| --- |
| license: cc-by-4.0 |
| task_categories: |
| - text-classification |
| language: |
| - en |
| - multilingual |
| tags: |
| - benchmark |
| - decision-models |
| - jev |
| - laya |
| - julia-1 |
| - calibration |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # Decision-Model Benchmark 2026-09-27: jev vs laya vs Julia-1-ONNX |
|
|
| Raw data behind the head-to-head benchmark of three decision engines: 19,776 decisions × 3 engines |
| (59,328 inferences), 0 inference failures. Everything needed to audit or re-score the run. |
|
|
| ## Contents |
|
|
| - `manifests/*.jsonl` — frozen test manifests (19,776 decisions): question wording, criteria, option |
| order (`keys`), gold key, per-dataset row ids. `manifests/meta.json` records source URLs and |
| SHA-256 of every dataset file (typed-decisions pinned rev `c76749ec…`). |
| - `predictions/{jev,laya,julia-onnx}.jsonl` — 19,776 rows each: predicted key, full probability |
| vector over `keys`, gold, latency_ms, engine-specific traces (julia tournament rounds/final |
| candidates; jev input_tokens). |
| - `predictions/*.latency.jsonl` — 250 rows per engine from dedicated serial runs (one engine at a |
| time, jev single in-flight). The only latency numbers reported publicly come from these files. |
| - `results.json` — all computed metrics: accuracy, macro-F1, Brier, clipped NLL, ECE, p(gold)=0, |
| per-dataset and per-slice breakdowns, paired-bootstrap deltas (20,000 resamples, |
| target-stratified, seed 20260927). |
|
|
| ## Test sets (gold comes from the datasets themselves) |
|
|
| | Dataset | Decisions | Source | |
| |---|---:|---| |
| | typed-decisions | 2,000 | `LocalLLaMA/typed-decisions` test parquet (official TypeSafe/Julia suite: 600 choice, 800 score, 600 noul) | |
| | AG News | 7,600 | full test split | |
| | DAIR Emotion | 2,000 | full test split | |
| | Banking77 | 3,076 | full test split (77 options per call) | |
| | MASSIVE scenario | 5,100 | 51 locales × 100 rows, deterministic SHA256-based selection, seed 20260927 | |
|
|
| Question wording and criteria are identical across engines and frozen in the manifests. |
|
|
| ## Engines |
|
|
| - **jev-1.13.0** — TypeSafe hosted API (`POST /v1/systemone`) |
| - **laya 0.3.20** — `NandhaKishorM/laya` shipped `Router`, base zero-shot (the `laya-typed-decisions` |
| fine-tuned checkpoint was deliberately excluded: train-on-test on this suite) |
| - **Julia-1-ONNX** — `SupersonicLabs/Julia-1-ONNX` published `model.onnx` + `model.onnx.data` on |
| onnxruntime CPU, native `julia.data.sequence` encoding (strict, max_length=1024, head_length=512); |
| >20 options via the official `julia.router.Router` tournament (width=20, survivors=2). |
| ONNX↔torch parity verified: argmax 40/40, max |Δlogit| 0.000258. |
|
|
| ## Scoring conventions (matters for calibration metrics) |
|
|
| - jev returns probability maps with sum drift up to 0.01 (display rounding is the working |
| hypothesis — see the run's error notes); 416 rows affected. laya probabilities pass through |
| display rounding (round(4)). All vectors are renormalized before scoring. |
| - julia Banking77 probabilities are conditional on the final tournament candidate set |
| (`probability_scope=final_candidates`) — the vendor's documented Router behavior. |
| - NLL is clipped; ECE is 15 equal-width bins. |
|
|
| ## Caveats |
|
|
| - MASSIVE uses bare label names as criteria (hard zero-shot). The Julia-1 card's 71.5% used scenario |
| descriptions — not comparable to the 0.402 measured here. |
| - Reported latency is end-to-end deployment latency on different hardware (jev: hosted API + |
| network RTT; laya: Apple MPS, torch 2.14; julia: onnxruntime CPU), not compute-normalized speed. |
| - Vendor-claim checks and the full write-up live in the companion report (`BENCHMARK.md` in the |
| release zip). |
|
|
| ## License |
|
|
| Published dataset rows remain under their original dataset licenses (typed-decisions, AG News, |
| DAIR Emotion, Banking77, MASSIVE). Prediction and manifest files are CC-BY-4.0. |
|
|
