Restore dataset-card figures and fix Hugging Face math rendering

Display-only change: restore the existing banner and current quality-cost figure, use supported inline math delimiters, fix pi/@ tokenization, and update the companion GitHub link. Only README.md and its manifest checksum change; all data splits, features, media, predictions, and experimental results remain unchanged.

README.md

@@ -30,6 +30,8 @@ configs:
 
 # RobustRouteBench
 
+![SinapisAI RobustRouteBench: a benchmark for robust model selection across General Text, Multimodal, and Chinese.](assets/robustroutebench-banner.png)
+
 RobustRouteBench is a unified public benchmark for general-text, multimodal,
 and Chinese-language model routing.
 
@@ -91,7 +93,7 @@ This card documents the RobustRouteBench experiment in Section 6.3 of the Sinapi
 
 **Mio selector** means the request-level selection component trained separately on each scenario's training split, with hyperparameters and operating points selected using development data. These results do not evaluate the hosted Mio 1.0 Lite API or the end-to-end SinapisAI platform. Mio's internal training and inference implementation is not included in this dataset release.
 
-The reported experiment covers the fixed-pool, single-request setting ($T=1$) under monetary budgets. Model-side evidence is learned from benchmark training observations; the Section 3 capability-profile construction pipeline is not jointly evaluated here. The results do not isolate profile encoding, pairwise supervision, or near-tie selection, and do not evaluate pool organization, multi-step handoff, or the Section 4.2 rank-fusion rule.
+The reported experiment covers the fixed-pool, single-request setting (\\(T=1\\)) under monetary budgets. Model-side evidence is learned from benchmark training observations; the Section 3 capability-profile construction pipeline is not jointly evaluated here. The results do not isolate profile encoding, pairwise supervision, or near-tie selection, and do not evaluate pool organization, multi-step handoff, or the Section 4.2 rank-fusion rule.
 
 - [Evaluation populations and development exclusion](#evaluation-populations-and-development-exclusion)
 - [Benchmark composition and transformations](#benchmark-composition-and-transformations)
@@ -250,21 +252,23 @@ These are aliases **of the fitted implementations in this experiment**, not a cl
 
 ## Metrics and reporting conventions
 
-For each method and reporting group, evaluate 100 evenly spaced nominal monetary budgets from the cheapest to the most expensive fixed-model mean cost on that group's held-out requests. These endpoints use evaluation-population cost aggregates, not training/development costs, and are shared across methods. They set the evaluator's reporting grid without supplying request-specific held-out outcomes to selectors. Write the common grid as $\mathcal G=\{C^{(1)},\ldots,C^{(K)}\}$ with $K=100$. Predicted cost determines candidate feasibility; predicted utility or preference selects the model. Held-out outcomes then determine mean utility $Q_\pi(C)$ and realized mean cost $\bar c_\pi(C)$. A nominal budget is not the measured expenditure, and predicted-cost feasibility does not guarantee a realized-cost bound.
+For each method and reporting group, evaluate 100 evenly spaced nominal monetary budgets from the cheapest to the most expensive fixed-model mean cost on that group's held-out requests. These endpoints use evaluation-population cost aggregates, not training/development costs, and are shared across methods. They set the evaluator's reporting grid without supplying request-specific held-out outcomes to selectors. Write the common grid as \\(\mathcal G=\{C^{(1)},\ldots,C^{(K)}\}\\) with \\(K=100\\). Predicted cost determines candidate feasibility; predicted utility or preference selects the model. Held-out outcomes then determine mean utility \\(Q_\pi(C)\\) and realized mean cost \\(\bar c_\pi(C)\\). A nominal budget is not the measured expenditure, and predicted-cost feasibility does not guarantee a realized-cost bound.
 
 For this offline monetary-budget sweep, a request with no candidate satisfying the predicted cost budget falls back to the model with the lowest predicted cost; its measured quality and cost remain in the reported point. This fallback can exceed the nominal budget even in predicted cost. It is an evaluation convention, distinct from the Section 4.2 deployment rule that reports unmet constraints when no feasible candidate exists.
 
-Let $Q_{\mathrm{strongest}}$ and $\bar c_{\mathrm{strongest}}$ be the quality and mean cost of the strongest fixed model on the same split and candidate pool. The primary metrics are:
+Let \\(Q_{\mathrm{strongest}}\\) and \\(\bar c_{\mathrm{strongest}}\\) be the quality and mean cost of the strongest fixed model on the same split and candidate pool. The primary metrics are:
 
 $$
-Q_{\mathrm{peak}}(\pi)=\max_{C\in\mathcal G}Q_\pi(C),\qquad
-\bar c_\pi@\tau=\min\{\bar c_\pi(C):C\in\mathcal G,\ Q_\pi(C)\geq\tau Q_{\mathrm{strongest}}\}.
+\begin{aligned}
+Q_{\mathrm{peak}}(\pi)&=\max_{C\in\mathcal G}Q_\pi(C),\\
+\bar{c}_{\pi}\mathbin{@}\tau&=\min\{\bar c_\pi(C):C\in\mathcal G,\ Q_\pi(C)\geq\tau Q_{\mathrm{strongest}}\}.
+\end{aligned}
 $$
 
-Cost@90, Cost@95, and Cost@100 use $\tau=0.90,0.95,1.00$, respectively. An unattained target is **N/R**; no extrapolation supplies a missing operating point. For a reached target:
+Cost@90, Cost@95, and Cost@100 use \\(\tau=0.90,0.95,1.00\\), respectively. An unattained target is **N/R**; no extrapolation supplies a missing operating point. For a reached target:
 
 $$
-\operatorname{Saving}_\pi@\tau=1-\frac{\bar c_\pi@\tau}{\bar c_{\mathrm{strongest}}}.
+\operatorname{Saving}_{\pi}\mathbin{@}\tau=1-\frac{\bar{c}_{\pi}\mathbin{@}\tau}{\bar c_{\mathrm{strongest}}}.
 $$
 
 Positive savings mean lower cost than always selecting the strongest fixed model; negative savings mean higher cost. Savings are also N/R when the target is unreached.
@@ -283,7 +287,7 @@ $$
 \bigl(C^{(g+1)}-C^{(g)}\bigr).
 $$
 
-This guarantees $\operatorname{nAUC}(\pi)\leq Q_{\mathrm{peak}}(\pi)$. Fixed-model references, artificial endpoints, and realized-cost completion points never enter the router curve. The report's quality-cost plot instead uses realized mean cost on a logarithmic x-axis; its geometric area is **not** nAUC. For that visualization, duplicate realized-cost points retain the highest quality, dominated points are removed, and the frontier is not extrapolated. Baselines are plotted at their least-cost peak-quality points, with the strongest baseline by nAUC highlighted separately on each split.
+This guarantees \\(\operatorname{nAUC}(\pi)\leq Q_{\mathrm{peak}}(\pi)\\). Fixed-model references, artificial endpoints, and realized-cost completion points never enter the router curve. The report's quality-cost plot instead uses realized mean cost on a logarithmic x-axis; its geometric area is **not** nAUC. For that visualization, duplicate realized-cost points retain the highest quality, dominated points are removed, and the frontier is not extrapolated. Baselines are plotted at their least-cost peak-quality points, with the strongest baseline by nAUC highlighted separately on each split.
 
 A diagnostic oracle selects the best observed model independently for each request:
 
@@ -291,13 +295,13 @@ $$
 Q_{\mathrm{oracle}}=\frac{1}{|\mathcal X|}\sum_{x\in\mathcal X}\max_{f_i\in\mathcal C}y_i(x),
 $$
 
-where $\mathcal X$ is the reporting population, $\mathcal C$ is its candidate pool, and $y_i(x)$ is the held-out utility. The oracle supplies no information to a selector. An evaluation record should retain the raw AUC, nominal interval endpoints, and strongest/cheapest fixed-model and oracle diagnostics so normalization can be reconstructed.
+where \\(\mathcal X\\) is the reporting population, \\(\mathcal C\\) is its candidate pool, and \\(y_i(x)\\) is the held-out utility. The oracle supplies no information to a selector. An evaluation record should retain the raw AUC, nominal interval endpoints, and strongest/cheapest fixed-model and oracle diagnostics so normalization can be reconstructed.
 
 **Currency.** Published `cost_cny` arrays always use CNY. The frozen report tables below retain their original units: USD for General Text and Multimodal, CNY for Chinese. Divide the first two scenarios' `cost_cny` values by **7.2** to recover those reporting units; convert budget values consistently. This is a frozen benchmark constant, not a current exchange rate or the pricing convention of the report's separate Mio 1.0 Lite experiment. A uniform currency rescaling of costs and budgets leaves nAUC unchanged. Multimodal costs are frozen USD proxies, not live service quotes.
 
 **Evaluator compatibility.** The accompanying Model Intelligence Toolkit evaluator computes a realized-cost Pareto AUC with a cheapest-fixed-model reference and endpoint completion. That quantity is not the report's nominal-budget nAUC above; its threshold costs can also include the inserted reference. To match this report, use actual router budget points only, align repeated runs on their common nominal-budget grid, average the utility and realized-cost curves pointwise, and then compute peak quality, nAUC, and target costs from that aggregate curve. A field named `normalized_auc` in another evaluator is not sufficient to establish metric compatibility.
 
-**Repetitions and uncertainty.** Multimodal uses three fitted runs for every implementation, with seeds 20260917, 20260918, and 20260919, 4,000 training requests, 45 candidates, shared CLIP text/image fusion features, and the same evaluation requests and 100-point grid for each reporting population. Fitting seeds vary; evaluation populations, source-group exclusions, and candidate identities are fixed. The reported curve is the pointwise mean of those three aligned run curves, formed before the cumulative maximum or any curve statistic is computed. Quality entries show the aggregate-curve statistic ± its exact nonparametric run-bootstrap standard error, obtained from all $3^3$ ordered resamples of the run curves. General Text and Chinese each use one fitted run per method (seed 20260917). These errors describe fitting variation, not request-sampling uncertainty. Target costs are computed from the aggregate curve; individual-run reach counts are retained only as diagnostics. Current tables contain no paired request-bootstrap intervals or statistical-significance tests.
+**Repetitions and uncertainty.** Multimodal uses three fitted runs for every implementation, with seeds 20260917, 20260918, and 20260919, 4,000 training requests, 45 candidates, shared CLIP text/image fusion features, and the same evaluation requests and 100-point grid for each reporting population. Fitting seeds vary; evaluation populations, source-group exclusions, and candidate identities are fixed. The reported curve is the pointwise mean of those three aligned run curves, formed before the cumulative maximum or any curve statistic is computed. Quality entries show the aggregate-curve statistic ± its exact nonparametric run-bootstrap standard error, obtained from all \\(3^3\\) ordered resamples of the run curves. General Text and Chinese each use one fitted run per method (seed 20260917). These errors describe fitting variation, not request-sampling uncertainty. Target costs are computed from the aggregate curve; individual-run reach counts are retained only as diagnostics. Current tables contain no paired request-bootstrap intervals or statistical-significance tests.
 
 **Aggregation and latency.** Macro nAUC is the unweighted mean of the six standard/pooled-robust split values, not a pooled curve over different candidate pools, units, or budget ranges. Ties share rank. Latency is auxiliary: realized latency at each budget and at peak, total router inference time, and mean router time per request are useful diagnostics, but they neither determine the primary ranking nor impose a hard latency constraint in this experiment.
 
@@ -315,6 +319,10 @@ nAUC ranks second and Multimodal combined-perturbation nAUC ranks third.
 Public baselines use fixed configurations; Mio uses designated development data
 for configuration selection, not a matched hyperparameter-search budget.
 
+![Quality-cost frontiers for General Text, Multimodal, and Chinese on standard and robust splits, comparing Mio selector with the 12 distinct baselines.](assets/quality-cost-results.png)
+
+*Green: Mio selector frontier and peak. Hollow circles: baseline peaks. Blue diamond: best baseline by nAUC. Multimodal points summarize the pointwise-mean curve over three fitted runs. The log x-axis shows realized cost (USD for General Text and Multimodal; CNY for Chinese); the plotted area is not nominal-budget nAUC.*
+
 The following tables are generated from the corrected, public-data evaluations.
 All 12 baselines were refitted per scenario; Text Mio was refitted/recalibrated,
 Multimodal Mio was refitted with CLIP features, and unchanged Chinese Mio
@@ -485,7 +493,7 @@ Ranks are among 13 distinct implementations; Multimodal metrics use the pointwis
 
 This section specifies analyses supported by the benchmark design. The current primary tables do **not** report the paired decomposition, bootstrap confidence intervals, or a complete weakest-group analysis.
 
-For a complete routed execution, let $y_\pi(x)$ be utility and let nonnegative analysis weights $w(x)$ sum to one over a declared population $\mathcal X$. A paired policy comparison uses:
+For a complete routed execution, let \\(y_\pi(x)\\) be utility and let nonnegative analysis weights \\(w(x)\\) sum to one over a declared population \\(\mathcal X\\). A paired policy comparison uses:
 
 $$
 \bar y_\pi=\sum_{x\in\mathcal X}w(x)y_\pi(x),\qquad
@@ -494,7 +502,7 @@ $$
 
 Weights encode the source-group and within-group estimand and must be fixed independently of final outcomes. A cluster bootstrap resamples source groups within task strata while keeping variants and competing policies' outcomes together. Repeated training seeds quantify fitting variation separately. Methods, thresholds, group definitions, and interval procedures should be frozen before final evaluation.
 
-For paired General Text or Chinese requests, let $\mathsf T$ be a validated transformation and $\pi(x)$ the selected model index with context, service snapshot, and allowance fixed. Total paired change decomposes as:
+For paired General Text or Chinese requests, let \\(\mathsf T\\) be a validated transformation and \\(\pi(x)\\) the selected model index with context, service snapshot, and allowance fixed. Total paired change decomposes as:
 
 $$
 \begin{aligned}
@@ -535,7 +543,7 @@ The numerical source is `report/aggregate-results.json`, schema
 `routestressbench-report-v3`, with the same content used to generate the report
 tables and figure. `report/reproduction-manifest.json` identifies every ID-aligned,
 SHA256-checked per-run prediction file. Public baseline configurations and fitting
-code are in the [companion repository](https://github.com/SinapisAI/collective-model-intelligence-private).
+code are in the [companion repository](https://github.com/SinapisAI/collective-model-intelligence).
 
 The companion command `python -m examples.reproduce_report --mode replay --output runs/replay`
 recomputes report metrics from public data and prediction arrays. `--mode fit`

manifest.json

@@ -94,8 +94,8 @@
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