Release 1.0.0: selected blackjack checkpoint and evaluation evidence

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+evaluation/figures/final-quality.png filter=lfs diff=lfs merge=lfs -text
+evaluation/figures/final-returns.png filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,202 @@
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NOTICE

@@ -0,0 +1,18 @@
+Laya Blackjack Laboratory
+Copyright 2026 Laya Blackjack contributors
+
+This project includes adaptations of and model weights derived from:
+
+Laya, by Convai Innovations and its contributors
+https://huggingface.co/convaiinnovations/laya
+https://github.com/NandhaKishorM/laya
+Upstream checkpoint revision: 1c5edc17a7acd8701df6fc341c0d179f1c62c982
+Licensed under the Apache License, Version 2.0.
+
+ModernBERT-large, by Answer.AI, LightOn, and their collaborators
+https://huggingface.co/answerdotai/ModernBERT-large
+Licensed under the Apache License, Version 2.0.
+
+The blackjack simulator, application, synthetic training pipeline, evaluation
+tools, and domain fine-tuning are modifications by this project's contributors.
+This project is independent and is not endorsed by the upstream authors.

README.md

@@ -0,0 +1,120 @@
+---
+license: apache-2.0
+language:
+- en
+base_model: convaiinnovations/laya
+base_model_relation: finetune
+tags:
+- blackjack
+- simulation
+- decision-making
+- modernbert
+- laya
+- probability-calibration
+---
+
+# Laya Blackjack
+
+A full-model adaptation of [Laya](https://huggingface.co/convaiinnovations/laya) for public-information blackjack research. One learned player shares a finite shoe with up to six simulated tablemates. The model predicts legal-action preferences, next-hit bust probability, and the dealer's finish distribution if no player draws again.
+
+**Selected release: 1.0.0, privately staged.** [Project and dashboard](https://github.com/adambloebaum/laya-blackjack) · [Measured results](https://github.com/adambloebaum/laya-blackjack/blob/main/docs/release-results.md) · [Selection protocol](https://github.com/adambloebaum/laya-blackjack/blob/main/docs/final-release-selection.md).
+
+The release contains one selected set of weights. The teacher-cost study's **ordinary-imitation arm** was retained after comparison with four completed research alternatives. Its fresh three-million-round benchmark supports a continuous-play advantage over the basic heuristic and the previously packaged model. Fresh-shoe differences remain inconclusive, and absolute average returns remain negative.
+
+## Use and input contract
+
+Local simulated play, strategy research, and probability inspection. The model sees the active hand, legal actions, exposed cards, rules, and unseen rank counts. It never receives the random seed, concealed dealer rank, or future shoe order. Inputs must use this project's state and question builders.
+
+Supported simulation: American hole-card rules; 1–7 seats; 1/2/4/6/8 decks; S17/H17; 3:2 or 6:5 payout; double after split; late surrender; configurable penetration and tablemate behavior. Wagers are fixed at one initial unit. No insurance or side bets; split aces get one card and cannot resplit. Unusual shoe exhaustion refunds the round.
+
+This is a **421,293,830-parameter typed decision model**, not a generative chat model. Context budgets are 1,024 tokens total and 256 for question heads; overflow fails explicitly. The tested stack is Python 3.13, `laya==0.3.4`, `torch==2.8.0`, and `transformers==4.57.6`, on RTX 4090 hardware. CPU inference is supported but slower. Weights occupy approximately 1.7 GB; the full download also includes evaluation and training evidence.
+
+## Quick start
+
+```bash
+git clone https://github.com/adambloebaum/laya-blackjack.git
+cd laya-blackjack
+uv sync --extra model --extra dev
+uv run --no-sync hf auth login
+uv run --no-sync blackjack fetch-model
+uv run --no-sync blackjack serve
+```
+
+Authentication is required while the repository is private; use an account with access. Open http://127.0.0.1:8000 and choose **Load trained Laya**. The project pins the Hub commit and manifest digest and verifies all files before installation. If an older checkpoint already occupies the default directory, use `blackjack fetch-model --output artifacts/checkpoints/released-v1` instead; the existing installation is preserved and the dashboard discovers the new completed checkpoint.
+
+Programmatic inference after a fresh default installation:
+
+```python
+from blackjack.engine import Game, Rules
+from blackjack.model import LayaPolicy
+
+policy = LayaPolicy()
+policy.load("artifacts/checkpoints/released", device="cuda:0")
+game = Game(Rules(players=3), seed=42)
+game.deal()
+if game.phase == "playing":
+    prediction = policy.predict(game.observation())
+    print(prediction["answers"])
+```
+
+Use the new installation path if you chose `released-v1`. Model loading is local after download; no paid inference service is required. The dashboard is a localhost research tool, without remote authentication.
+
+## Training lineage and final selection
+
+The upstream checkpoint is `convaiinnovations/laya` at `1c5edc17a7acd8701df6fc341c0d179f1c62c982`, with the ModernBERT-large backbone from Answer.AI and LightOn. The selected lineage is:
+
+| Stage | Training states | Selection / calibration / test states | Selected training |
+| --- | ---: | --- | --- |
+| Full-model pilot | 500 | See archived pilot report | Six epochs |
+| Broad simulation | 100,000 | 2,000 / 2,000 / 5,000 | LR 5e-6, epoch 3 |
+| Composition-focused | 65,536 | 4,096 / 2,048 / 8,192 | LR 5e-6, epoch 3 |
+| Hybrid-reference follow-up | 65,536 | 4,096 / 2,048 / 8,192 | Ordinary imitation, LR 5e-6, epoch 3 |
+
+Each stage uses separate whole-game groups across its splits. The last stage trained for 73,728 updates, batch size eight, seed 40260924. Its action loss weights uncertain labels less heavily. The cost-sensitive alternative did not win that study; “teacher-cost” names the study, not the selected objective. The later model-visited studies did not establish a better policy. Training is supervised distillation, not a reimplementation of upstream RLCD.
+
+The final release stage did **no training or temperature fitting**. Five frozen nominees were compared on 8,192 new SDK selection states. The incumbent remained unless a challenger improved reference regret with a four-comparison-adjusted paired whole-game bootstrap bound and satisfied the general/depleted guards. No challenger qualified. The selected complete inference files were frozen before the 16,384-state final test and return benchmark. Temperature buckets remain those fitted on the last training stage's 2,048 calibration states.
+
+## Fresh SDK evaluation
+
+The final test contains equal general and depleted-shoe strata: **16,384 states from 4,840 game groups**. This enriched mix is not the natural frequency of states during ordinary play. Both models use the same canonical three-question SDK and the same states.
+
+| Metric | Previously packaged v0.2 | Selected model |
+| --- | ---: | ---: |
+| Reference action agreement | 92.8345% | **95.9473%** |
+| Reference EV regret, units / decision ↓ | 0.00320423 | **0.00101425** |
+| Next-hit probability Brier distance ↓ | 0.00164981 | **0.00027077** |
+| Dealer probability Brier distance ↓ | 0.00252707 | **0.00087878** |
+| Action preference Brier distance ↓ | 0.10846779 | **0.06084717** |
+
+Selected-model 95% whole-game bootstrap intervals are **95.6276–96.2274%** for agreement and **0.00091122–0.00112688** for reference regret (2,000 replicates). These individual intervals condition on recorded labels and exclude reference sampling uncertainty. Reference-regret reduction is **68.35% by point estimate**. Median measured three-question SDK latency was **19.26 ms** on an RTX 4090, excluding model loading.
+
+The hybrid reference solves finite-shoe public-belief decisions exactly only for supported single-player unsplit states within a 50,000-node limit. Other states use 4,096–16,384 common-world Monte Carlo samples with basic continuation. More samples reduce sampling noise, not continuation bias. Agreement is an imitation measure, not proof of global optimality.
+
+Action probabilities express preferences among legal actions, **not win probabilities**. SDK `confidence` is normalized entropy; use `probabilities` for distributions. Next-hit bust uses the exact peek-conditioned rank marginal. Dealer predictions assume no further player draws. Brier distances measure proximity to reference probabilities, not calibration against realized casino wins.
+
+Canonical SDK inference was used for every final Laya decision. The accelerated batched path was not evaluated for this release; earlier empirical fallback thresholds did not universally guarantee action parity on later candidates.
+
+## Three-million-round return benchmark
+
+Each of three policies played **500,000 independent fresh rounds** and **5,000 independent 100-round continuous blocks**. Initial seeds were paired across policies in ten equally weighted scenarios: six-deck S17 tables at each of 1–7 seats; H17/no-DAS/6:5; two-deck S17; and random tablemates at seven seats. Different actions can diverge after the paired start. No rounds were voided.
+
+Values below are selected-model minus comparator **betting units per 100 original-wager rounds**, including doubles and splits. Intervals use a Bonferroni correction across these four predeclared aggregate contrasts and a normal approximation over independent rounds or blocks.
+
+| Setting | Comparator | Difference | Familywise 95% interval |
+| --- | --- | ---: | --- |
+| Fresh | Basic heuristic | −0.00630 | [−0.08343, +0.07083] |
+| Fresh | Packaged v0.2 | −0.01990 | [−0.09414, +0.05434] |
+| Continuous | Basic heuristic | **+0.30854** | **[+0.03349, +0.58359]** |
+| Continuous | Packaged v0.2 | **+0.27458** | **[+0.01612, +0.53304]** |
+
+Continuous-play advantages are resolved under this design. Fresh-shoe differences are unresolved. Absolute selected-model returns were **−0.49368 units per 100 fresh rounds** and **−0.55542 per 100 continuous rounds**. This is reduced loss in the measured continuous setting, not evidence of profitability. Scenario-level contrasts remain exploratory; no claim applies to every rule configuration. The basic comparator is a transparent multi-deck heuristic, not a universally exact strategy.
+
+## Limits and reproducibility
+
+Selection establishes the best-supported nominee under a declared reference-imitation criterion, not the globally best possible model or training method. The final reference is approximate outside its exact scope. Training stability across many random seeds, live casino deployment, varying bet sizes, insurance, and multiplayer exact optimal control are not established. Final test results are now inspected; future tuning requires new locked evaluation games.
+
+The package includes one Safetensors checkpoint, tokenizer, encoder and calibrated configuration, original training-stage report plus a separately identified release comparison, license/attribution, SHA-256 inventory, raw final evaluation records, evaluator source, training-lineage data and reports, and plotting inputs. Intermediate model weights and optimizer recovery files are excluded. Original local research artifacts are preserved privately. The project records the pinned-download and golden-state reload check separately from the immutable model package.
+
+Weights SHA-256: `64ea2841949f306ed76c3032596c24bdc6cc5a0e45cfe25f180ee62468f151de`. Calibration configuration SHA-256: `f977fd353d0c0c84a44dd08419369dbd3e91f5d80ad38b09da36588140e51096`.
+
+See `evaluation/README.md` for archive contents and reproduction steps. Data is synthetic. Code and model modifications use Apache 2.0; upstream attribution is retained in `NOTICE`. This independent project is not affiliated with Convai Innovations, Answer.AI, or LightOn.

encoder/config.json

@@ -0,0 +1,85 @@
+{
+  "architectures": [
+    "ModernBertForMaskedLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "dtype": "float32",
+  "embedding_dropout": 0.0,
+  "eos_token_id": 50282,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 1024,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 2624,
+  "layer_norm_eps": 1e-05,
+  "layer_types": [
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention"
+  ],
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "pad_token_id": 50283,
+  "position_embedding_type": "absolute",
+  "repad_logits_with_grad": false,
+  "rope_parameters": {
+    "full_attention": {
+      "rope_theta": 160000.0,
+      "rope_type": "default"
+    },
+    "sliding_attention": {
+      "rope_theta": 10000.0,
+      "rope_type": "default"
+    }
+  },
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "transformers_version": "4.57.6",
+  "vocab_size": 50368
+}

evaluation/README.md

@@ -0,0 +1,19 @@
+# Selected model evidence
+
+This package contains one selected set of model weights. The training lineage is pilot → broad 100k → composition → hybrid-reference ordinary imitation. The final selection stage did no fitting. Intermediate weights and optimizer files are excluded.
+
+## Final evaluation
+
+- `final-release-completed.json`: verified portable result record, including all planned comparisons, source identity, selection and final SDK metrics. Its `uploaded: false` records the earlier local evaluation checkpoint; it is historical, not a claim about this downloaded package.
+- `selection-frozen.json`, `run.json`, `summary.json`: selection criterion, original plan and four predeclared return contrasts. This run's configuration paths are descriptive local-artifact aliases.
+- `sdk-audit.json`: selected model's fresh final SDK audit. `fresh-comparison.json` and `continuous-comparison.json` retain full policy/scenario results.
+- `evaluation-records.tar.gz`: original final data manifests/shards, all five selection audits, two final audits, raw return shards/receipts, and `evaluator-source.tar`. No model binaries are inside. Extract into a new directory and extract the source archive into a separate source directory. Use that archived `blackjack.evaluation.compare_evaluations` with the three `returns/{candidate,baseline,basic}/{fresh,continuous}` directories to reproduce each comparison into a new output file. The original saved reports must not be overwritten. This recomputation needs no model inference.
+- `figures/`: standalone SVG/PNG figures. The project's `scripts/plot_final_release.py` regenerates them from `docs/results/final-release-completed.json`. An identical script is included here; its paths expect the project layout.
+
+## Training lineage
+
+`training-lineage/` contains the saved 500-state pilot data, the 100k broad dataset, the composition and hybrid-reference datasets, original source archives, sanitized parameter/selection/training reports, and lineage identities. Data archives preserve shard bytes and manifest hashes. Extract each stage into its own directory; use its archived source and original report's training settings. The broad and pilot source revisions are preserved separately because historical data/training contracts differ.
+
+Reproduction from the upstream pinned model requires retraining the preceding stages; their intermediate weights are intentionally private. Saved states and source enable reproduction of the procedure, not a guarantee of bit-identical optimization across hardware or software. Original stage reports keep their own test counts/baselines. The root `training_report.json` adds a separate `release_evaluation` block for the matched 16,384-state SDK comparison, without rewriting those historical metrics.
+
+The live project records pinned download/inference verification separately, avoiding a self-referential package digest. The original run's generated review package and its manifest remain preserved privately; this presentation package has a new inventory with identical inference files.

evaluation/continuous-comparison.json

@@ -0,0 +1,636 @@
+{
+  "identity": {
+    "version": "paired-policy-v1",
+    "scenarios": {
+      "s17-6d-1p": {
+        "players": 1,
+        "decks": 6,
+        "hit_soft_17": false,
+        "double_after_split": true,
+        "surrender": true,
+        "blackjack_payout": 1.5,
+        "penetration": 0.75,
+        "max_hands": 4,
+        "tablemate_policy": "basic"
+      },
+      "s17-6d-2p": {
+        "players": 2,
+        "decks": 6,
+        "hit_soft_17": false,
+        "double_after_split": true,
+        "surrender": true,
+        "blackjack_payout": 1.5,
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+        "max_hands": 4,
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+  "limitations": "The basic policy is a multi-deck heuristic. Fixed wagers, no insurance; these simulator results do not establish optimality or real-world profitability."
+}

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+      },
+      "inference": "Fixed equal-weight strata; normal 95% intervals over independent rounds or 100-round blocks. Scenario intervals are exploratory, without multiplicity correction.",
+      "pairing": "Identical initial RNG seeds per unit; different actions can diverge subsequent trajectories.",
+      "limitations": "The basic policy is a multi-deck heuristic. Fixed wagers, no insurance; these simulator results do not establish optimality or real-world profitability."
+    }
+  },
+  "research_evidence": true,
+  "activated": false,
+  "uploaded": false,
+  "published": false,
+  "interpretation": "Research incumbent retained under the predeclared selection rule. Continuous-shoe return advantages over basic and packaged v0.2 have positive familywise intervals; fresh-shoe differences are unresolved. Aggregate absolute returns remain negative. This establishes neither global optimality nor real-world profitability.",
+  "next": "Review presentation and usage examples, stage only the chosen model in a clean private Hub history, and verify a pinned download before activation or publication."
+}

evaluation/fresh-comparison.json

@@ -0,0 +1,636 @@
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+      "model_hash": null,
+      "inference_mode": "not_applicable"
+    }
+  },
+  "candidate": "candidate",
+  "paired_candidate_minus": {
+    "baseline": {
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+        -0.0007816102831134994,
+        0.0003836102831134993
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+            0.0009761241088835958
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+            0.0005592464667522634
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+        },
+        "h17-no-das-6to5": {
+          "mean_units_per_round": -0.00069,
+          "standard_error": 0.0009021678092988703,
+          "ci95": [
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+            0.0010782489062257858
+          ],
+          "independent_units": 50000
+        },
+        "s17-2d-3p": {
+          "mean_units_per_round": 0.0013,
+          "standard_error": 0.001260792604657816,
+          "ci95": [
+            -0.0011711535051293193,
+            0.003771153505129319
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+          "independent_units": 50000
+        },
+        "random-tablemates-7p": {
+          "mean_units_per_round": 0.00052,
+          "standard_error": 0.0009365960368497855,
+          "ci95": [
+            -0.0013157282322255799,
+            0.0023557282322255796
+          ],
+          "independent_units": 50000
+        }
+      }
+    }
+  },
+  "inference": "Fixed equal-weight strata; normal 95% intervals over independent rounds or 100-round blocks. Scenario intervals are exploratory, without multiplicity correction.",
+  "pairing": "Identical initial RNG seeds per unit; different actions can diverge subsequent trajectories.",
+  "limitations": "The basic policy is a multi-deck heuristic. Fixed wagers, no insurance; these simulator results do not establish optimality or real-world profitability."
+}

evaluation/plot_final_release.py

@@ -0,0 +1,88 @@
+"""Render the selected model's figures from verified, committed release evidence."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt  # noqa: E402
+from matplotlib.ticker import MultipleLocator  # noqa: E402
+
+ROOT = Path(__file__).resolve().parents[1]
+INK, TEAL, GRAY = "#203648", "#087f6c", "#87929c"
+
+
+def save(fig, name):
+    for ext in ("svg", "png"):
+        path = ROOT / "docs/figures" / f"{name}.{ext}"
+        fig.savefig(path, dpi=180, facecolor="white", metadata={"Date": None} if ext == "svg" else None)
+        if ext == "svg":
+            path.write_text("\n".join(line.rstrip() for line in path.read_text().splitlines()) + "\n")
+    plt.close(fig)
+
+
+def main():
+    data = json.loads((ROOT / "docs/results/final-release-completed.json").read_text())
+    plt.rcParams.update({
+        "font.family": "DejaVu Sans", "font.size": 10, "text.color": INK,
+        "axes.labelcolor": INK, "xtick.color": INK, "ytick.color": INK,
+        "axes.spines.top": False, "axes.spines.right": False, "axes.spines.left": False,
+        "axes.edgecolor": "#cbd1d6", "svg.hashsalt": "laya-final-release-v1",
+    })
+    fig, ax = plt.subplots(figsize=(11.8, 4.7))
+    fig.subplots_adjust(left=0.255, right=0.70, top=0.73, bottom=0.25)
+    fig.text(0.035, 0.93, "One selected model · 3 million simulated rounds", fontsize=17, weight="bold")
+    fig.text(0.035, 0.865, "Continuous-play gains; fresh-shoe differences remain inconclusive", fontsize=11)
+    ax.axvline(0, color=INK, linewidth=1, alpha=0.45)
+    labels = []
+    for y, (mode, comparator) in zip((3, 2, 1, 0), (
+        ("fresh", "basic"), ("fresh", "baseline"), ("continuous", "basic"), ("continuous", "baseline"),
+    ), strict=True):
+        row = data["summary"]["comparisons"][f"{mode}:candidate-minus-{comparator}"]
+        mean, (low, high) = row["units_per_100_rounds"], row["familywise_ci95"]
+        ax.errorbar(mean, y, xerr=[[mean-low], [high-mean]], fmt="o", color=TEAL, capsize=4, markersize=7)
+        ax.text(1.04, y, f"{mean:+.3f}  [{low:+.3f}, {high:+.3f}]",
+                transform=ax.get_yaxis_transform(), va="center", fontsize=10)
+        labels.append(f"{'Fresh shoe' if mode == 'fresh' else 'Continuous'} · vs "
+                      f"{'basic' if comparator == 'basic' else 'packaged v0.2'}")
+    ax.set_yticks([3, 2, 1, 0], labels)
+    ax.tick_params(axis="y", length=0, pad=10)
+    ax.set_xlim(-0.15, 0.65)
+    ax.set_ylim(-0.5, 3.5)
+    ax.set_xlabel("Selected-model advantage · betting units per 100 rounds", labelpad=12)
+    fig.text(0.035, 0.095, "Bonferroni-adjusted 95% family coverage across four planned contrasts; normal approximation.", fontsize=9)
+    fig.text(0.035, 0.045, "Per policy: 500,000 fresh rounds + 5,000 independent 100-round blocks. Absolute mean returns remained negative.", fontsize=9)
+    save(fig, "final-returns")
+
+    fig, axes = plt.subplots(1, 2, figsize=(11.8, 4.5))
+    fig.subplots_adjust(left=0.16, right=0.97, top=0.71, bottom=0.25, wspace=0.65)
+    fig.text(0.035, 0.93, "Closer to the reference on a fresh locked test", fontsize=17, weight="bold")
+    fig.text(0.035, 0.865, "16,384 states · 4,840 game groups · canonical three-question SDK", fontsize=11)
+    for ax, metric, interval, title, scale in (
+        (axes[0], "teacher_agreement", "teacher_agreement_ci95", "Reference action agreement (%) ↑", 100),
+        (axes[1], "teacher_ev_regret", "teacher_ev_regret_ci95", "Reference regret (units / decision) ↓", 1),
+    ):
+        for y, name, color in ((1, "packaged", GRAY), (0, "incumbent", TEAL)):
+            audit = data["final_audits"][name]
+            value = audit["metrics"][metric] * scale
+            low, high = [x * scale for x in audit[interval]]
+            ax.errorbar(value, y, xerr=[[value-low], [high-value]], fmt="o", color=color, capsize=4, markersize=7)
+            label = f"{value:.2f}%" if scale == 100 else f"{value:.6f}"
+            ax.annotate(label, (value, y), xytext=(0, 15), textcoords="offset points", ha="center")
+        ax.set_yticks([1, 0], ["Packaged v0.2", "Selected model"])
+        ax.tick_params(axis="y", length=0)
+        ax.set_title(title, loc="left", fontsize=11, pad=15)
+        ax.set_ylim(-0.55, 1.65)
+        ax.set_xlim((91.5, 97) if scale == 100 else (0, 0.0038))
+        if scale == 1:
+            ax.xaxis.set_major_locator(MultipleLocator(0.001))
+    fig.text(0.035, 0.095, "Individual 95% whole-game bootstrap intervals, conditional on recorded reference labels.", fontsize=9)
+    fig.text(0.035, 0.045, "Equal general/depleted state mix; includes Monte Carlo reference approximations. Agreement is not win probability.", fontsize=9)
+    save(fig, "final-quality")
+
+
+if __name__ == "__main__":
+    main()

evaluation/run.json

@@ -0,0 +1,420 @@
+{
+  "config": {
+    "version": "final-release-selection-v1",
+    "sources": {
+      "incumbent": "local-artifact:teacher-cost-v1",
+      "packaged": "local-artifact:v0.2.0",
+      "composition": "local-artifact:composition-v1",
+      "visited": "local-artifact:evaluated-candidate",
+      "replication_control": "local-artifact:evaluated-candidate"
+    },
+    "checkpoints": {
+      "incumbent": {
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+        "README.md": {
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+          "bytes": 1047
+        },
+        "encoder/config.json": {
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+          "bytes": 2117
+        },
+        "manifest.json": {
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+          "bytes": 1678
+        },
+        "model.safetensors": {
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+          "bytes": 1685197088
+        },
+        "rl_agent_config.json": {
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+          "bytes": 632
+        },
+        "tokenizer/special_tokens_map.json": {
+          "sha256": "ea97ecdbcc73713039d8d64dbb05e3689495c96657fbd9a18f5bed381be81049",
+          "bytes": 694
+        },
+        "tokenizer/tokenizer.json": {
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+          "bytes": 3583228
+        },
+        "tokenizer/tokenizer_config.json": {
+          "sha256": "2966a59b9e9cf122279aec1249e22e5bc7ad8430c754e95031b13fd128d4e560",
+          "bytes": 20840
+        },
+        "training_report.json": {
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+          "bytes": 14664
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evaluation/sdk-audit.json

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+  "scope": "Descriptive analysis of the already frozen final test, not a new independent validation or a basis for selecting this release. Reusing these examples for tuning requires a new final test set.",
+  "reference_method": "Exact optimal finite-shoe single-player unsplit decisions within 50000 cache misses; explicit basic-continuation Monte Carlo fallback otherwise. Per-row method/coverage recorded.",
+  "uncertainty": "Game-cluster bootstrap; conditional on recorded reference values and their stated scope. Excludes Monte Carlo label uncertainty where sampling is used."
+}

evaluation/selection-frozen.json

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training_report.json

@@ -0,0 +1,493 @@
+{
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