. A separately registered [training/inference integration
+benchmark](camera-action-reference-model-integration-v01.md) has now fitted both
+variants on the same 96,000 training rows and verified save/load replay parity.
+Sixteen initial engineering attempts were refused before policy callbacks. A
+separately registered eight-attempt native development batch then completed;
+both unchanged models achieved 0/4 strict placements and remain unqualified. The frozen v0.1 result
+remains 5/64 strict learned placements and unqualified. The proposed 164-attempt
+v0.2 screen remains unrun and needs its own registration.
 
 The module is `dvidia_training.camera_action_reference_model`. It changes none
 of the existing recorder, trainer, model, installer, actuator limits or scorer.
 It provides no hardware commands, new CLI command or installation qualification.
 Its policy has the same callable interface that the unchanged
 `camera_action_capture.run_rollout` already accepts. That interface compatibility
-is tested with synthetic contexts; no native rollout was run for this bridge.
+is tested with synthetic contexts and one recorded-trajectory integration replay.
+A later eight-attempt native development batch exercised learned control with
+zero strict placements. Its results and all earlier initialization refusals are
+linked from the integration report.
 
 ## From sealed training inputs to actions
 
@@ -96,7 +103,7 @@ Models remain `physical_robot_ready=false`, `task_qualified=false`, with
 `scientific_registration=null`. A model file is not an experiment registration
 or a qualification receipt, and these hashes are not publisher authentication.
 
-For a future separately registered experiment, the API is:
+For a separately registered training experiment, the API is:
 
 ```python
 from dvidia_training import camera_action_reference_model as reference_model
@@ -136,7 +143,8 @@ bitwise cross-variant reset parity and saved-model inference parity. These are
 implementation tests, not task benchmarks. Synthetic fit losses are not
 evidence of manipulation progress.
 
-Before the real v0.2 screen: register this parameterization and source, verify
+The bounded integration benchmark does not replace the real v0.2 screen. Before
+that screen: register this parameterization and source, verify
 shared initialization on the registered reset contexts, bind the exact dataset
 and resource budgets, and then run the declared fits and fresh closed-loop
 attempts with independent scoring. Existing confirmation data must remain