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Publish initial Apache-2.0 Shingi v0.2 adapter and evaluation evidence
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Shingi v0.2 CUDA evaluation

Fresh test: 1124/1,500 (74.93%) Shingi versus 1002/1,500 (66.80%) unchanged Bonsai on the RTX PRO 6000.

The paired change is +8.13 percentage points, with a descriptive paired-bootstrap 95% interval [+6.00, +10.27]. The 9-source holdout change is +6.11 points [+3.67, +8.56].

Accuracy by source

Metric, calibrated Unchanged Bonsai Shingi v0.2
NLL (n=1500) 0.95619 0.73016
Multiclass Brier (n=1500) 0.43559 0.32850
Binary Brier (n=400) 0.10848 0.04877
Expected-score MAE (n=300) 0.80599 0.56875
Ordinal RPS (n=300) 0.17671 0.10517
Distance to human labels (n=400) 0.40500 0.32314
Top-label ECE (n=1,500) 0.05147 0.04734

Calibration and score quality

GPU, same 1,500 inputs Unchanged Bonsai correct Shingi correct
6000 1002/1,500 (66.80%) 1124/1,500 (74.93%)
4090 1004/1,500 (66.93%) 1125/1,500 (75.00%)
Cross-GPU comparison Argmax agreement Mean / maximum probability TVD
base 99.5333% 0.0048461 / 0.0617394
adapter 99.7333% 0.0028019 / 0.3467724

Both models pass the predeclared 99% agreement / 0.01 mean-TVD gates, but this is not exact numerical equivalence. Different final adaptive prompts occur in 3 base and 3 adapter requests after a chunk winner changes. Every static chunk prompt and candidate token ID matches across devices; every adaptive trace is verified independently by replay. Small hardware-dependent logit changes can cause a large individual probability shift in this approximate method; the maximum TVD is reported above and its record ID is in the JSON.

Choice keys are sorted before inference. Map-order stability is an interface property, not learned invariance. The additional input-order diagnostic varies the underlying prompt order.

GPU Map-order flips, base / adapter Input-order diagnostic flips, base / adapter Adapter context probes
6000 0/200 / 0/200 26/200 / 14/200 12/12
4090 0/200 / 0/200 26/200 / 14/200 12/12
GPU Mixed median / p95 Short HTTP median / p95 Model initialization
6000 81.1 / 612.3 ms 51.0 / 58.0 ms 1.17 s
4090 107.5 / 755.3 ms 70.5 / 73.8 ms 9.11 s

Comparison with the frozen v0.1 adapter

Both adapters use the same fresh test. Each retains its own frozen calibration. Raw probability comparisons are also recorded in the JSON.

GPU v0.1 accuracy New accuracy Paired change, 95% interval
6000 74.47% 74.93% +0.47 pp [-0.93, +1.87]
4090 74.40% 75.00% +0.60 pp [-0.80, +2.00]

Measured regressions

The following losses are retained even when the aggregate improves. Source slices contain 100 examples. Small changes can be sampling noise; these are descriptive measurements.

GPU Scope Metric Previous New
6000 overall ece_top_label 0.02697 0.04734
6000 banking77 ece_top_label 0.05990 0.08226
6000 boolq nll 0.23052 0.32196
6000 boolq brier_multiclass 0.14044 0.15556
6000 boolq brier_binary 0.07022 0.07778
6000 boolq ece_top_label 0.05725 0.06719
6000 chaosnli accuracy_failures_incorrect 0.60000 0.57000
6000 fever_evidence nll 0.15015 0.17628
6000 go_emotions human_tvd 0.67764 0.67846
6000 helpsteer2_helpfulness accuracy_failures_incorrect 0.48000 0.45000
6000 helpsteer2_helpfulness ece_top_label 0.12104 0.12334
6000 ledgar nll 1.12368 1.12537
6000 sms_spam accuracy_failures_incorrect 0.99000 0.96000
6000 sms_spam brier_multiclass 0.05283 0.05776
6000 sms_spam brier_binary 0.02642 0.02888
4090 overall ece_top_label 0.02717 0.04564
4090 banking77 ece_top_label 0.05062 0.08572
4090 boolq nll 0.23105 0.32666
4090 boolq brier_multiclass 0.14113 0.15488
4090 boolq brier_binary 0.07056 0.07744
4090 boolq ece_top_label 0.05721 0.07422
4090 chaosnli accuracy_failures_incorrect 0.60000 0.58000
4090 fever_evidence nll 0.15006 0.17766
4090 go_emotions human_tvd 0.67757 0.67825
4090 helpsteer2_helpfulness accuracy_failures_incorrect 0.48000 0.45000
4090 helpsteer2_helpfulness ece_top_label 0.09464 0.12303
4090 ledgar nll 1.12220 1.12559
4090 ledgar ece_top_label 0.08144 0.08272
4090 mmlu ece_top_label 0.07102 0.08043
4090 sms_spam accuracy_failures_incorrect 0.99000 0.96000
4090 sms_spam brier_multiclass 0.05317 0.05874
4090 sms_spam brier_binary 0.02658 0.02937
GPU Mixed median, previous / new Mixed p95, previous / new
6000 81.50 / 81.12 ms 613.75 / 612.26 ms
4090 107.17 / 107.48 ms 755.57 / 755.29 ms

Latency curves

Hardware measurements

Method and limits

  • 100 fresh records from each of 15 sources; 10,708 prior IDs and 10,708 state hashes excluded. The aggregate weights sources equally and is not a user-workload population estimate.
  • Frozen adapter and readout, with separate 180-record calibration. No release-test tuning. Upstream pretraining overlap is unknown.
  • Choice keys are sorted before inference. Checkpoint selection and calibration preceded this locked test.
  • Paired intervals use 20,000 bootstrap draws, seed 20260922; descriptive, without multiplicity correction. Source slices have only 100 records.
  • Mixed latency: 300 fixed records, 20/source, repeated three times after ten warmups. Synthetic curves: ten repetitions per case after warmup. Short HTTP: 30 localhost SDK requests after three warmups; one three-choice question.
  • Latency percentiles use sorted values at index round((n−1) × p). With ten repeats, the sample p95 is the maximum. Curves are workload measurements, not population tail-latency guarantees.
  • Wall times include all forward passes, tokenization and memory checks; loading is separate. Model initialization times exclude artifact checksum hashing. OS filesystem cache is uncontrolled. Device memory is sampled every 100 ms; a brief peak can be missed. Host RSS is sampled only after native readiness, so it does not establish a host load-time peak. Other resident processes are recorded in private operator evidence.
  • Synthetic context probes test simple planted-fact retrieval through about 15K tokens. They do not establish real-document comprehension.
  • Text/JSON only; sequential serving. More than 52 choices use approximate multi-pass chunk-and-anchor scoring. Labels and wording can still affect decisions.
  • No Mac result, hosted Jev request, or hosted latency comparison is included.

Exact metrics, GPU/driver information, file hashes, source revisions, calibration and data identifiers are in summary.json. accuracy.csv contains both devices' per-source results. Raw predictions and traces are retained separately.