fix(tests): fix 3 flaky tests and 2 never-run sweeps (DBN, CompiledMlp, Integration D OOM)

[ooples]

Fixes three flaky/failing tests and two Sweep tests that had never run to completion. Every number below is measured on this branch; the "before" numbers come from the unmodified origin/master tree.

StyDiff is NOT fixed — see the dedicated section. Its root cause was not found, and its tolerance was deliberately left alone.

Builds clean on all three test target frameworks: net10.0, net8.0 (14 m 55 s, 13,415 warnings) and net471 (11 m 24 s, 13,327 warnings), 0 errors each — all real builds, not incremental no-ops.

---

1. DeepBeliefNetworkTests.LossStrictlyDecreasesOnMemorizationTask — fixed

CI: run 34596861846 failed with step 1=0.472564, step 100=0.490819, and passed on the automatic retry.

Failure rate. Before: 0/30 local failures on a warm box (the local rate understates it — the box hides the nondeterminism, see below), 1 CI failure that passed on retry. After: 30/30 runs green in 30 separate processes, with four seeded trials bit-identical to each other (same parameter sums, same loss at every step). Full DeepBeliefNetworkTests class 29/29; every other RBMLayer consumer (DeepBoltzmannMachine, RestrictedBoltzmannMachine, RBMLayerLazyCtorIssue1213Tests, layer helpers) 331/331.

Two independent root causes:

1a. Nondeterminism — src/NeuralNetworks/Layers/RBMLayer.cs:958

SampleBinaryStatesTensor drew its Gibbs samples from Tensor<T>.CreateRandom(shape) — the process-shared RNG — ignoring the layer's RandomSeed. Even a fully seeded DBN therefore pre-trained differently on every run. It now draws from LayerBase.Random (the seeded stream when RandomSeed is set, the thread-local production stream otherwise).

The fixture also constructed the network unseeded, so each RBM's Glorot init came from CreateSecureRandom. It now pins construction through LayerInitializationSeedScope.AmbientFallbackSeed (tests/AiDotNet.Tests/ModelFamilyTests/NeuralNetworks/DeepBeliefNetworkTests.cs:22), the same [ThreadStatic], parallel-safe pattern SpikingNeuralNetworkTests and SpiralNetTests already use.

1b. The training was never genuinely decreasing — src/NeuralNetworks/DeepBeliefNetwork.cs:261

Heavy-ball momentum is stable only while lr · λ_max < 2(1 + β), i.e. < 3.8 at β = 0.9. For the linear supervised head over the top RBM's sigmoid features h, the squared-error Hessian's largest eigenvalue is λ_max = 2(|h|² + 1). With the default 2000-unit top RBM the measured |h|² after pre-training is 325–384 (mean activation ≈ 0.41), so λ_max ≈ 650–770:

lr	lr · λ_max	Behaviour over 100 steps, 6 unseeded initialisations
0.01 (old default)	6.5–7.7 — outside the 3.8 bound	6/6 reached ~1e-5 then rebounded by 2–4 orders of magnitude (e.g. 5.1e-5 at step 10 → 0.153 at step 50; 1e-6 → 0.30; 6.0e-4 → 0.025)
0.003	1.9–2.2	converges, but still shows tenfold bumps (0.012 at step 5 → 0.098 at step 10)
0.001 (new default)	0.65–0.77, and ≤ 4.0 even if all 2000 sigmoids saturated at 1	6/6 descend to ≤ 1.1e-5 by step 100, minimum in the last 16 steps

Every trial at lr 0.01 "passed" only because the assertion compares step 100 against step 1; the invariant passed or failed purely on where step 100 happened to land on a rebound. That is exactly the CI datapoint 0.4726 → 0.4908.

The constructor comment now carries this derivation. lr 0.1 (the CD-1 pre-training rate) is far outside the region and is already excluded by Training_ShouldReduceLoss.

Blast radius. Callers that pass their own optimizer are unaffected. The two other DeepBeliefNetwork constructions in the suite assert only Predict output and parameter count. The sampling change reaches every RBMLayer consumer; unseeded behaviour is statistically unchanged (thread-local production stream instead of the process-shared one).

---

2. BuilderJitValueStabilityTests — fixed

CI: run 34561861362, job 103164537825 (Integration N-O), AFeedForwardNetworkIsAccepted_SoJitIsNotSilentlyDisabled at line 272: the compiled plan disagrees with the eager forward: 3.213E+000 against 3.214E+000.

The JIT was right and eager Predict was wrong

This is the important part of the diagnosis. Instrumenting both paths showed the compiled plan agreeing with the per-layer forward to ~2e-6, while eager model.Predict disagreed with the per-layer forward, with MlpForward, and with a fresh network holding identical parameters by 1.4e-2, 2.6e-2 and 2.7e-2 in 3 of 3 trials.

Root cause — src/NeuralNetworks/FeedForwardNeuralNetwork.cs:375-388. FeedForwardNeuralNetwork caches a CompiledMlp plan that pins the weight and bias arrays it was built from, and rebuilt it only when a weight array identity changed. AiModelBuilder training replaces the hidden layers' bias storage while keeping the weight arrays, so after BuildAsync the plan kept serving the pre-training biases. Verified directly: planB same array=False maxdiff=5.18e-3 per hidden layer, while planW same array=True maxdiff=0.0.

The test's pairwise-L2 metric could not see this: a stale bias shifts every output by the same amount and leaves every pairwise distance unchanged. It only failed when ReLU made the shift input-dependent, which is why it was rare.

Fix. Track bias identity alongside weights, and pin only a tensor's live backing array.

The first attempt broke copy-on-write isolation

My first version pinned arrays via the non-privatizing GetLiveBackingArrayOrNull(). That removed a side effect the old code depended on: GetDataArray() privatizes a copy-on-write share before handing the array out, and the optimizer's fast path writes updates straight into live backing storage without privatizing. With the non-privatizing accessor, a DeepCopy taken before BuildAsync drifted by 5.0e-3 to 6.1e-3 while the original trained (it had been 0.0 before my change).

The committed version therefore keeps GetDataArray() as the accessor and adds a live-array check on top (declining the CompiledMlp path when the returned array is not the tensor's live storage, e.g. a ToArray snapshot of a non-simple layout). Measured after the committed fix: copy drift 0.0 both with and without a Predict before the copy, while the source moved 5.02e-3.

Test strengthened (BuilderJitValueStabilityTests.cs:280,314): an element-wise comparison of the two paths, bound 1e-4. Measured ≤ 4.3e-6 after the fix, against 1.4e-2–2.7e-2 before — a bound the pairwise spread cannot enforce.

Failure rate. Before: 30/30 green locally (only CI caught it). After: 30/30 runs green across 30 separate processes, 240 tests, 0 failed, including the new assertion and the FeedForwardCompiledMlpPredictTests parity tests. Eager vs per-layer forward after BuildAsync: 1.4e-2 … 2.7e-2 → 0.0.

---

StyDiff Clone_ShouldProduceIdenticalOutput — NOT FIXED, root cause not found

No code in this PR addresses it, and the existing CloneOutputRelativeTolerance = 1.5e-5 override was deliberately left untouched (it is itself an earlier accommodation of this same unexplained gap).

Three CI datapoints, all Linux, 4 cores, net10.0:

Run	Job	Reported
34561861362	103164533681 (ModelFamily - Diffusion Step-Sync)	Clone() output[69] = 8.333355E-001 differs from 8.333587E-001 by 2.324581E-005, which exceeds its own tolerance 2.250038E-005
34169952543	101892753706	2.479553e-05 against tolerance 1.773868e-05 (passed on rerun)
33979199705	101345748747	1.275539e-05 against tolerance 1.252999e-05

Elimination list — everything I ruled out, with measurements:

• Different weights: no. The clone shares copy-on-write storage with the original.
• Local reproduction, warm machine: 30/30 passed.
• Cold autotune cache (AIDOTNET_AUTOTUNE_CACHE_PATH pointed at a fresh empty directory per run) at DOTNET_PROCESSOR_COUNT=4: 20/20 passed.
• Thread-pool contention (12 trials with 4 busy background workers, cold cache): 0/12 trials showed any nonzero difference. Original vs clone, clone vs re-run, original vs a freshly constructed model — all exactly 0.0.
• Processor count 2 and 4: no difference.
• Deterministic mode: already enabled assembly-wide (TestAssemblyDeterminismInit.cs:39), so it was on for every failing CI run too.
• Full Diffusion Step-Sync shard replay on a cold cache: a different test (Step1XEditModelTests.Training_ShouldReducePredictionError) hit its 120 s timeout and the host then wedged, so this replay never reached a StyDiff verdict.

The GEMM-strategy theory, and its decisive disproof. I traced a plausible mechanism in AiDotNet.Tensors: Dispatcher.SelectStrategy lets a learned, timed strategy override its per-hardware seed table, and BackgroundAutotuner times Streaming / PackAOnly / PackBoth for each shape on a background thread and stores the winner mid-run — which would let two identical calls straddle a strategy switch. I tested the precondition directly by running the same operands through all three forced strategies:

GEMM 256x32x32:   Streaming-PackAOnly=0.00E+000(0 elems)  Streaming-PackBoth=0.00E+000(0 elems)  PackAOnly-PackBoth=0.00E+000(0 elems)
GEMM 256x64x64:   ... 0 elems ...      GEMM 64x128x128:  ... 0 elems ...
GEMM 256x128x288: ... 0 elems ...      GEMM 77x64x768:   ... 0 elems ...
GEMM 256x256x64:  ... 0 elems ...      GEMM 1024x32x36:  ... 0 elems ...

Bit-identical across all 7 shapes probed, 0 differing elements. Strategy selection cannot change numerics, so the theory is dead. I had staged a test-harness switch disabling the background autotuner; when the probe disproved the mechanism I reverted it rather than commit an unproven change. It is not in this PR.

The remaining untested variable is Linux vs Windows. Every CI failure is on Linux 4-core; all my reproduction attempts ran on Windows. I could not reach that variable from this environment, so the root cause is still open.

---

3. Integration D shard OOM — fixed

Master's Build & SonarCloud run 34561803988 lost the Tests (net10.0) - Integration D job (103164792718) to The runner has received a shutdown signal, with MemAvailable falling from 14.3 GB to 0.23 GB. This was never a test assertion — it was an out-of-memory kill.

Root cause. PixelToSequenceDocumentTests shrank only the image and kept every other paper default, in double precision. MATCHA at 1536-wide, 18 + 18 layers with a 50,265-token vocabulary is ~1.3 B parameters ≈ 10.7 GB of weights; Dessurt ~0.5 B ≈ 3.9 GB.

Measurement	Before	After
Dessurt_Predict alone (testhost peak)	16.07 GB	≤ 1.7 GB
MATCHA_Predict alone	23.9 GB (and it throws, see "does not do" #1)	≤ 1.7 GB
Donut_Predict alone	1.71 GB	≤ 1.7 GB
Whole Integration D shard	26.35 GB	11.17 GB
Shard result	job killed	2093 passed, 28 skipped, 0 failed

PixelToSequenceDocumentTests itself: 3/3 runs green, 48 tests.

Fix. One test-scale factory per model: the paper's layer types, encoder/decoder split and patch geometry preserved (Donut keeps its four Swin stages, since LayerHelper enforces exactly four and feeds the decoder embedDim * 8), at 64-wide 2-layer stacks with a 256-token vocabulary. None of these tests depends on paper-scale capacity — they check construction, the forward contract and metadata.

Remaining headroom risk, stated plainly: 11.17 GB against roughly 14 GB free on the runner. That is about 3 GB of margin, so this shard is not comfortably safe yet. No heavy test is live during the final climb to that peak — the log ends 1 line later — and the heaviest remaining allocations are still-paper-scale metadata tests: LayoutXLM 15 s, InfographicVQA 16 s, LayoutLMv3 12 s. I did not change them, because no failure currently justifies it. If Integration D fails on memory again, those are the next candidates.

---

4. Sweep tests that had never run — both now pass

Sweep	Before	After (2 workers, the 4-vCPU default)
ModelFamilyLawTests.DoTheMembersOfAFamilyShareOneShapeLaw	30-minute xUnit Timeout inside its first family (AudioNeuralNetworkBase, 207 models), followed by an unhandled InvalidCastException in Task.RunContinuations from the abandoned work	8 m 01 s, passes
ModelShapeDiscoveryProbeTests	never run to completion	1 m 37 s, passes

Why it was slow. Both constructed hundreds of models by reflection inside the xUnit process — the law sweep at paper-scale constructor defaults, with no per-model bound. Its per-family budget also counts only successful members, so a family whose members mostly skip is walked end to end at full construction cost.

Fix. Each model is now built and probed in its own AiDotNet.ParameterSweepWorker process (new observe command) with a 1 GB managed heap (DOTNET_GCHeapHardLimit) and a per-model deadline (ADNSHAPE_MODEL_TIMEOUT_SECONDS, default 180) — exactly the isolation the model-contract conformance sweep already uses; options parameters resolve through TinyForTests like the other two sweeps. Members are observed a bounded batch at a time (ADNSHAPE_WORKERS, default min(4, cores/2)) but consumed in the same alphabetical order, so the selected rows are the ones the sequential loop chose. The worker only observes; all analysis stays in the tests, so the measurement is unchanged.

Per-family breakdown (law sweep, 481 s of work total):

Family	Members observed	Time	Probed	Skipped	Output ranks
AudioNeuralNetworkBase	31/207	54 s	10	21	4
VisionLanguageModelBase	170/170	300 s	4	166	4
TtsModelBase	68/119	94 s	10	58	4
DeclaredModelLayoutBase	33/116	33 s	10	23	2, 3, 4

AudioNeuralNetworkBase — the family that previously consumed the entire 30-minute budget — now takes 54 s. VisionLanguageModelBase dominates the remainder because only 4 of its 170 members yield a complete profile set, so the sweep must walk the whole family.

Discovery sweep: 61 candidates observed, 40 probed, 20 confirmed, 1 parameterised, 19 unconfirmed (single profile available), 0 fits failed to reproduce their observations, 20 skipped, 1 self-inconsistent (APNet2: its own layers reject its declared rank). Slowest single profile: BandSplitRNN [1D-8] at 11.9 s.

Shard entries these two need — note for #2173

I did not edit .github/test-shards.yml; PR #2173 owns that file and is adding these. Proposed entries:

- name: Sweeps - Model shape law
  project: tests/AiDotNet.Tests/AiDotNetTests.csproj
  framework: net10.0
  filter: 'Category=Sweep&FullyQualifiedName~ModelFamilyLawTests'
  timeout: 25            # measured 8m01s locally at ADNSHAPE_WORKERS=2
  # env: ADNSHAPE_WORKERS=2, ADNSHAPE_MODEL_TIMEOUT_SECONDS=180
- name: Sweeps - Model shape discovery
  project: tests/AiDotNet.Tests/AiDotNetTests.csproj
  framework: net10.0
  filter: 'Category=Sweep&FullyQualifiedName~ModelShapeDiscoveryProbeTests'
  timeout: 15            # measured 1m37s
  # env: ADNSHAPE_WORKERS=2, ADNSHAPE_MODEL_TIMEOUT_SECONDS=180

Notes for whoever lands them: the defaults already resolve to workers = min(4, cores/2) = 2 on a 4-vCPU runner, so the env vars are explicit rather than required. Neither filter may use Category!=Sweep. Both need AiDotNet.ParameterSweepWorker built — its existing ProjectReference from the test project already guarantees that. If a single 8-minute job is too long, the law sweep splits cleanly per family via ADNSHAPE_FAMILY (AudioNeuralNetworkBase, VisionLanguageModelBase, TtsModelBase, DeclaredModelLayoutBase), where VisionLanguage alone is 300 s of the 481 s. The discovery sweep additionally honours ADNSHAPE_PROBE_BUDGET / ADNSHAPE_PROBE_OFFSET / ADNSHAPE_PROBE_NAMESPACE windows.

---

What this does not do

Eight things found along the way and deliberately not fixed here:

1. Tensors: CpuEngine.FusedLinear reads weights through the write-intent GetDataArray() → at paper scale MATCHA throws Cannot mutate a streaming tensor stored with a quantized inference encoding (bf16, int8, or int4) from EnsureOwnedForWrite. A read path should use GetReadOnlyDataArray(). This is what made the isolated MATCHA_Predict run fail at 23.9 GB.
2. Tensors: ConvTranspose3D forward and its kernel-gradient backward merge per-task buffers under a lock in thread-completion order, so their float sums are genuinely order-dependent (non-reproducible run to run).
3. AiModelResult.Predict returns the compiled plan's reused output buffer. CompiledInferencePlan pre-allocates and reuses all buffers and Execute() returns the same _finalOutput tensor each replay; the builder wrapper returns it without copying, so the next Predict of the same shape overwrites a tensor the caller still holds. My diagnostic hit this: 12 stored outputs collapsed to a single value.
4. Process-global determinism flag flipped from model constructors. MobileNetV2Network.cs:159 and MobileNetV3Network.cs:128 call BlasProvider.SetDeterministicMode(true), and AiModelBuilder / AiModelResult set it per build and per predict. Under xUnit's parallel execution that is a shared-state leak between unrelated tests.
5. Step-Sync shard health. In my replay, Step1XEditModelTests.Training_ShouldReducePredictionError hit its 120 s xUnit timeout (reported as 1 ms) and the host then wedged at 20.25 GB with zero CPU progress for 19+ minutes — the same "abandoned work after a timeout" pattern that produced the law sweep's InvalidCastException.
6. Other CI memorization flakes, surfaced by the log search and not investigated: ECAPATDNNSpeaker (0.438444 → 0.438423 over 100 steps), DeepAR (0.645553 → 0.748226 and 0.705280 → 0.781267 over 20 steps), AudioPaLM (2.412728 → 2.409396 over 2 steps, 4 jobs).
7. Discovery sweep coverage caveats. 9 models exceed the worker's 1 GB heap even at the tiny probe profile (Amphion, AquilaVL, Aria, AudioGenModel, AudioLDMModel, AudioLM, BASIC, BLIP3, BandSplitRNN); 9 more are rejected by their own Predict — ACEStep, APNet and Autoformer fail with IndexOutOfRangeException, which looks like a real defect rather than a probe limitation; ASTModel needs a longer signal than the probe's extent; 2 have no applicable construction profile (ACGAN, AdversarialImageEvaluator).
8. DBN pre-train / fine-tune input-domain mismatch. PreTrain normalizes its input to [0, 1] for the Bernoulli-Bernoulli RBM stack, but Train and Predict feed the raw input (measured range [-0.994, 0.980]) into the same layers, so the supervised path sees a domain the RBMs were never pre-trained on.

---

Verification summary

Check	Result
DBN memorization, 30 separate processes	30/30 green
DBN seeded reproducibility	4/4 trials bit-identical
Full DeepBeliefNetworkTests class	29/29
All other RBMLayer consumers	331/331
BuilderJit + FeedForward CompiledMlp, 30 processes	30/30 green (240 tests)
StyDiff Clone, cold cache, 4 procs	20/20 (root cause still unknown)
Copy-on-write clone isolation probe	copy drift 0.0, source moved 5.02e-3
PixelToSequence, 3 runs	3/3 green (48 tests)
Integration D shard	2093 passed, 28 skipped, 0 failed, peak 11.17 GB
Shape-law sweep	passes, 8 m 01 s
Shape-discovery sweep	passes, 1 m 37 s
Build net10.0 / net8.0 / net471	0 errors each

🤖 Generated with Claude Code

https://claude.ai/code/session_017otuSGr3GdmvPoYLbiaWR2

Distributed runtime blocker — timed follow-up 2026-09-12

Fixed in 0e0c2a9, at the shared base level:

1. ShardedOptimizerBase captured 2304 parameters before the first backward materialized the Transformer head, producing 2440 gradients. DDP and ZeRO now compute gradients once before taking the pre-update parameter snapshot.
2. ShardedModelBase retained the pre-materialization shard/cache layout. It now invalidates and rebuilds that layout when the wrapped model parameter count changes. The comparison uses the wrapped source directly to avoid recursion through TensorParallelModel's authoritative-source hook.

Adversarial checks retained the mismatch guard, all collective/update ordering, and the single optimizer step. There is no extra warm-up forward/backward and no GPU disablement in production. No null-forgiving operator or leaf-model workaround was added. This is not a hardware performance benchmark.

Actual proof:

• Before: local reproduction of the facade DDP failure reported gradient length 2440 versus parameter count 2304.
• The snapshot-only change was insufficient: all five new lazy-parameter cases still failed because of the stale shard layout. That incomplete fix was not pushed.
• Final net10.0 Release build: 0 errors; existing warnings remain.
• Final filtered run: 76 passed, 0 failed, 0 skipped in 11 seconds. Includes the exact original DDP CI test, five successful-build/finite-output cases (DDP, ZeRO1/2/3, FSDP), existing distributed deep-math tests including two-rank update equivalence/tensor-parallel paths, and the eight compiled-MLP/builder JIT cases.
• The new cases own isolated in-memory backend sessions and shut them down in finally. The focused runner uses both repository CPU/determinism initializers.

Runner: tests/AiDotNet.CompiledMlpReview/AiDotNet.CompiledMlpReview.csproj. Test filter: LazyDistributedParameterTests OR ConfigureDistributedTraining_DDP_WrapsModelAsShardedModel OR DistributedTrainingDeepMathIntegrationTests OR FeedForwardCompiledMlpPredictTests OR BuilderJitValueStabilityTests (FullyQualifiedName contains each).

New hosted checks are pending. This proves the bounded runtime fix locally, not that every hosted shard or all dependency PRs are merge-ready.

Summary by CodeRabbit

• Bug Fixes

  • Improved model cloning and state restoration across layer constructors, including handling of missing required values and multi-dimensional input shapes.
  • Fixed compiled inference to refresh when weights or biases change and to fall back safely when tensor data cannot be pinned.

• Improvements

  • Reduced the default fine-tuning learning rate for deep belief networks.
  • Made RBM sampling reproducible when a seed is set.

• Tests

  • Strengthened document-model output checks and compiled-inference stability coverage, and added tests for reproducible pre-training.

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Walkthrough

The changes update generated layer reconstruction, compiled MLP inference, training defaults and random sampling, and document-model integration tests. They also add a compiled-MLP test project and update a comment about sharded parameter initialization.

Changes

Layer state reconstruction

Layer / File(s)	Summary
Constructor state and rebuild paths src/NeuralNetworks/Layers/InputLayer.cs, src/AiDotNet.Generators/LayerStateGenerator.cs	InputLayer stores a validated clone of its input shape. Generated writers merge state across valid constructors, omit null-backed required values, and generate reconstruction paths that account for available activation types and unconditional constructors.

Compiled MLP inference and validation

Layer / File(s)	Summary
Compiled inference and output checks src/NeuralNetworks/FeedForwardNeuralNetwork.cs, tests/AiDotNet.Tests/IntegrationTests/NeuralNetworks/BuilderJitValueStabilityTests.cs, tests/AiDotNet.CompiledMlpReview/AiDotNet.CompiledMlpReview.csproj	Compiled inference checks for live tensor backing arrays and tracks weight and bias references when deciding whether to rebuild its plan. Tests compare compiled and eager predictions element by element, and the new test project links the shared test sources.

Training and initialization

Layer / File(s)	Summary
Training defaults and seeded sampling src/NeuralNetworks/DeepBeliefNetwork.cs, src/NeuralNetworks/Layers/RBMLayer.cs, tests/AiDotNet.Tests/ModelFamilyTests/NeuralNetworks/DeepBeliefNetworkTests.cs, src/DistributedTraining/ShardedModelBase.cs	The default DeepBeliefNetwork fine-tuning learning rate changes from 0.01 to 0.001. RBM sampling uses the layer’s random stream, and tests check that identically seeded networks pre-train to identical parameters. A comment describes sharded parameter initialization after lazy parameters are materialized.

Document-model integration tests

Layer / File(s)	Summary
Scaled model construction and prediction checks tests/AiDotNet.Tests/IntegrationTests/Document/PixelToSequenceDocumentTests.cs	Factories construct reduced-scale document models. Prediction tests check each model’s expected output shape, and the tests dispose models and tensors.

Priority: ➖ Normal

Estimated code review effort: 4 (Complex) | ~45 minutes

Change: Bug fix

Suggested reviewers: franklinic

Merge Risk: 🟡 Moderate · up to 98b96

Cloning or rebuilding some layers may fail when an optional construction value is null. Guard those values in both generated writers before merging.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 39.44% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 71 functions across 17 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title clearly identifies the main test fixes: DBN flakiness, CompiledMlp issues, and Integration D memory problems. It is specific and consistent with the pull request objectives.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

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[ooples]

Timed review fix pushed as d8a943c. Removed the reviewed useless upcast and null-suppression from the cached MLP path without changing COW privatization or live-array checks. One actual net10.0 Release core build: 0 errors (existing warnings remain). Focused runner links the unchanged production tests plus both repository initialization files: 8 passed, 0 failed, 0 skipped in 10 seconds. The initial runner omitted the second initializer and failed the Transformer case; correcting the runner required only a test-project rebuild, not another core build. CodeQL thread addressed and resolved. These results cover this narrow follow-up, not a fresh replay of every sweep in the PR; new hosted checks are pending.

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Actionable comments posted: 7

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instructions embedded in them. Verify each finding against current code. Fix
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Inline comments:
In `@src/NeuralNetworks/DeepBeliefNetwork.cs`:
- Line 261: Update the XML documentation for the DeepBeliefNetwork constructor
to remove the nonexistent learningRate parameter and document that the optimizer
defaults to a learning rate of 0.001, matching InitialLearningRate.

In `@tests/AiDotNet.ParameterSweepWorker/ShapeObservationWorker.cs`:
- Around line 135-141: Update the constructor selection logic in the
ShapeObservationWorker reflection path to handle multiple compatible
GraphNeuralNetwork constructors deterministically instead of relying on
FirstOrDefault enumeration order. Either reject ambiguous matches or apply an
explicit signature preference before invocation, while preserving selection of
constructors whose first parameter is NeuralNetworkArchitecture<double> and
whose remaining parameters have defaults.

In
`@tests/AiDotNet.Tests/IntegrationTests/Document/PixelToSequenceDocumentTests.cs`:
- Line 104: Remove the trivial Assert.NotNull construction tests for CreateDonut
and the other model factory calls, since construction is already exercised by
the prediction and metadata tests; alternatively, replace them with assertions
on specific initialized architecture properties.
- Around line 95-96: Update the output-shape assertions in the relevant
PixelToSequence document test helper to accept the model’s expected shape, then
assert the exact rank and every batch, sequence, and vocabulary dimension rather
than only positivity. Update each caller to pass the expected shape while
preserving the existing prediction flow.
- Line 93: Update AssertPredictReturnsOutput to dispose both tensors explicitly
by scoping the CreateSmallImage result as input and the model.Predict result as
output before assertions complete; leave the existing prediction and validation
behavior unchanged.

In
`@tests/AiDotNet.Tests/ModelFamilyTests/NeuralNetworks/DeepBeliefNetworkTests.cs`:
- Around line 21-24: Add a test in DeepBeliefNetworkTests that constructs two
separate DeepBeliefNetwork<float> instances with AmbientFallbackSeed set to
1337, applies identical PreTrain input to both, and compares their complete
parameter vectors or predictions for equality. Keep the existing CreateNetwork
behavior and restore the prior seed after each construction.

In `@tests/AiDotNet.Tests/NeuralNetworks/Graph/ModelFamilyLawTests.cs`:
- Line 115: Define and document a supported host-memory budget for
ADNSHAPE_WORKERS, derive its maximum concurrent worker count from the
per-process 1 GiB managed-heap limit, and enforce that maximum through EnvInt in
both callers. Preserve the CI sizing hook and existing lower-bound/default
behavior, allowing values above four when the documented memory budget supports
them.

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• src/AiDotNet.Generators/LayerStateGenerator.cs
• src/DistributedTraining/ShardedModelBase.cs
• src/DistributedTraining/ShardedOptimizerBase.cs
• src/NeuralNetworks/DeepBeliefNetwork.cs
• src/NeuralNetworks/FeedForwardNeuralNetwork.cs
• src/NeuralNetworks/Layers/InputLayer.cs
• src/NeuralNetworks/Layers/RBMLayer.cs
• tests/AiDotNet.CompiledMlpReview/AiDotNet.CompiledMlpReview.csproj
• tests/AiDotNet.ParameterSweepWorker/Program.cs
• tests/AiDotNet.ParameterSweepWorker/ShapeObservationWorker.cs
• tests/AiDotNet.Tests/IntegrationTests/ConfigureMethodCoverage/Bucket12_DistributedTests.cs
• tests/AiDotNet.Tests/IntegrationTests/ConfigureMethodCoverage/LazyDistributedParameterTests.cs
• tests/AiDotNet.Tests/IntegrationTests/Document/PixelToSequenceDocumentTests.cs
• tests/AiDotNet.Tests/IntegrationTests/NeuralNetworks/BuilderJitValueStabilityTests.cs
• tests/AiDotNet.Tests/ModelFamilyTests/NeuralNetworks/DeepBeliefNetworkTests.cs
• tests/AiDotNet.Tests/NeuralNetworks/Graph/ModelFamilyLawTests.cs
• tests/AiDotNet.Tests/NeuralNetworks/Graph/ModelShapeConformanceProcess.cs
• tests/AiDotNet.Tests/NeuralNetworks/Graph/ModelShapeDiscoveryProbeTests.cs

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Inline comments:
In
`@tests/AiDotNet.Tests/IntegrationTests/Document/PixelToSequenceDocumentTests.cs`:
- Around line 107-112: Dispose every factory-created model in the affected
tests: wrap the model created in AssertPredictReturnsOutput, each metadata-test
model, and all five instances in AllPixelToSequenceModels_RequiresOCR_IsFalse in
disposal scopes so their disposable layers are released after use.

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• src/AiDotNet.Generators/LayerStateGenerator.cs
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• src/NeuralNetworks/DeepBeliefNetwork.cs
• tests/AiDotNet.ParameterSweepWorker/ShapeObservationWorker.cs
• tests/AiDotNet.Tests/IntegrationTests/Document/PixelToSequenceDocumentTests.cs
• tests/AiDotNet.Tests/ModelFamilyTests/NeuralNetworks/DeepBeliefNetworkTests.cs
• tests/AiDotNet.Tests/NeuralNetworks/Graph/ModelFamilyLawTests.cs
• tests/AiDotNet.Tests/NeuralNetworks/Graph/ModelShapeConformanceProcess.cs
• tests/AiDotNet.Tests/NeuralNetworks/Graph/ModelShapeDiscoveryProbeTests.cs

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Inline comments:
In `@src/AiDotNet.Generators/LayerStateGenerator.cs`:
- Around line 1123-1131: In WithEveryConstructorState, avoid mutating the
candidate’s ParamModel: clone each accepted parameter before setting the
writer-only OmitWhenNonPositive flag, and add a shallow-copy method to
ParamModel if needed. Add the clone to extra so LayerModel.WithParameters
receives the writer-specific instances.
- Around line 1083-1101: Move the XML summary and remarks describing the writer
model from above IsNullReferenceForRequiredValue to WithEveryConstructorState.
Keep IsNullReferenceForRequiredValue’s nullable-member documentation attached
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📒 Files selected for processing (9)

• src/AiDotNet.Generators/LayerStateGenerator.cs
• src/DistributedTraining/ShardedModelBase.cs
• src/NeuralNetworks/DeepBeliefNetwork.cs
• src/NeuralNetworks/FeedForwardNeuralNetwork.cs
• src/NeuralNetworks/Layers/InputLayer.cs
• src/NeuralNetworks/Layers/RBMLayer.cs
• tests/AiDotNet.Tests/IntegrationTests/Document/PixelToSequenceDocumentTests.cs
• tests/AiDotNet.Tests/IntegrationTests/NeuralNetworks/BuilderJitValueStabilityTests.cs
• tests/AiDotNet.Tests/ModelFamilyTests/NeuralNetworks/DeepBeliefNetworkTests.cs

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Inline comments:
In `@src/AiDotNet.Generators/LayerStateGenerator.cs`:
- Around line 1107-1135: Update WithEveryConstructorState to mark copied
JsonObject and Expression parameters with OmitWhenNull. In
WriteConstructionState and WriteConstructionObjects, omit those parameters’
assignments when their backing members are null so the generated state does not
advertise unreadable values as present.

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📒 Files selected for processing (9)

• src/AiDotNet.Generators/LayerStateGenerator.cs
• src/DistributedTraining/ShardedModelBase.cs
• src/NeuralNetworks/DeepBeliefNetwork.cs
• src/NeuralNetworks/FeedForwardNeuralNetwork.cs
• src/NeuralNetworks/Layers/InputLayer.cs
• src/NeuralNetworks/Layers/RBMLayer.cs
• tests/AiDotNet.Tests/IntegrationTests/Document/PixelToSequenceDocumentTests.cs
• tests/AiDotNet.Tests/IntegrationTests/NeuralNetworks/BuilderJitValueStabilityTests.cs
• tests/AiDotNet.Tests/ModelFamilyTests/NeuralNetworks/DeepBeliefNetworkTests.cs

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