Datasets:

PureOne
/

Eidrion

The immutable protocol and all executed Python sources are in final_standard/source/; their SHA-256 values are bound by final_standard/source_lock.json. The lock was written on 2026-10-08 at 20:18:11 UTC, before the first final worker loaded official test data. The manifest SHA-256 is af4ac3635681ac02faa2b36181830f45654c3f9549601258e2fe4aa879308825. The 260 jobs were launched at 20:18:38 UTC.

There are thirteen methods, ten paired fresh seeds, and two datasets (MNIST and Fashion-MNIST). Every run has five two-class arrivals. The class permutation is fixed by the run seed and shared across methods. Output labels are remapped to arrival rank, but inference always predicts from all ten outputs without a task ID or a seen-class mask. The training procedure knows the arrival boundaries. This is a class-incremental benchmark with bounded streams, not a task-free, unbounded online deployment study.

The globally fixed source partition is 48,000 fitting images, 6,000 validation images, and 6,000 independent calibration images. The validation partition is unused by final learners. Its earlier proxy subdivision supported development; the real calibration images were excluded from every development fit and model selection. Ordinary baselines may fit all 54,000 nonvalidation images, while certificate methods spend 6,000 of the same total observed-image allowance on calibration and fit only 48,000. The no-controller center ablation uses the same 48,000 fitting images as the scalar center, isolating that controller. No baseline-trained representation is reused by a certificate method.

Method	Fixed final configuration	Main scope
SGD	Shared 784–128–128–10 ReLU MLP, SGD 0.1, two epochs per arrival	No memory
ER	Same MLP; concatenated current/replay cross-entropy	Reservoir over presentation events
DER++	Current CE + 0.5 all-logit MSE + 0.5 replay CE	Two independent replay draws; pre-update targets
ER-ACE	Reference scale-10 cosine head and asymmetric current loss; two epochs	Strong replay baseline
ER-ACE, four epochs	Same loss/model, four epochs	Explicitly compute-relaxed control
Cumulative RFF ridge	512 fixed RFF, bandwidth 0.5, ridge 0.001	Global sufficient statistics; no feature learning
Global prototypes	128 MiniBatchKMeans prototypes/class on normalized pixels	Fits all 54,000 baseline images
Scalar center	Same RFF map, projected ridge center, 64 added geometry directions, risk 0.1	Preserves possibly inaccurate acquired center predictions
Center without scalar controller	Same fitting data, feature map, center and geometry update	No retention certificate
Immutable prototype regions	128 prototypes/class, risk 0.1, one mutable MLP outside earlier gates	Atomic two-class admission; refused regions stay unprotected
Fresh renewal, risk 0.2	Cumulative 128 prototypes/class; 30 fresh class tests	Deploy only fully accepted prefixes
Fresh renewal, risk 0.1	Same learner and archive	Predeclared evidence-resolution variant
Stagewise joint reference	Reinitialized MLP trained for two epochs on all seen data each stage	Explicit noncontinual access and memory exception

All methods except the joint reference have a 6 MiB persistent payload budget. This includes model and feature-map tensors, replay/prototype/Gram/basis state, active certificates and ledgers, retained calibration archives, source IDs, and serialized non-tensor checkpoint metadata. Reservoirs are charged at allocated capacity, rather than their currently filled size. The joint reference's retained raw images, labels, and IDs are counted and its budget exception is explicit. Transient fitting buffers, parameter gradients, Python allocator overhead, and evaluator copies are not called persistent payload; separate per-process peak RSS captures their memory effect.

Renewal retains one shared prototype array and an accepted-prefix index. Its unused calibration archive is released one arriving class pair at a time, partitioned into disjoint future-stage blocks, and counted. A rejected proposal does not replace the accepted predictor. Before any acceptance, zero logits are an explicit unprotected fallback. The ordinary global-prototype baseline fits more images and therefore does not use identical learned prototypes. Each renewal run also saves unconditional predictions from its own exact proposal, which supplies the identical-prototype controller diagnostic without another fitting run.

The statistical failure allocation is 0.05 per seeded run and per policy, paid across every predeclared attempt, including failed acquisitions. It is not a simultaneous 95% claim across twenty dataset/seed repetitions or across policies. All policy results are reported. Conditional population statements assume independent representative class-conditional calibration evidence; data provenance establishes partition separation, not the absence of benchmark-to- deployment distribution shift. Renewal's statement concerns each accepted checkpoint and does not bound ever-error on a fixed input over time.

Saved predictions support full class and task matrices, final micro/macro accuracy, mean acquisition accuracy, average incremental accuracy, backward transfer, and signed forgetting. Backward transfer averages final-minus- acquisition accuracy on the first four tasks. Forgetting averages the largest previous post-acquisition accuracy minus final accuracy on those tasks, so an improvement may give a negative value. Forward transfer compares a task's accuracy immediately before its arrival with the same model's initialization accuracy. Intransigence compares with the paired stagewise joint reference. Mean, sample standard deviation, and Student-t 95% intervals are reported across the ten paired seeds; paired-difference intervals use matching seeds. These are descriptive comparisons without multiplicity-adjusted superiority claims.

Full ten-way softmax ECE uses fifteen equal-width confidence bins and no fitted temperature. Prototype logits have an arbitrary declared scale of four and ER-ACE a cosine scale of ten, so calibration metrics compare those output conventions rather than claiming calibrated posterior models. NLL and Brier are a separately hashed post-lock descriptive analysis requested after launch, implemented by analyze_nll_brier.py; they cannot influence fitting or model selection.

Each worker uses one PyTorch/BLAS thread in its own process. Four synthetic resource-probe processes used 1,962,400 KiB in summed peak RSS, below the 6 GiB admission threshold. Training workers may contend, so their wall times are labeled accordingly. After all workers exit, serial batch-one and batch-128 inference measurements use three warmups and twenty-one fixed-checkpoint trials. Reported arithmetic counts are component estimates: dense neural multiply/adds, RFF products, ridge products/solves, covariance products, and dominant KMeans mini-batch distance products. Transcendentals, loss/norm operations, QR/SVD/eigh implementation details, KMeans++ initialization, and bookkeeping are excluded explicitly. Solver calls, fitting examples, optimizer steps, and measured times provide additional cost evidence; these estimates are not advertised as full hardware FLOP measurements.

The benchmark is executed from the captured source with:

python research/omega/experiments/final_standard/source/run_final.py \
  --output-dir research/omega/experiments/final_standard \
  --data-root research/omega/experiments/data \
  --execute-locked --workers 4

Existing completed or failed captures are preserved; the runner does not silently overwrite or retry them. For a fresh reproduction, use reproduce_final.py --captured-root <capture> --output-dir <fresh> --data-root <data> --workers 4. That wrapper verifies source, environment, and archive digests; copies only source/ and source_lock.json; and runs the unchanged captured implementation into fresh directories. It never duplicates or overwrites captured prediction/state evidence. The original lock-creation helper assumes the development directory layout, so it is not used by this portable reproduction route. The downloaded archive hashes and fixed partition-ID files remain part of data provenance. Failures, if any, remain visible in final aggregation.