Restore verified Text scores, publish CLIP multimodal fits and reproducible report bundle

20,167 exact-match successful score restorations; known failures stay zero. Unverified legacy scores unchanged. Shared CLIP text/all-image late fusion, 12 canonical public baselines and ID-aligned prediction replay for all three scenarios.

README.md

@@ -30,24 +30,8 @@ configs:
 
 # RobustRouteBench
 
-![SinapisAI RobustRouteBench: a benchmark for robust model selection across General Text, Multimodal, and Chinese.](assets/robustroutebench-banner.png)
-
-**RobustRouteBench** evaluates how model routers balance quality and cost when requests change in wording, language, context, or visual content. It covers General Text, Multimodal, and Chinese, each with its own fixed candidate pool.
-
-This benchmark accompanies Section 6.3 of the SinapisAI technical report [**Toward Collective Model Intelligence: Characterizing and Coordinating Models**](https://github.com/SinapisAI/sinapisai-tech-report/blob/main/main.pdf). Reported results cover offline, fixed-pool, single-request model selection ($T=1$) under monetary budgets.
-
-## Dataset overview
-
-| Scenario / config | Models | Train | Standard test (final) | Robust test (final) |
-| --- | ---: | ---: | ---: | ---: |
-| General Text / `text` | 63 | 3,000 | 4,188 (3,688) | 3,000 (2,894) |
-| Multimodal / `multimodal` | 45 | 4,000 | 5,000 (4,600) | 2,000 (1,900) |
-| Chinese / `chinese` | 10 | 7,590 | 4,400 (3,900) | 2,100 (2,000) |
-| Total | — | 14,590 | 13,588 (12,188) | 7,100 (6,794) |
-
-Parentheses show the final evaluation counts after excluding development source groups: **18,982 requests** in total. Candidate pools are scenario-specific; the model counts do not form one combined pool.
-
-## Quick start
+RobustRouteBench is a unified public benchmark for general-text, multimodal,
+and Chinese-language model routing.
 
 ```python
 from datasets import load_dataset
@@ -57,97 +41,282 @@ multimodal = load_dataset("SinapisAI/RobustRouteBench", "multimodal")
 chinese = load_dataset("SinapisAI/RobustRouteBench", "chinese")
 ```
 
-Each config provides `train`, `validation`, and `test` splits. Rows contain a request and aligned per-model outcomes:
+Each scenario exposes separate `train`, `validation`, and `test` splits. The
+500-row validation split is a convenience overlay containing 400 standard and
+100 robust queries. Those rows intentionally remain in the complete test split,
+so validation and test overlap by design.
+
+The test split contains both standard and robustness evaluation rows. Use
+`evaluation_type` (`standard` or `robust`) and `variant_type` to report slices.
+
+Each row is query-centric. `performance`, `cost_cny`, and `latency_ms` are
+aligned to `model_ids`. All costs use CNY; historical USD proxies use the frozen
+benchmark conversion `1 USD = 7.2 CNY`.
+
+Source outcomes that were not successful have performance zero. No source
+diagnostic payloads or raw model responses are published. General Text and Chinese
+use BGE-M3 features. Multimodal uses frozen
+[CLIP ViT-B/32](https://huggingface.co/openai/clip-vit-base-patch32) features
+([ICML paper](https://proceedings.mlr.press/v139/radford21a.html)), checkpoint
+`3d74acf9a28c67741b2f4f2ea7635f0aaf6f0268`. Full prompt tokens are split into
+75-token payload chunks plus BOS/EOS. Normalize each 512-dimensional text chunk
+vector and pool with payload-token-count weights. Encode every referenced RGB
+image using official 224-pixel resizing/center-cropping, normalize each vector,
+and pool images equally. Normalize the two pooled modality vectors, concatenate,
+and divide by √2. This yields 1,024-dimensional **late fusion**, not Qwen joint
+encoding. All methods/seeds use the same features. Missing images are errors;
+labels and reference answers never enter the encoder.
 
-- `model_ids` indexes the `performance`, `cost_cny`, and `latency_ms` arrays. Unsuccessful source outcomes have performance zero.
-- `evaluation_type` separates `standard` and `robust` test rows; `variant_type` identifies the perturbation.
-- Costs in the dataset use **CNY**. Released BGE-M3 arrays provide optional text-only baseline features; other encoders are allowed.
+The corrected release restores exactly **20,167** successful General Text scores
+whose model, request, and answer match the historical nonzero record, including
+522 cells in the validation overlay. Known failed requests retain zero scores.
+Other legacy zeros without a verified scoring source remain unchanged. This is a
+provenance-confirmed repair, **not independent regrading of every legacy outcome**.
 
-For the reported Multimodal experiment, **all compared methods share [Qwen3-VL-Embedding-8B](https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B) representations**, jointly encoding each request's text and images ([technical report](https://arxiv.org/abs/2601.04720)). Image and combined perturbations therefore enter the selector through the multimodal request representation.
+The repository is self-contained. All MMR-4000 training images and all standard
+and robustness evaluation media are stored under `media/multimodal/`; no other
+dataset repository is required to train or evaluate a router. See `LICENSES.md`
+for upstream attribution and terms. This compilation uses `license: other`; no
+blanket license is asserted over all upstream questions or images.
 
-All multimodal training and evaluation media are included under `media/multimodal/`. Field definitions are in [normalized-schema.json](normalized-schema.json).
+## Evaluation guide
 
-## Evaluation protocol
+Companion to **SinapisAI: A Model Intelligence System for Collective AI**, Section 6.3 (October 2026).
 
-### Separate development and final evaluation
+This card documents the RobustRouteBench experiment in Section 6.3 of the SinapisAI technical report, including the protocol and complete results previously collected in its RobustRouteBench appendix. The scored benchmark evaluates **offline request-level model selection** under a fixed candidate pool. It covers semantic rewrites, language changes, context noise, and multimodal perturbations. Model-ecosystem events and service recovery are complementary **extension protocols**, with no numerical event-replay or live-service results in the reported snapshot.
 
-**Validation and the published test split overlap by design.** Each scenario's 500 validation rows contain 400 standard and 100 robust requests. Before final scoring, exclude **every source group represented in validation**, including linked base requests and variants; dropping only `is_validation=True` rows is insufficient.
+**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.
 
-Group General Text and Chinese by `base_query_id`, falling back to `query_id`; group Multimodal by `source_sample_id`. This leaves 6,582 / 6,500 / 5,900 final requests, respectively, with no development-group overlap. Training groups are also disjoint from development and final-test groups under these identifiers. Benchmark sources and task domains can still be shared, so this is base-query/source-sample separation rather than a document-level or cross-benchmark independence claim. Development used a public subset of the same benchmark release; the final evaluation is group-disjoint, not an independently administered hidden benchmark.
+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.
 
-<details>
-<summary>Example: construct the final evaluation splits</summary>
+- [Evaluation populations and development exclusion](#evaluation-populations-and-development-exclusion)
+- [Benchmark composition and transformations](#benchmark-composition-and-transformations)
+- [Compared implementations](#compared-implementations)
+- [Metrics and reporting conventions](#metrics-and-reporting-conventions)
+- [Complete reported results](#complete-reported-results)
+- [Attribution and uncertainty protocol](#attribution-and-uncertainty-protocol)
+- [Model-ecosystem and service extensions](#model-ecosystem-and-service-extensions)
+- [Reproducibility and release boundaries](#reproducibility-and-release-boundaries)
+
+## Evaluation populations and development exclusion
+
+**The published `test` split is the complete evaluation inventory, not the report's final holdout.** Its overlap with the 500-row `validation` overlay is intentional. To reproduce the report's final evaluation, remove every source group represented in validation, including linked base requests and variants, before calculating any test metric. Removing only rows with `is_validation=True` is insufficient for the paired text scenarios.
+
+| Scenario / config | Fixed candidate models | Train | Published standard | Final standard | Published robust | Final robust | Unique scenario instances |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| General Text / `text` | 63 | 3,000 | 4,188 | 3,688 | 3,000 | 2,894 | 10,188 |
+| Multimodal / `multimodal` | 45 | 4,000 | 5,000 | 4,600 | 2,000 | 1,900 | 11,000 |
+| Chinese / `chinese` | 10 | 7,590 | 4,400 | 3,900 | 2,100 | 2,000 | 14,090 |
+| Total | Scenario-specific | 14,590 | 13,588 | 12,188 | 7,100 | 6,794 | 35,278 |
+
+Validation contains 400 standard and 100 robust requests per scenario and is already counted in the published evaluation inventory. Group exclusion removes 606 General Text, 500 Multimodal, and 600 Chinese rows, leaving **18,982 final-test requests** (6,582 / 6,500 / 5,900). The resulting development-to-test source-group overlap is zero. Document- and repository-level identities are not consistently available, so this is a base-query/source-sample separation claim, not a stronger document-level independence claim. Development used a public subset of the same benchmark release; this is not an independently administered hidden benchmark.
+
+Training groups are disjoint from development and final-test groups under the same base-query/source-sample identifiers. Benchmark sources and task domains can still be shared across these roles; the group separation does not establish cross-benchmark or cross-domain generalization.
+
+The grouping rule in the frozen result archive is `base_query_id` when present, otherwise `query_id`, for General Text and Chinese; Multimodal uses `source_sample_id`. The following read-only filtering example operates on the loaded datasets above and does not change any published split:
 
 ```python
+from collections import Counter
+
+expected = {
+    "text": {"standard": 3688, "robust": 2894},
+    "multimodal": {"standard": 4600, "robust": 1900},
+    "chinese": {"standard": 3900, "robust": 2000},
+}
+
 def source_group(row, scenario):
     if scenario == "multimodal":
         key = row["source_sample_id"]
         if key is None or key == "":
             raise ValueError("Missing multimodal source group")
-        return key
-    return row["base_query_id"] or row["query_id"]
+    else:
+        key = row["base_query_id"] or row["query_id"]
+    return (scenario, key)
 
 final_tests = {}
 for scenario, ds in [("text", text), ("multimodal", multimodal), ("chinese", chinese)]:
     dev_groups = {source_group(row, scenario) for row in ds["validation"]}
-    final_tests[scenario] = ds["test"].filter(
+    final = ds["test"].filter(
         lambda row: source_group(row, scenario) not in dev_groups
     )
-
-assert [len(final_tests[s]) for s in ("text", "multimodal", "chinese")] == [6582, 6500, 5900]
+    assert not dev_groups.intersection(source_group(row, scenario) for row in final)
+    assert Counter(final["evaluation_type"]) == expected[scenario]
+    final_tests[scenario] = final
 ```
 
-</details>
+Each scenario has its own fixed action space, shared by its training, standard, and robust populations. The pools may overlap in model family or version; **63, 45, and 10 are not one global pool**. Candidate identities, train/development roles, final requests, generation settings, and outcome matrices are held fixed across methods within a scenario. Test utilities, references, and realized costs are evaluation labels and must remain unavailable to the selector at decision time.
+
+## Benchmark composition and transformations
+
+### General Text
+
+| Domain | Train | Published standard | Published robust |
+| --- | ---: | ---: | ---: |
+| Code | 1,386 | 859 | 268 |
+| General Reasoning | 435 | 649 | 474 |
+| Knowledge QA | 388 | 616 | 506 |
+| Long Context | 0 | 1,000 | 1,000 |
+| Mathematical Reasoning | 438 | 574 | 410 |
+| Reading Understanding | 353 | 490 | 342 |
+
+The 3,000 robust instances derive from 2,907 unique base queries retained in standard test. All 1,000 Long Context queries serve as robustness bases, and none appear in training. The language-variation set contains 999 instances with Chinese characters. Long-context noise inserts one to three background notes whose removal exactly recovers the original request.
+
+### Multimodal
+
+MMR-4000 training examples are sampled from 18 [MMR-Bench-V2][ma2026mmrbench] sub-benchmarks. VisionQA-5000 standard evaluation has the following composition:
 
-Train and tune on training/development data only. Methods share the same candidate pool and final requests within each scenario; held-out utilities, references, and realized costs are evaluation labels, unavailable to the router at decision time.
+| Capability group | Source benchmark | Published standard instances |
+| --- | --- | ---: |
+| General VQA | [VQAv2][goyal2017vqav2] | 1,500 |
+| Science / Diagram | [AI2D][kembhavi2016ai2d] | 676 |
+| Science / Diagram | [ScienceQA][lu2022scienceqa] | 667 |
+| Science / Diagram | [ScienceQA-IMG][lu2022scienceqa] | 657 |
+| Document / OCR | [DocVQA][mathew2021docvqa] | 183 |
+| Document / OCR | [OCR-VQA][mishra2019ocrvqa] | 1,317 |
+| Total | | 5,000 |
+
+A multimodal request is an ordered sequence of text and visual segments and may contain multiple images and answer options. The 2,000 robust items are sampled **independently** of VisionQA-5000, using the same 45-model action space. Standard versus robust differences are distribution-level comparisons, not per-request clean-versus-perturbed losses.
+
+### Chinese
+
+| Coverage | Source benchmark |
+| --- | --- |
+| Alignment | [AlignBench][liu2023alignbench] |
+| Knowledge and examinations | [C-Eval][huang2023ceval] |
+| Instruction following | [CIF-Bench][li2024cifbench] |
+| Language, NLI, and reading | [CLUE benchmark family][xu2020clue] |
+| Clinical medicine | [CMB-Clin from CMB][wang2023cmb] |
+| Factual knowledge | [Chinese SimpleQA][he2025chinesesimpleqa] |
+| Financial knowledge | [FinEval][guo2023fineval] |
+| Constraint following | [FollowBench-zh][jiang2024followbench] |
+| Standardized examinations | [GAOKAO-Bench][zhang2023gaokao] |
+| Legal knowledge | [LawBench][fei2023lawbench] |
+| Long-context tasks | [Chinese subsets of LongBench][bai2023longbench] |
+
+Each source retains its task and evaluator semantics; the utility is not assumed to be the same accuracy measure across all tasks. The published `performance` arrays are the scored outcomes, with unsuccessful source outcomes set to zero as specified above.
+
+### Robustness groups and pairing
+
+| Scenario | Robustness type | Published instances | Final-test instances | Relation to standard population |
+| --- | --- | ---: | ---: | --- |
+| General Text | Semantic rewrite | 1,000 | 963 | Paired base retained |
+| General Text | Language variation | 1,000 | 964 | Paired base retained |
+| General Text | Long-context noise | 1,000 | 967 | Paired base retained |
+| Multimodal | Text rewrite | 500 | 475 | Independently sampled |
+| Multimodal | Context noise | 500 | 475 | Independently sampled |
+| Multimodal | Image variation | 500 | 475 | Independently sampled |
+| Multimodal | Combined perturbation | 500 | 475 | Independently sampled |
+| Chinese | Semantic rewrite | 700 | 667 | Paired, disjoint bases |
+| Chinese | English translation | 700 | 666 | Paired, disjoint bases |
+| Chinese | Prefix noise | 700 | 667 | Paired, disjoint bases |
 
-General Text and Chinese contain paired base requests and variants. Multimodal standard and robust sets are independently sampled, so their difference is a distribution-level comparison. Even in the paired scenarios, an overall standard–robust difference is not a paired effect: the standard set also contains requests outside the robust base set.
+Transformations must preserve meaning, necessary evidence, the answer contract, and scorer validity. Image variation assigns 100 instances each to JPEG degradation, controlled cutout, Gaussian noise, Gaussian blur, and contrast reduction. Combined multimodal perturbations change text, context, and image together. The three Chinese types use disjoint base requests, all retained in standard test. Even for the paired text scenarios, the overall standard population contains requests outside the robust base set, so subtracting overall standard and robust metrics is not a paired transformation effect.
 
-### Metrics
+## Compared implementations
 
-Evaluate each method on **100 evenly spaced nominal budgets**, from the cheapest to the most expensive fixed-model mean cost on the reporting population. The endpoints use held-out cost aggregates for evaluator-side budget construction and are shared across methods; they are not training/development statistics. Predicted cost determines feasibility; predicted quality or preference selects the model. Report quality and realized cost from held-out outcomes.
+The report ranks **13 distinct implementations**: Mio selector and 12 baselines evaluated through [ORBIT][lai2026orbit]'s common interfaces. All use the same scenario-specific candidate pool and available training/development evidence.
+
+| Reported implementation | Method source |
+| --- | --- |
+| Avengers | [The Avengers][zhang2025avengers] |
+| Avengers-Pro | [Performance-Efficiency Optimized Routing][zhang2025avengerspro] |
+| Causal RM-Softmax | [Causal LLM Routing, RM-Softmax][tsiourvas2025causalrouting] |
+| Eagle | [Eagle][zhao2024eagle] |
+| EmbedLLM-RouteJudge | [EmbedLLM][zhuang2024embedllm] |
+| Mio selector | Request-oriented selection, technical report Section 4.2; scenario-specific offline training |
+| GraphRouter | [GraphRouter][feng2024graphrouter] |
+| HybridLLM | [Hybrid LLM][ding2024hybridllm] |
+| kNN | [TensorOpera][stripelis2024tensoropera] / [ORBIT][lai2026orbit] baseline |
+| MLP | [TensorOpera][stripelis2024tensoropera] / [ORBIT][lai2026orbit] baseline |
+| MIRT | [IRT-Router, MIRT variant][song2025irtrouter] |
+| RouteLLM-MF | [RouteLLM, matrix-factorization variant][ong2024routellm] |
+| SVM Router | [TensorOpera][stripelis2024tensoropera] / [ORBIT][lai2026orbit] baseline |
 
-When no candidate meets the predicted cost budget, the offline sweep falls back to the model with the lowest predicted cost and retains its measured outcome. This fallback can exceed the nominal budget even in predicted cost. It is an evaluation convention; Section 4.2's deployment rule reports unmet constraints when its feasible set is empty.
+The implementation audit found five groups of additional labels with identical adapters/configurations and identical `model_ids`, performance-score, cost-score, latency-score, and selection-policy arrays across every evaluated scenario and seed:
 
-| Metric | Definition |
+| Ranked implementation | Additional implementation aliases in this experiment |
 | --- | --- |
-| Peak quality | Highest mean utility reached on the budget grid. |
-| nAUC | Trapezoidal area under the cumulative-best quality curve in increasing **nominal-budget** order, divided by the full nominal-budget interval. Use actual router points only; no fixed-model references, added endpoints, or extrapolation. |
-| Cost@90 / 95 / 100 | Lowest **realized mean cost** among grid points reaching 90% / 95% / 100% of the strongest fixed model's quality on the same split. **N/R** means the target was not reached. |
+| Causal RM-Softmax | RM-Classification, RM-Interval ([source][tsiourvas2025causalrouting]) |
+| Eagle | RouteLLM-SW Ranking ([RouteLLM][ong2024routellm]) |
+| EmbedLLM-RouteJudge | Model-SAT ([Capability Instruction Tuning][zhang2025modelsat]), [RouterDC][chen2024routerdc] |
+| MLP | [OmniRouter][mei2025omnirouter], RouteLLM-BERT ([RouteLLM][ong2024routellm]) |
+| MIRT | NIRT ([IRT-Router][song2025irtrouter]) |
+
+These are aliases **of the fitted implementations in this experiment**, not a claim that the original research methods are equivalent. They receive no extra rows or rank weight and are not independent reproductions of every cited method. Each adapter must declare its input features, fitted parameters, predicted scores, selection policy, and deviations from the source method.
+
+## 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 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:
+
+$$
+Q_{\mathrm{peak}}(\pi)=\max_{C\in\mathcal G}Q_\pi(C),\qquad
+\bar c_\pi@\tau=\min\{\bar c_\pi(C):C\in\mathcal G,\ Q_\pi(C)\geq\tau Q_{\mathrm{strongest}}\}.
+$$
+
+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:
 
-Macro nAUC is the unweighted mean over the six scenario/split results. Latency is auxiliary: it does not enter the selection objective, impose a hard constraint, or determine the ranking. A nominal budget does not guarantee a realized-cost bound. The toolkit's realized-cost Pareto AUC is a different metric. To reproduce the report, align repeated runs on the same nominal-budget grid, average their utility and realized-cost curves pointwise, and then compute the cumulative maximum, peak quality, nAUC, and target costs.
+$$
+\operatorname{Saving}_\pi@\tau=1-\frac{\bar c_\pi@\tau}{\bar c_{\mathrm{strongest}}}.
+$$
 
-**Pooled Robust is evaluated directly on the combined robust request population.** Each robustness type is evaluated separately on its own cost interval; averaging type-level nAUC values does not generally recover pooled Robust nAUC. Both budget grids and the nonlinear cumulative-maximum envelope can differ. This also applies to the four equally sized Multimodal robustness types.
+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.
 
-**Units and runs.** Dataset costs are CNY; the report tables retain USD for General Text and Multimodal and CNY for Chinese. Convert the first two using the fixed benchmark constant **1 USD = 7.2 CNY**, including budgets. Multimodal cost proxies are fixed, not live prices. Multimodal metrics are computed from the pointwise mean of three run curves; **± denotes exact run-bootstrap standard error**, obtained from all $3^3$ ordered resamples of the run curves with the complete curve statistic recomputed. General Text and Chinese use one run. Threshold costs come from the aggregate curve; `(r/3)` records how many individual runs reach the target. **N/R can coexist with a nonzero reach count** when the aggregate curve does not reach the target. These errors describe fitting variation, not request-sampling confidence intervals.
+For nAUC, first form the cumulative maximum of quality in increasing **nominal-budget** order:
 
-## Results
+$$
+Q_\pi^{\mathrm{env}}(C^{(g)})=\max_{1\leq h\leq g}Q_\pi(C^{(h)}).
+$$
 
-<!-- BEGIN GENERATED SUMMARY -->
+Then integrate by the trapezoidal rule and divide by the full nominal-budget interval:
 
-Mio selector achieves macro nAUC **0.6375**, followed by MLP at **0.6151**, across 13 distinct implementations. The best baseline below is selected independently for each split.
+$$
+\operatorname{nAUC}(\pi)=\frac{1}{C^{(K)}-C^{(1)}}\sum_{g=1}^{K-1}
+\frac{Q_\pi^{\mathrm{env}}(C^{(g)})+Q_\pi^{\mathrm{env}}(C^{(g+1)})}{2}
+\bigl(C^{(g+1)}-C^{(g)}\bigr).
+$$
 
-| Scenario | Split | Mio selector nAUC | Best baseline | Baseline nAUC |
-| --- | --- | ---: | --- | ---: |
-| General Text | Standard | 0.5908 | Eagle | 0.6045 |
-| General Text | Robust | 0.5299 | MLP | 0.5052 |
-| Multimodal | Standard | 0.7109 ± 0.0083 | Causal RM-Softmax | 0.6703 ± 0.0402 |
-| Multimodal | Robust | 0.6228 ± 0.0064 | EmbedLLM-RouteJudge | 0.5958 ± 0.0125 |
-| Chinese | Standard | 0.7653 | GraphRouter | 0.7688 |
-| Chinese | Robust | 0.6051 | GraphRouter | 0.6167 |
+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.
 
-<!-- END GENERATED SUMMARY -->
+A diagnostic oracle selects the best observed model independently for each request:
 
-Mio selector leads both Multimodal splits and General Text Robust by nAUC; Eagle leads General Text Standard and GraphRouter leads both Chinese splits. For Mio selector, General Text Robust Cost@90/95/100 is 0.2492 USD and Chinese Robust does not reach Cost@100. The aggregate Multimodal Mio curve does not reach Cost@100 on either split; each records a 1/3 individual-run diagnostic rather than an aggregate target cost.
+$$
+Q_{\mathrm{oracle}}=\frac{1}{|\mathcal X|}\sum_{x\in\mathcal X}\max_{f_i\in\mathcal C}y_i(x),
+$$
 
-![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)
+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.
 
-*Green: Mio selector frontier and peak. Hollow circles: other baseline peaks. Blue diamond: best baseline by nAUC. Multimodal curves are pointwise means of three runs; both the displayed peaks and nAUC are computed from those mean curves. The log x-axis shows realized cost (USD for General Text/Multimodal; CNY for Chinese); the plotted area is not nominal-budget nAUC. This supplementary visualization uses the same frozen results as the report tables.*
+**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.
 
-<details>
-<summary>Full results: all 13 implementations and robustness-type breakdown</summary>
+**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.
 
-Costs are per request in the stated currency. Rankings use nAUC. Multimodal values are aggregate-curve statistics with exact run-bootstrap standard errors; threshold-cost reach counts are individual-run diagnostics. Values below reproduce the report's frozen results.
+**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.
+
+Robust split metrics are computed directly from the pooled robust request population. Each robustness type is evaluated separately with cost endpoints from that type's requests. Consequently, both the nominal grid and the cumulative-maximum envelope can differ, and averaging type-level nAUC values does not generally recover pooled Robust nAUC. The Multimodal robustness types each contain 475 final-test requests; equal group sizes do not change this aggregation order.
+
+The wider reporting protocol calls for standard, each robust type, robust pooled, each task domain, and standard-plus-robust groups. A combined Multimodal statistic must be labeled an **unpaired aggregate**. A weakest-group statistic takes the minimum over groups declared before final testing and names the attaining group; an empty group is unavailable, not zero. The tables below contain the released primary and Mio variation-level results, not every diagnostic proposed by that protocol.
+
+## Complete reported results
+
+The corrected result snapshot is dated **2026-10-15**. Mio selector has macro
+nAUC **0.6360**, followed by MLP at **0.6183**. Mio leads pooled General Text
+Robust and both Multimodal splits, but not General Text Standard or either
+Chinese split. It also does not lead every perturbation type: language-variation
+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.
+
+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
+predictions were reevaluated. Costs are per request in the named currency.
+**N/R** means the aggregate curve does not reach a threshold; `(r/3)` records
+individual-run reach counts. Primary-table rank is by nAUC.
 
 <!-- BEGIN GENERATED RESULTS -->
 
@@ -155,19 +324,19 @@ Costs are per request in the stated currency. Rankings use nAUC. Multimodal valu
 
 | Rank | Implementation | Macro nAUC |
 | ---: | --- | ---: |
-| 1 | Mio selector | 0.6375 |
-| 2 | MLP | 0.6151 |
-| 3 | EmbedLLM-RouteJudge | 0.6053 |
-| 4 | Eagle | 0.5552 |
-| 5 | Avengers-Pro | 0.5503 |
-| 6 | Avengers | 0.5442 |
-| 7 | RouteLLM-MF | 0.5333 |
-| 8 | HybridLLM | 0.5245 |
-| 9 | kNN | 0.5190 |
-| 10 | Causal RM-Softmax | 0.5088 |
-| 11 | SVM Router | 0.5031 |
-| 12 | GraphRouter | 0.4629 |
-| 13 | MIRT | 0.4315 |
+| 1 | Mio selector | 0.6360 |
+| 2 | MLP | 0.6183 |
+| 3 | EmbedLLM-RouteJudge | 0.6044 |
+| 4 | Avengers-Pro | 0.5753 |
+| 5 | Eagle | 0.5731 |
+| 6 | Avengers | 0.5603 |
+| 7 | RouteLLM-MF | 0.5573 |
+| 8 | kNN | 0.5443 |
+| 9 | HybridLLM | 0.5344 |
+| 10 | Causal RM-Softmax | 0.5245 |
+| 11 | SVM Router | 0.5146 |
+| 12 | MIRT | 0.4733 |
+| 13 | GraphRouter | 0.4669 |
 
 ### General Text: Standard
 
@@ -178,16 +347,16 @@ Costs in **USD per request**.
 | 1 | Eagle | 0.6104 | 0.6045 | 0.0253 | 0.0416 | 0.1131 |
 | 2 | Avengers-Pro | 0.6071 | 0.6022 | 0.0442 | 0.0625 | 0.1067 |
 | 3 | RouteLLM-MF | 0.6016 | 0.5939 | 0.0476 | 0.0874 | 0.2490 |
-| 4 | Mio selector | 0.5949 | 0.5908 | 0.0369 | 0.0553 | 0.2471 |
-| 5 | MLP | 0.5949 | 0.5891 | 0.0296 | 0.0391 | 0.2475 |
-| 6 | EmbedLLM-RouteJudge | 0.5957 | 0.5863 | 0.2476 | 0.2476 | 0.2476 |
+| 4 | Mio selector | 0.5979 | 0.5924 | 0.0321 | 0.0609 | 0.2285 |
+| 5 | MLP | 0.5949 | 0.5884 | 0.0238 | 0.0409 | 0.2475 |
+| 6 | EmbedLLM-RouteJudge | 0.5949 | 0.5863 | 0.1674 | 0.2475 | 0.2475 |
 | 7 | kNN | 0.5905 | 0.5853 | 0.0360 | 0.0691 | N/R |
 | 8 | SVM Router | 0.5854 | 0.5789 | 0.0725 | 0.1643 | N/R |
 | 9 | Avengers | 0.5817 | 0.5770 | 0.0653 | 0.1267 | N/R |
 | 10 | HybridLLM | 0.5778 | 0.5684 | 0.0722 | 0.2039 | N/R |
 | 11 | Causal RM-Softmax | 0.4969 | 0.4969 | N/R | N/R | N/R |
-| 12 | MIRT | 0.2687 | 0.2616 | N/R | N/R | N/R |
-| 13 | GraphRouter | 0.2393 | 0.2379 | N/R | N/R | N/R |
+| 12 | MIRT | 0.2853 | 0.2771 | N/R | N/R | N/R |
+| 13 | GraphRouter | 0.2501 | 0.2468 | N/R | N/R | N/R |
 
 ### General Text: Robust
 
@@ -195,19 +364,19 @@ Costs in **USD per request**.
 
 | nAUC rank | Implementation | Peak quality | nAUC | Cost@90 | Cost@95 | Cost@100 |
 | ---: | --- | ---: | ---: | ---: | ---: | ---: |
-| 1 | Mio selector | 0.5596 | 0.5299 | 0.2492 | 0.2492 | 0.2492 |
-| 2 | MLP | 0.5596 | 0.5052 | 0.2492 | 0.2492 | 0.2492 |
-| 3 | EmbedLLM-RouteJudge | 0.5399 | 0.4990 | 0.2488 | 0.2488 | N/R |
-| 4 | Eagle | 0.2592 | 0.2428 | N/R | N/R | N/R |
-| 5 | Avengers | 0.1653 | 0.1625 | N/R | N/R | N/R |
-| 6 | RouteLLM-MF | 0.1683 | 0.1593 | N/R | N/R | N/R |
-| 7 | Avengers-Pro | 0.1599 | 0.1562 | N/R | N/R | N/R |
-| 8 | kNN | 0.1373 | 0.1266 | N/R | N/R | N/R |
-| 9 | HybridLLM | 0.1255 | 0.1092 | N/R | N/R | N/R |
-| 10 | SVM Router | 0.1141 | 0.1030 | N/R | N/R | N/R |
-| 11 | GraphRouter | 0.0233 | 0.0233 | N/R | N/R | N/R |
-| 12 | MIRT | 0.0218 | 0.0210 | N/R | N/R | N/R |
-| 13 | Causal RM-Softmax | 0.0000 | 0.0000 | N/R | N/R | N/R |
+| 1 | Mio selector | 0.5596 | 0.5328 | 0.2258 | 0.2389 | 0.2492 |
+| 2 | EmbedLLM-RouteJudge | 0.5596 | 0.5304 | 0.2492 | 0.2492 | 0.2492 |
+| 3 | MLP | 0.5596 | 0.5231 | 0.2492 | 0.2492 | 0.2492 |
+| 4 | Eagle | 0.3483 | 0.3359 | N/R | N/R | N/R |
+| 5 | RouteLLM-MF | 0.2974 | 0.2891 | N/R | N/R | N/R |
+| 6 | Avengers-Pro | 0.2894 | 0.2865 | N/R | N/R | N/R |
+| 7 | Avengers | 0.2854 | 0.2832 | N/R | N/R | N/R |
+| 8 | kNN | 0.2773 | 0.2671 | N/R | N/R | N/R |
+| 9 | HybridLLM | 0.2764 | 0.2616 | N/R | N/R | N/R |
+| 10 | SVM Router | 0.2706 | 0.2611 | N/R | N/R | N/R |
+| 11 | Causal RM-Softmax | 0.1774 | 0.1774 | N/R | N/R | N/R |
+| 12 | MIRT | 0.1185 | 0.1133 | N/R | N/R | N/R |
+| 13 | GraphRouter | 0.1154 | 0.1110 | N/R | N/R | N/R |
 
 ### Multimodal: Standard
 
@@ -215,19 +384,19 @@ Costs in **USD per request**.
 
 | nAUC rank | Implementation | Peak quality | nAUC | Cost@90 | Cost@95 | Cost@100 |
 | ---: | --- | ---: | ---: | ---: | ---: | ---: |
-| 1 | Mio selector | 0.7116 ± 0.0080 | 0.7109 ± 0.0083 | 3.00e-6 (3/3) | 3.00e-6 (3/3) | N/R (1/3 runs) |
-| 2 | Causal RM-Softmax | 0.6703 ± 0.0402 | 0.6703 ± 0.0402 | 2.94e-6 (2/3) | N/R (2/3 runs) | N/R (2/3 runs) |
-| 3 | MLP | 0.6609 ± 0.0024 | 0.6293 ± 0.0042 | 0.0041 (3/3) | N/R | N/R |
-| 4 | Avengers | 0.6335 ± 0.0136 | 0.6230 ± 0.0174 | N/R (2/3 runs) | N/R (1/3 runs) | N/R |
-| 5 | Avengers-Pro | 0.6291 ± 0.0067 | 0.6059 ± 0.0014 | N/R | N/R | N/R |
-| 6 | GraphRouter | 0.6021 ± 0.0234 | 0.6020 ± 0.0242 | N/R (1/3 runs) | N/R | N/R |
-| 7 | RouteLLM-MF | 0.6298 ± 0.0000 | 0.5844 ± 0.0000 | N/R | N/R | N/R |
-| 8 | HybridLLM | 0.5843 ± 0.0000 | 0.5842 ± 0.0000 | N/R | N/R | N/R |
-| 9 | EmbedLLM-RouteJudge | 0.6772 ± 0.0107 | 0.5782 ± 0.0508 | 0.0032 (3/3) | N/R (2/3 runs) | N/R (1/3 runs) |
-| 10 | Eagle | 0.5881 ± 0.0000 | 0.5778 ± 0.0000 | N/R | N/R | N/R |
-| 11 | MIRT | 0.5770 ± 0.0024 | 0.5765 ± 0.0021 | N/R | N/R | N/R |
-| 12 | kNN | 0.5502 ± 0.0000 | 0.5502 ± 0.0000 | N/R | N/R | N/R |
-| 13 | SVM Router | 0.5049 ± 0.0048 | 0.5049 ± 0.0048 | N/R | N/R | N/R |
+| 1 | Mio selector | 0.7084 ± 0.0008 | 0.7077 ± 0.0007 | 2.91e-6 (3/3) | 2.91e-6 (3/3) | N/R |
+| 2 | MIRT | 0.6359 ± 0.0155 | 0.6359 ± 0.0155 | N/R (1/3 runs) | N/R | N/R |
+| 3 | MLP | 0.6663 ± 0.0027 | 0.6299 ± 0.0032 | 0.0035 (3/3) | N/R | N/R |
+| 4 | Avengers-Pro | 0.6464 ± 0.0010 | 0.6239 ± 0.0019 | N/R (2/3 runs) | N/R | N/R |
+| 5 | Causal RM-Softmax | 0.6217 ± 0.0393 | 0.6217 ± 0.0393 | N/R (1/3 runs) | N/R (1/3 runs) | N/R |
+| 6 | Eagle | 0.6507 ± 0.0000 | 0.6095 ± 0.0000 | 0.0039 (3/3) | N/R | N/R |
+| 7 | Avengers | 0.6358 ± 0.0075 | 0.6064 ± 0.0096 | N/R | N/R | N/R |
+| 8 | RouteLLM-MF | 0.6236 ± 0.0000 | 0.5914 ± 0.0000 | N/R | N/R | N/R |
+| 9 | kNN | 0.6000 ± 0.0000 | 0.5846 ± 0.0000 | N/R | N/R | N/R |
+| 10 | GraphRouter | 0.5558 ± 0.0006 | 0.5558 ± 0.0005 | N/R | N/R | N/R |
+| 11 | EmbedLLM-RouteJudge | 0.6193 ± 0.0291 | 0.5510 ± 0.0524 | N/R (3/3 runs) | N/R (2/3 runs) | N/R |
+| 12 | HybridLLM | 0.5555 ± 0.0000 | 0.5385 ± 0.0000 | N/R | N/R | N/R |
+| 13 | SVM Router | 0.4869 ± 0.0222 | 0.4869 ± 0.0222 | N/R | N/R | N/R |
 
 ### Multimodal: Robust
 
@@ -235,19 +404,19 @@ Costs in **USD per request**.
 
 | nAUC rank | Implementation | Peak quality | nAUC | Cost@90 | Cost@95 | Cost@100 |
 | ---: | --- | ---: | ---: | ---: | ---: | ---: |
-| 1 | Mio selector | 0.6261 ± 0.0083 | 0.6228 ± 0.0064 | 3.44e-6 (3/3) | 3.44e-6 (3/3) | N/R (1/3 runs) |
-| 2 | EmbedLLM-RouteJudge | 0.6013 ± 0.0102 | 0.5958 ± 0.0125 | 0.0002 (3/3) | N/R (3/3 runs) | N/R |
-| 3 | MLP | 0.5965 ± 0.0079 | 0.5929 ± 0.0089 | 0.0002 (3/3) | N/R (2/3 runs) | N/R |
-| 4 | Causal RM-Softmax | 0.5824 ± 0.0291 | 0.5824 ± 0.0291 | 3.41e-6 (2/3) | N/R (2/3 runs) | N/R |
-| 5 | Avengers-Pro | 0.5802 ± 0.0041 | 0.5659 ± 0.0051 | 0.0031 (3/3) | N/R | N/R |
-| 6 | Avengers | 0.5891 ± 0.0082 | 0.5629 ± 0.0096 | 0.0027 (2/3) | N/R | N/R |
-| 7 | HybridLLM | 0.5579 ± 0.0000 | 0.5579 ± 0.0000 | N/R | N/R | N/R |
-| 8 | Eagle | 0.5663 ± 0.0000 | 0.5434 ± 0.0000 | N/R | N/R | N/R |
-| 9 | SVM Router | 0.5430 ± 0.0018 | 0.5424 ± 0.0024 | N/R | N/R | N/R |
-| 10 | RouteLLM-MF | 0.5506 ± 0.0000 | 0.5366 ± 0.0000 | N/R | N/R | N/R |
-| 11 | GraphRouter | 0.5290 ± 0.0256 | 0.5289 ± 0.0260 | N/R (1/3 runs) | N/R | N/R |
-| 12 | kNN | 0.5354 ± 0.0000 | 0.5229 ± 0.0000 | N/R | N/R | N/R |
-| 13 | MIRT | 0.4881 ± 0.0015 | 0.4880 ± 0.0012 | N/R | N/R | N/R |
+| 1 | Mio selector | 0.6266 ± 0.0008 | 0.6128 ± 0.0005 | 3.41e-6 (3/3) | 3.41e-6 (3/3) | N/R |
+| 2 | MLP | 0.5932 ± 0.0006 | 0.5918 ± 0.0007 | 0.0001 (3/3) | N/R | N/R |
+| 3 | EmbedLLM-RouteJudge | 0.5869 ± 0.0145 | 0.5869 ± 0.0138 | 0.0002 (3/3) | N/R (3/3 runs) | N/R |
+| 4 | Avengers-Pro | 0.5979 ± 0.0025 | 0.5680 ± 0.0028 | 0.0031 (3/3) | N/R | N/R |
+| 5 | MIRT | 0.5641 ± 0.0128 | 0.5638 ± 0.0128 | N/R (1/3 runs) | N/R | N/R |
+| 6 | Avengers | 0.5913 ± 0.0019 | 0.5553 ± 0.0204 | 0.0025 (3/3) | N/R | N/R |
+| 7 | Causal RM-Softmax | 0.5477 ± 0.0286 | 0.5477 ± 0.0287 | N/R (1/3 runs) | N/R (1/3 runs) | N/R |
+| 8 | RouteLLM-MF | 0.5572 ± 0.0000 | 0.5439 ± 0.0000 | N/R | N/R | N/R |
+| 9 | Eagle | 0.5609 ± 0.0000 | 0.5264 ± 0.0000 | N/R | N/R | N/R |
+| 10 | HybridLLM | 0.5234 ± 0.0000 | 0.5106 ± 0.0000 | N/R | N/R | N/R |
+| 11 | GraphRouter | 0.5038 ± 0.0003 | 0.5038 ± 0.0002 | N/R | N/R | N/R |
+| 12 | kNN | 0.4995 ± 0.0000 | 0.4995 ± 0.0000 | N/R | N/R | N/R |
+| 13 | SVM Router | 0.4714 ± 0.0204 | 0.4714 ± 0.0204 | N/R | N/R | N/R |
 
 ### Chinese: Standard
 
@@ -255,17 +424,17 @@ Costs in **CNY per request**.
 
 | nAUC rank | Implementation | Peak quality | nAUC | Cost@90 | Cost@95 | Cost@100 |
 | ---: | --- | ---: | ---: | ---: | ---: | ---: |
-| 1 | GraphRouter | 0.7688 | 0.7688 | 0.0666 | 0.0666 | 0.0666 |
+| 1 | GraphRouter | 0.7675 | 0.7675 | 0.0666 | 0.0666 | N/R |
 | 2 | Avengers-Pro | 0.7688 | 0.7660 | 0.0123 | 0.0123 | 0.0606 |
-| 3 | Mio selector | 0.7711 | 0.7653 | 0.0127 | 0.0127 | 0.0348 |
-| 4 | MLP | 0.7678 | 0.7642 | 0.0132 | 0.0132 | N/R |
+| 3 | MLP | 0.7687 | 0.7656 | 0.0132 | 0.0132 | N/R |
+| 4 | Mio selector | 0.7711 | 0.7653 | 0.0127 | 0.0127 | 0.0348 |
 | 5 | Eagle | 0.7719 | 0.7642 | 0.0122 | 0.0122 | 0.0516 |
-| 6 | EmbedLLM-RouteJudge | 0.7679 | 0.7632 | 0.0117 | 0.0147 | N/R |
+| 6 | EmbedLLM-RouteJudge | 0.7675 | 0.7629 | 0.0117 | 0.0148 | N/R |
 | 7 | kNN | 0.7663 | 0.7598 | 0.0122 | 0.0122 | N/R |
 | 8 | Avengers | 0.7604 | 0.7597 | 0.0132 | 0.0132 | N/R |
 | 9 | HybridLLM | 0.7623 | 0.7555 | 0.0137 | 0.0137 | N/R |
 | 10 | RouteLLM-MF | 0.7585 | 0.7532 | 0.0140 | 0.0140 | N/R |
-| 11 | MIRT | 0.7688 | 0.7418 | 0.0201 | 0.0296 | 0.0666 |
+| 11 | MIRT | 0.7688 | 0.7437 | 0.0206 | 0.0266 | 0.0666 |
 | 12 | Causal RM-Softmax | 0.7406 | 0.7404 | 0.0143 | 0.0143 | N/R |
 | 13 | SVM Router | 0.7434 | 0.7394 | 0.0137 | 0.0171 | N/R |
 
@@ -275,9 +444,9 @@ Costs in **CNY per request**.
 
 | nAUC rank | Implementation | Peak quality | nAUC | Cost@90 | Cost@95 | Cost@100 |
 | ---: | --- | ---: | ---: | ---: | ---: | ---: |
-| 1 | GraphRouter | 0.6167 | 0.6167 | 0.0792 | 0.0792 | 0.0792 |
-| 2 | MLP | 0.6176 | 0.6098 | 0.0062 | 0.0100 | 0.0639 |
-| 3 | EmbedLLM-RouteJudge | 0.6167 | 0.6094 | 0.0182 | 0.0182 | 0.0778 |
+| 1 | GraphRouter | 0.6163 | 0.6163 | 0.0791 | 0.0791 | 0.0791 |
+| 2 | MLP | 0.6178 | 0.6108 | 0.0062 | 0.0098 | 0.0642 |
+| 3 | EmbedLLM-RouteJudge | 0.6167 | 0.6087 | 0.0182 | 0.0182 | 0.0775 |
 | 4 | Avengers-Pro | 0.6161 | 0.6054 | 0.0173 | 0.0186 | N/R |
 | 5 | Mio selector | 0.6137 | 0.6051 | 0.0061 | 0.0089 | N/R |
 | 6 | Eagle | 0.6142 | 0.5982 | 0.0122 | 0.0165 | N/R |
@@ -287,7 +456,7 @@ Costs in **CNY per request**.
 | 10 | kNN | 0.5919 | 0.5694 | 0.0224 | 0.0521 | N/R |
 | 11 | Causal RM-Softmax | 0.5632 | 0.5628 | 0.0078 | N/R | N/R |
 | 12 | SVM Router | 0.5607 | 0.5499 | 0.0479 | N/R | N/R |
-| 13 | MIRT | 0.6162 | 0.4999 | 0.0725 | 0.0725 | 0.0792 |
+| 13 | MIRT | 0.6162 | 0.5060 | 0.0728 | 0.0728 | 0.0792 |
 
 ### Mio selector by robustness type
 
@@ -295,137 +464,112 @@ Ranks are among 13 distinct implementations; Multimodal metrics use the pointwis
 
 | Scenario | Robustness type | Peak quality | Peak rank | nAUC | nAUC rank |
 | --- | --- | ---: | ---: | ---: | ---: |
-| General Text | Semantic rewrite | 0.5124 | 1/13 | 0.4636 | 1/13 |
-| General Text | Language variation | 0.4913 | 1/13 | 0.4360 | 1/13 |
-| General Text | Long-context noise | 0.6747 | 1/13 | 0.6556 | 1/13 |
-| Multimodal | Text rewrite | 0.6559 ± 0.0116 | 1/13 | 0.6550 ± 0.0115 | 1/13 |
-| Multimodal | Context noise | 0.6534 ± 0.0037 | 1/13 | 0.6380 ± 0.0030 | 1/13 |
-| Multimodal | Image variation | 0.6375 ± 0.0068 | 1/13 | 0.6367 ± 0.0068 | 1/13 |
-| Multimodal | Combined perturbation | 0.6265 ± 0.0029 | 1/13 | 0.6059 ± 0.0070 | 1/13 |
-| Chinese | Semantic rewrite | 0.6156 | 4/13 | 0.6086 | 4/13 |
+| General Text | Semantic rewrite | 0.5124 | 1/13 | 0.4829 | 1/13 |
+| General Text | Language variation | 0.4913 | 1/13 | 0.4487 | 2/13 |
+| General Text | Long-context noise | 0.6747 | 1/13 | 0.6529 | 1/13 |
+| Multimodal | Text rewrite | 0.6455 ± 0.0010 | 1/13 | 0.6438 ± 0.0003 | 1/13 |
+| Multimodal | Context noise | 0.6219 ± 0.0028 | 1/13 | 0.6021 ± 0.0018 | 1/13 |
+| Multimodal | Image variation | 0.6289 ± 0.0009 | 1/13 | 0.6247 ± 0.0010 | 1/13 |
+| Multimodal | Combined perturbation | 0.6099 ± 0.0006 | 2/13 | 0.5827 ± 0.0013 | 3/13 |
+| Chinese | Semantic rewrite | 0.6156 | 5/13 | 0.6086 | 4/13 |
 | Chinese | English translation | 0.5893 | 7/13 | 0.5802 | 5/13 |
 | Chinese | Prefix noise | 0.6375 | 7/13 | 0.6287 | 5/13 |
 
 <!-- END GENERATED RESULTS -->
 
-</details>
-
-## Benchmark details
-
-<details>
-<summary>Source datasets and robustness transformations</summary>
-
-### General Text
-
-| Domain | Train | Published standard | Published robust |
-| --- | ---: | ---: | ---: |
-| Code | 1,386 | 859 | 268 |
-| General Reasoning | 435 | 649 | 474 |
-| Knowledge QA | 388 | 616 | 506 |
-| Long Context | 0 | 1,000 | 1,000 |
-| Mathematical Reasoning | 438 | 574 | 410 |
-| Reading Understanding | 353 | 490 | 342 |
-
-The 3,000 robust instances derive from 2,907 base queries retained in standard test. All 1,000 Long Context requests are robustness bases; none are in training. Long-context noise adds one to three removable background notes.
-
-### Multimodal
-
-MMR-4000 training examples come from 18 [MMR-Bench-V2](https://doi.org/10.1007/978-3-032-37083-9_34) sub-benchmarks. VisionQA-5000 standard evaluation contains:
-
-| Capability group | Source benchmark | Published standard instances |
-| --- | --- | ---: |
-| General VQA | [VQAv2](https://openaccess.thecvf.com/content_cvpr_2017/html/Goyal_Making_the_v_CVPR_2017_paper.html) | 1,500 |
-| Science / Diagram | [AI2D](https://doi.org/10.1007/978-3-319-46493-0_15) | 676 |
-| Science / Diagram | [ScienceQA](https://proceedings.neurips.cc/paper_files/paper/2022/hash/11332b6b6cf4485b84afadb1352d3a9a-Abstract-Conference.html) | 667 |
-| Science / Diagram | [ScienceQA-IMG](https://proceedings.neurips.cc/paper_files/paper/2022/hash/11332b6b6cf4485b84afadb1352d3a9a-Abstract-Conference.html) | 657 |
-| Document / OCR | [DocVQA](https://openaccess.thecvf.com/content/WACV2021/html/Mathew_DocVQA_A_Dataset_for_VQA_on_Document_Images_WACV_2021_paper.html) | 183 |
-| Document / OCR | [OCR-VQA](https://doi.org/10.1109/ICDAR.2019.00156) | 1,317 |
-| Total | | 5,000 |
-
-Requests may contain multiple images, text segments, and answer options. The 2,000 robust items are sampled independently of VisionQA-5000; both sets use the same 45 candidates.
-
-### Chinese
-
-| Coverage | Source benchmark |
-| --- | --- |
-| Alignment | [AlignBench](https://arxiv.org/abs/2311.18743) |
-| Knowledge and examinations | [C-Eval](https://arxiv.org/abs/2305.08322) |
-| Instruction following | [CIF-Bench](https://arxiv.org/abs/2402.13109) |
-| Language, NLI, and reading | [CLUE benchmark family](https://aclanthology.org/2020.coling-main.419/) |
-| Clinical medicine | [CMB-Clin from CMB](https://arxiv.org/abs/2308.08833) |
-| Factual knowledge | [Chinese SimpleQA](https://aclanthology.org/2025.acl-long.941/) |
-| Financial knowledge | [FinEval](https://arxiv.org/abs/2308.09975) |
-| Constraint following | [FollowBench-zh](https://aclanthology.org/2024.acl-long.257/) |
-| Standardized examinations | [GAOKAO-Bench](https://arxiv.org/abs/2305.12474) |
-| Legal knowledge | [LawBench](https://arxiv.org/abs/2309.16289) |
-| Long-context tasks | [Chinese subsets of LongBench](https://arxiv.org/abs/2308.14508) |
-
-Each source retains its task and evaluator semantics; quality is not a single shared accuracy measure across tasks.
-
-### Robustness groups
-
-| Scenario | Robustness type | Published instances | Final-test instances | Relation to standard population |
-| --- | --- | ---: | ---: | --- |
-| General Text | Semantic rewrite | 1,000 | 963 | Paired base retained |
-| General Text | Language variation | 1,000 | 964 | Paired base retained |
-| General Text | Long-context noise | 1,000 | 967 | Paired base retained |
-| Multimodal | Text rewrite | 500 | 475 | Independently sampled |
-| Multimodal | Context noise | 500 | 475 | Independently sampled |
-| Multimodal | Image variation | 500 | 475 | Independently sampled |
-| Multimodal | Combined perturbation | 500 | 475 | Independently sampled |
-| Chinese | Semantic rewrite | 700 | 667 | Paired, disjoint bases |
-| Chinese | English translation | 700 | 666 | Paired, disjoint bases |
-| Chinese | Prefix noise | 700 | 667 | Paired, disjoint bases |
-
-Transformations must preserve meaning, required evidence, the answer contract, and scorer validity. Image variation uses 100 instances each of JPEG degradation, controlled cutout, Gaussian noise, Gaussian blur, and contrast reduction. Combined multimodal perturbations change text, context, and image together. Chinese robustness types use disjoint base requests retained in standard test.
-
-</details>
-
-<details>
-<summary>Compared methods, implementation aliases, and run settings</summary>
-
-The report compares **Mio selector and 12 baselines** through [ORBIT](https://journal.hep.com.cn/fcs/EN/10.1007/s11704-026-61310-5)'s common interfaces.
-
-| Reported implementation | Method source |
-| --- | --- |
-| Avengers | [The Avengers](https://arxiv.org/abs/2505.19797) |
-| Avengers-Pro | [Performance-Efficiency Optimized Routing](https://doi.org/10.1145/3772429.3772445) |
-| Causal RM-Softmax | [Causal LLM Routing, RM-Softmax](https://arxiv.org/abs/2505.16037) |
-| Eagle | [Eagle](https://arxiv.org/abs/2409.15518) |
-| EmbedLLM-RouteJudge | [EmbedLLM](https://proceedings.iclr.cc/paper_files/paper/2025/hash/bf5e4b85d203481d6e37bd32d9600162-Abstract-Conference.html) |
-| Mio selector | Request-oriented selection, technical report Section 4.2; scenario-specific offline training |
-| GraphRouter | [GraphRouter](https://arxiv.org/abs/2410.03834) |
-| HybridLLM | [Hybrid LLM](https://proceedings.iclr.cc/paper_files/paper/2024/hash/b47d93c99fa22ac0b377578af0a1f63a-Abstract-Conference.html) |
-| kNN | [TensorOpera](https://arxiv.org/abs/2408.12320) / [ORBIT](https://journal.hep.com.cn/fcs/EN/10.1007/s11704-026-61310-5) baseline |
-| MLP | [TensorOpera](https://arxiv.org/abs/2408.12320) / [ORBIT](https://journal.hep.com.cn/fcs/EN/10.1007/s11704-026-61310-5) baseline |
-| MIRT | [IRT-Router, MIRT variant](https://aclanthology.org/2025.acl-long.761/) |
-| RouteLLM-MF | [RouteLLM, matrix-factorization variant](https://arxiv.org/abs/2406.18665) |
-| SVM Router | [TensorOpera](https://arxiv.org/abs/2408.12320) / [ORBIT](https://journal.hep.com.cn/fcs/EN/10.1007/s11704-026-61310-5) baseline |
-
-Mio selector is trained separately on each scenario's training split, with hyperparameters and operating points selected on development data. Model-side evidence comes from benchmark training observations; the Section 3 Model Eval profile-construction pipeline is not jointly evaluated here. These are selector-level results, not a hosted Mio 1.0 Lite API or end-to-end platform evaluation. They do not isolate profile encoding, pairwise supervision, or near-tie selection, and do not evaluate candidate-pool organization, multi-step handoff, or Section 4.2's rank-fusion rule.
-
-The following labels produced identical fitted-adapter predictions and selection arrays in this experiment and are counted once:
-
-| Ranked implementation | Additional implementation aliases in this experiment |
-| --- | --- |
-| Causal RM-Softmax | RM-Classification, RM-Interval ([source](https://arxiv.org/abs/2505.16037)) |
-| Eagle | RouteLLM-SW Ranking ([RouteLLM](https://arxiv.org/abs/2406.18665)) |
-| EmbedLLM-RouteJudge | Model-SAT ([Capability Instruction Tuning](https://ojs.aaai.org/index.php/AAAI/article/view/34790)), [RouterDC](https://proceedings.neurips.cc/paper_files/paper/2024/hash/7a641b8ec86162fc875fb9f6456a542f-Abstract-Conference.html) |
-| MLP | [OmniRouter](https://arxiv.org/abs/2502.20576), RouteLLM-BERT ([RouteLLM](https://arxiv.org/abs/2406.18665)) |
-| MIRT | NIRT ([IRT-Router](https://aclanthology.org/2025.acl-long.761/)) |
-
-These are implementation aliases within this experiment, not a claim that the original research methods are equivalent or independently reproduced.
-
-Multimodal uses seeds `20260917`, `20260918`, and `20260919`; General Text and Chinese use `20260917`. The Multimodal runs share 4,000 training requests, 45 candidates, Qwen3-VL-Embedding-8B text-and-image representations, identical final evaluation requests and source-group exclusions, and the same 100-point budget grid for each reporting population. Only the fitting seed changes. The reported uncertainty uses exact run-bootstrap standard errors, not paired request-bootstrap intervals; no significance tests are reported.
-
-</details>
-
-## Release scope and license
-
-The release includes scored per-model outcomes, baseline embeddings, and multimodal media. It does not include raw model responses, source diagnostic payloads, Mio's internal training implementation, or per-run prediction arrays. Dynamic model-pool changes and live-service recovery are outside the reported evaluation.
-
-The numerical source is the technical report's [frozen RobustRouteBench results](https://github.com/SinapisAI/sinapisai-tech-report/blob/main/scripts/results/routestressbench-results.json), archive format version 3, generated 2026-10-12. It records the pointwise-curve aggregation protocol, exclusion counts, seeds, budget points, metrics, and the alias audit.
-
-The original data revision is [`fcf2e3607c1c3ad303c202e2175a837ae4fbfede`](https://huggingface.co/datasets/SinapisAI/RobustRouteBench/tree/fcf2e3607c1c3ad303c202e2175a837ae4fbfede). Pin it through `revision=` when reproducing the data. This documentation update preserves the published data, split layout, schema, model metadata, and original manifest. The manifest's README checksum refers to the original card at that data revision.
-
-See [manifest.json](manifest.json) for the data inventory and [LICENSES.md](LICENSES.md) for upstream attribution and terms. This compilation uses `license: other`; upstream questions and images retain their respective terms.
+## Attribution and uncertainty protocol
+
+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:
+
+$$
+\bar y_\pi=\sum_{x\in\mathcal X}w(x)y_\pi(x),\qquad
+\Delta_y=\sum_{x\in\mathcal X}w(x)\bigl[y_{\pi_1}(x)-y_{\pi_2}(x)\bigr].
+$$
+
+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:
+
+$$
+\begin{aligned}
+\Delta_{\mathrm{model}}(x)&=y_{\pi(x)}(\mathsf T(x))-y_{\pi(x)}(x),\\
+\Delta_{\mathrm{selection}}(x)&=y_{\pi(\mathsf T(x))}(\mathsf T(x))-y_{\pi(x)}(\mathsf T(x)).
+\end{aligned}
+$$
+
+The first term measures changed performance of the originally selected model; the second measures the additional effect of changing the choice. Their sum is the total paired change. A changed selection is therefore not itself a failure. The identity requires matched outcomes and does not apply to the independently sampled Multimodal sets. Attributing candidate-organization effects requires the staged ecosystem controls below.
+
+## Model-ecosystem and service extensions
+
+These are controlled experiment specifications, **not completed numerical evaluations**. Each starts from a frozen snapshot and replays an ordered event stream, recording both effective time and information-visibility time. Conditions receive the same events and may use only evidence visible before the next decision. Preparation/update costs remain separate from subsequent execution costs.
+
+| Condition | Permitted update | Intended attribution |
+| --- | --- | --- |
+| Frozen | None | Exposure of the original system to the event |
+| Capability refresh | New measurements or reconstructed capability profiles | Benefit of updated capability evidence |
+| Candidate repair | Capability refresh plus candidate-pool reconstruction | Additional effect of changing the retained action set |
+| Selector adaptation | Prior updates plus fitting or calibration | Additional effect of adapting request-level choices |
+| Execution recovery | Endpoint rescheduling; explicit selector reselection stated separately | Restored service versus a changed model choice |
+
+The ecosystem stream covers model arrival, withdrawal, version replacement, profile refresh, and evaluation-scope expansion. It tests capability characterization, candidate organization, and selector adaptation cumulatively.
+
+The service stream controls load, prices, rate limits, endpoint interruption/withdrawal, SLOs, and budgets. Compare frozen endpoint assignment, same-model rescheduling with bounded recovery, and a condition explicitly permitting a new Mio selector decision. Over a common post-event horizon, report task utility, expenditure, SLO attainment, throughput, tail latency, retries, endpoint switches, reselections, and recovery time. Unrecovered episodes retain failure or censoring status. Stored outcome matrices can replay selection alternatives; they cannot establish live queueing, failover delay, cancellation, or readiness.
+
+## Reproducibility and release boundaries
+
+Use the corrected **v1.1.0-20261015** release, rather than earlier score snapshots.
+Resolve and pin its Git revision through `revision=` when loading data for a fixed
+experiment. `manifest.json` records current file checksums; `release-audit.json`
+records structural/range/group/feature validation and the limits of score verification.
+Split inventories and media are unchanged; Text scores and Multimodal features are corrected.
+
+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 [toolkit](https://github.com/SinapisAI/model-intelligence-toolkit).
+
+The toolkit's `python -m examples.reproduce_report --mode replay --output runs/replay`
+recomputes report metrics from public data and prediction arrays. `--mode fit`
+re-trains the 12 public baselines; Mio is explicitly replay-only. Library/hardware
+differences can affect fitted neural outputs. Mio's internal training implementation,
+raw responses, private source diagnostics, and completed live-service traces are
+not supplied. Offline score replay does not reproduce live-service experiments.
+
+For a fully auditable new run, retain split/pair identifiers, source groups, the development-group exclusion list, model/endpoint versions and candidate membership, transformations, seeds, generation/scoring settings, price tables, response status and available latency fields, method configurations, the ORBIT/toolkit commit, source-method deviations, task-level outputs, and prediction-array hashes for alias detection. Dynamic studies additionally need event ordering, visibility times, update costs, and execution traces. This is the required experimental record, not a claim that every listed item is present in the published dataset manifest.
+
+Upstream question and media attribution and terms remain in [`LICENSES.md`](https://huggingface.co/datasets/SinapisAI/RobustRouteBench/blob/main/LICENSES.md).
+
+[bai2023longbench]: https://arxiv.org/abs/2308.14508
+[chen2024routerdc]: https://proceedings.neurips.cc/paper_files/paper/2024/hash/7a641b8ec86162fc875fb9f6456a542f-Abstract-Conference.html
+[ding2024hybridllm]: https://proceedings.iclr.cc/paper_files/paper/2024/hash/b47d93c99fa22ac0b377578af0a1f63a-Abstract-Conference.html
+[fei2023lawbench]: https://arxiv.org/abs/2309.16289
+[feng2024graphrouter]: https://arxiv.org/abs/2410.03834
+[goyal2017vqav2]: https://openaccess.thecvf.com/content_cvpr_2017/html/Goyal_Making_the_v_CVPR_2017_paper.html
+[guo2023fineval]: https://arxiv.org/abs/2308.09975
+[he2025chinesesimpleqa]: https://aclanthology.org/2025.acl-long.941/
+[huang2023ceval]: https://arxiv.org/abs/2305.08322
+[jiang2024followbench]: https://aclanthology.org/2024.acl-long.257/
+[kembhavi2016ai2d]: https://doi.org/10.1007/978-3-319-46493-0_15
+[lai2026orbit]: https://journal.hep.com.cn/fcs/EN/10.1007/s11704-026-61310-5
+[li2024cifbench]: https://arxiv.org/abs/2402.13109
+[liu2023alignbench]: https://arxiv.org/abs/2311.18743
+[lu2022scienceqa]: https://proceedings.neurips.cc/paper_files/paper/2022/hash/11332b6b6cf4485b84afadb1352d3a9a-Abstract-Conference.html
+[ma2026mmrbench]: https://doi.org/10.1007/978-3-032-37083-9_34
+[mathew2021docvqa]: https://openaccess.thecvf.com/content/WACV2021/html/Mathew_DocVQA_A_Dataset_for_VQA_on_Document_Images_WACV_2021_paper.html
+[mei2025omnirouter]: https://arxiv.org/abs/2502.20576
+[mishra2019ocrvqa]: https://doi.org/10.1109/ICDAR.2019.00156
+[ong2024routellm]: https://arxiv.org/abs/2406.18665
+[song2025irtrouter]: https://aclanthology.org/2025.acl-long.761/
+[stripelis2024tensoropera]: https://arxiv.org/abs/2408.12320
+[tsiourvas2025causalrouting]: https://arxiv.org/abs/2505.16037
+[wang2023cmb]: https://arxiv.org/abs/2308.08833
+[xu2020clue]: https://aclanthology.org/2020.coling-main.419/
+[zhang2023gaokao]: https://arxiv.org/abs/2305.12474
+[zhang2025avengers]: https://arxiv.org/abs/2505.19797
+[zhang2025avengerspro]: https://doi.org/10.1145/3772429.3772445
+[zhang2025modelsat]: https://ojs.aaai.org/index.php/AAAI/article/view/34790
+[zhao2024eagle]: https://arxiv.org/abs/2409.15518
+[zhuang2024embedllm]: https://proceedings.iclr.cc/paper_files/paper/2025/hash/bf5e4b85d203481d6e37bd32d9600162-Abstract-Conference.html

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