Leaderboard: zero-shot models only; add self-reported entries; second table for models fitted on train

README.md

@@ -62,8 +62,10 @@ not reproduce their Jev model.
 
 ## Leaderboard
 
-`test` split: 400 cases, 2,000 decisions. General models are scored zero-shot;
-they have never seen these workflows or their question schemas.
+`test` split: 400 cases, 2,000 decisions. This table lists general models scored
+zero-shot: they have never seen these workflows or their question schemas.
+Models that were fitted or fine-tuned on `train` are in the second table below;
+the two tables are not comparable.
 
 | # | Model | Kind | Accuracy ↑ | KL from gold ↓ | Brier ↓ | ECE ↓ | p50 latency | Price / 1M input |
 |---|---|---|---|---|---|---|---|---|
@@ -71,11 +73,12 @@ they have never seen these workflows or their question schemas.
 | 2 | [Liquid AI d1](https://docs.liquid.ai/lfm/models/decision-models) (`d1:free`) | general, zero-shot | 0.742 | 0.475 | 0.155 | 0.124 | 525 ms | free tier |
 | 3 | TypeSafe Jev 1.13.0 | general, zero-shot | 0.727 | 1.442 | 0.148 | 0.144 | 710 ms | $0.042 |
 | 4 | [Featherless Simple Jev](https://simple-jev.featherless.ai) (`Qwen3.6-35B-A3B-classifier`) | general, zero-shot | 0.716 | 0.488 | 0.176 | – | – | free demo |
-| 5 | ModernBERT-base (149M) | specialist, fitted per workflow | 0.646 | 0.223 | 0.119 | 0.179 | 349 ms† | – |
-| 6 | MiniLM-L6 (22M) | specialist, fitted per workflow | 0.587 | 0.262 | 0.143 | 0.108 | 22 ms† | – |
-| 7 | [Jeff-Gemma4-E2B](https://huggingface.co/mstrasser/Jeff-Gemma4-E2B) | general, zero-shot, open weights | 0.561 | 0.403 | 0.219 | 0.188 | 2,272 ms† | open weights |
-| 8 | [Jeff-Qwen3.5-2B](https://huggingface.co/mstrasser/Jeff-Qwen3.5-2B) | general, zero-shot, open weights | 0.511 | 0.460 | 0.237 | 0.203 | 1,346 ms† | open weights |
-| 9 | [Jeff-Qwen3.5-0.8B](https://huggingface.co/mstrasser/Jeff-Qwen3.5-0.8B) | general, zero-shot, open weights | 0.483 | 0.679 | 0.313 | 0.251 | 662 ms† | open weights |
+| 5 | [prima-ratio + 12B](https://github.com/andrea-tomassi/prima-ratio) § | general, zero-shot | 0.702 | 0.564 | 0.234 | 0.146 | 700 ms‡ | local GGUF |
+| 6 | [OpenDecider-small](https://huggingface.co/manjunathshiva/opendecider-small) § | general, zero-shot, open weights | 0.671 | 0.211 | 0.117 | – | 40 ms‡ | open weights |
+| 7 | [Bongard-mini](https://huggingface.co/AgentBull/bongard-mini) § | general, zero-shot, open weights | 0.594 | 0.256 | 0.132 | 0.067 | 225 ms‡ | open weights |
+| 8 | [Jeff-Gemma4-E2B](https://huggingface.co/mstrasser/Jeff-Gemma4-E2B) | general, zero-shot, open weights | 0.561 | 0.403 | 0.219 | 0.188 | 2,272 ms† | open weights |
+| 9 | [Jeff-Qwen3.5-2B](https://huggingface.co/mstrasser/Jeff-Qwen3.5-2B) | general, zero-shot, open weights | 0.511 | 0.460 | 0.237 | 0.203 | 1,346 ms† | open weights |
+| 10 | [Jeff-Qwen3.5-0.8B](https://huggingface.co/mstrasser/Jeff-Qwen3.5-0.8B) | general, zero-shot, open weights | 0.483 | 0.679 | 0.313 | 0.251 | 662 ms† | open weights |
 | – | Prior (ignores the input) | reference | 0.470 | 0.347 | 0.189 | 0.088 | – | – |
 | – | Uniform (same probability on every option) | reference | 0.308 | 0.444 | 0.238 | 0.169 | – | – |
 
@@ -85,9 +88,33 @@ one request. Request shape matters: in a third-party run Jev's yes/no accuracy w
 
 Hosted rows are p50 end to end from a client, one request at a time.
 † Measured on the same machine as the model (M3 Max), not comparable with hosted latency.
+‡ Reported by the submitter on their own hardware (a different GPU and a different unit for each), not comparable with the other rows.
+§ Self-reported by the submitter in the linked discussion and not re-run by us. Metric definitions follow the submitter's report; ECE in particular is computed differently by different submitters.
+
+### Fitted or fine-tuned on `train`
+
+These models were trained on this benchmark's `train` split, so they are not
+zero-shot and their scores are not comparable with the table above. All were
+self-reported in the linked discussions and were not re-run by us except the two
+specialists marked ours. Scores well above the 0.735 teacher self-agreement mean a
+model is learning the teacher's quirks.
+
+| Model | Kind | Accuracy ↑ | KL from gold ↓ | Brier ↓ | ECE ↓ | Reported latency |
+|---|---|---|---|---|---|---|
+| [OpenDecider-large-td](https://huggingface.co/manjunathshiva/opendecider-large-td) (Qwen3-Next-80B-A3B + LoRA) | general, then fine-tuned on `train` | 0.801 | 0.081 | 0.044 | – | 440 ms‡ |
+| [od1-typed-decisions](https://huggingface.co/mvbalaji/od1-typed-decisions) (Qwen3.5-4B) | specialist | 0.797 | 0.082 | 0.045 | 0.156¶ | – |
+| [OpenDecider-nano](https://huggingface.co/manjunathshiva/opendecider-nano) (400M encoder) | general, then fine-tuned on `train` | 0.796 | 0.079 | 0.043 | – | 17 ms‡ |
+| [OpenDecider-small-td](https://huggingface.co/manjunathshiva/opendecider-small-td) (Qwen3-4B + LoRA) | general, then fine-tuned on `train` | 0.792 | 0.080 | 0.043 | – | 40 ms‡ |
+| [OpenDecider-medium-td](https://huggingface.co/manjunathshiva/opendecider-medium-td) (Qwen3-30B-A3B + LoRA) | general, then fine-tuned on `train` | 0.788 | 0.081 | 0.044 | – | 214 ms‡ |
+| [soft-decider-421m](https://huggingface.co/winwinwinbb/soft-decider-421m) | specialist | 0.774 | – | – | 0.141¶ | 50 ms‡ |
+| [Laya typed-decisions](https://huggingface.co/convaiinnovations/laya-typed-decisions) | fitted on `train` | 0.766 | – | – | – | – |
+| ModernBERT-base (149M), ours | specialist, fitted per workflow | 0.646 | 0.223 | 0.119 | 0.179 | 349 ms† |
+| MiniLM-L6 (22M), ours | specialist, fitted per workflow | 0.587 | 0.262 | 0.143 | 0.108 | 22 ms† |
+
+¶ The submitter's own ECE definition, which does not match the one used above.
 
 **[meraGPT Decider 1](https://meragpt.com/models/state-decider-1?utm_source=huggingface&utm_medium=dataset_card&utm_campaign=typed-decisions) is state of
-the art on this benchmark.** It leads Jev on every question type (`noul` 0.840 vs
+the art among zero-shot models on this benchmark.** It leads Jev on every question type (`noul` 0.840 vs
 0.775, `choice` 0.733 vs 0.720, `score` 0.739 vs 0.696), its distributions sit far
 closer to the gold (KL 0.096 vs 1.442), it is faster end to end, and it costs less
 per token. It answers `POST /v1/systemone` at
@@ -117,6 +144,15 @@ discussion with the numbers and the mode (specialist or general) it used.
   three clear the Prior on accuracy but not on KL. Jeff's own 83.1% comes from a
   different five-benchmark panel. If there is a better way to serve them, open a
   discussion and we will rescore.
+- **Self-reported rows** (§ above, and the second table) come from discussions
+  [#5](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/5)
+  (prima-ratio), [#7](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/7)
+  (Bongard-mini), [#8](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/8)
+  (OpenDecider), [#4](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/4)
+  (od1), [#3](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/3)
+  (soft-decider) and [#2](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/2)
+  (Laya). Each submitter states the mode; we have not re-run them. If a number looks
+  wrong, say so in the discussion and we will correct it.
 - **Specialists** use [Adaptive Classifier](https://github.com/codelion/adaptive-classifier)
   0.2.0, one classifier per question on a frozen encoder, tuned on a held-out
   quarter of `train` (mean pooling, `max_length` 512, 30 epochs,