cs.CLOct 14, 2026
SaveTyped Decision Models on Tabular Data
Organizations: University of Wisconsin–Madison · School of Artificial Intelligence, Jilin University · School of Data Science, Chinese University of Hong Kong, Shenzhen
Abstract
Typed decision models are language models that answer a question with a fixed set of possible answers by returning a probability for each answer instead of generating text. We study them on tabular data and compare four decision models with three large language models (LLMs) on row classification, fact verification and anomaly detection. Every request is sent in its original form and under 21 types of rewrite that keep its content, such as renaming the options or writing the table in another format. The decision models perform as well as the LLMs at a lower cost. Three of them are also far more robust to how the question is formulated. No model we test is robust to how the table is organized. For every model, at least a third of the row decisions change under some rewrite of the table or the question. The decisions to which a decision model gives low probability are the ones most likely to change. Decision models are a practical choice for tabular decisions, but their robustness remains an open problem.
Figures & tables
| Decision models | |||
|---|---|---|---|
| Research question | Jev, Clef, Kev-27B | GPT-6 Luna Decisions | LLMs |
| RQ1 Are decision models practical? (§ 3 ) | ✓ As accurate as LLMs and best on two of three tasks, at far lower cost | ✓ As accurate as GPT-6 Luna and cheaper on rows and samples | ✓ Best on row classification, but a decoding pass per decision |
| RQ2 Are they robust to the formulation of the question? (§ 4 ) | ✓ Option names and order barely matter | ✗ Reversing the options changes up to 23% | ✗ Option names and order change up to 30% |
| RQ3 Are they robust to how the table is organized? (§ 5 ) | ✗ Format and row and column order change many decisions | ✗ Likewise | ✗ Likewise |
| RQ4 Does their confidence indicate which decisions are sensitive to rewrites? (§ 6 ) | ✓ Low confidence marks the decisions a rewrite changes | Partly. As well as an LLM’s best readout | Partly. Only with ten draws or token probabilities |
Table 1: Our four research questions and their answers for the decision models and the LLMs.
| Task | Input | Decision | Data |
|---|---|---|---|
| Row classification | One sample and a description of its features | Its class | 10 datasets, 3,481 rows |
| Fact verification | A table and a claim about it | Supported or refuted | TabFact, 2,000 claims |
| Anomaly detection | One sample, a description of the data and statistics of normal samples | Normal or anomalous | ReTabAD, 20 sources, 5,059 samples |
Table 2: The three tasks: row classification on the ten datasets of TabLLM and FeatLLM ( Hegselmann et al., 2023 ; Han et al., 2024 ) , fact verification on TabFact ( Chen et al., 2020 ) and anomaly detection on ReTabAD ( Yoon et al., 2026 ) . Metrics: Macro-F1, accuracy and AUROC, averaged over datasets or sources.
| Question , options with their descriptions | |
|---|---|
| original | {no, yes} with descriptions |
| options reversed | {yes, no} |
| renamed A, B | {A, B}, descriptions kept |
| synonyms | {false, true} |
| yes/no question | "Is the target class yes?" |
| paraphrased | "Determine the class …" |
Figure 1: One row-classification request (blood donation) under the rewrites of Figure 3 , shortened to the part that each rewrite changes. Shuffling or reversing the columns permutes their names, descriptions and values together.
Figure 2: In-context examples (RQ1). Grey lines show the best of eight classifiers or five anomaly detectors trained on the same rows, and dotted lines the best trained on all training rows. max stands for as many rows as fit in the context. In (c), stats is the original request of ReTabAD, which summarizes the normal training samples by statistics. none leaves these statistics out, and +16 and +max add raw normal samples.
Figure 3: Row classification, per rewrite: decisions that differ from the model’s original decision (left) and the change of Macro-F1 (right). The upper block shows RQ2 and the lower block RQ3. Filled markers are decision models and hollow markers LLMs. Clef’s point for option order is hollow because Clef sorts the options.
| performance | generated tokens | |||||
| Model | Cl. | TF | AD | Cl. | TF | AD |
| Jev | 52.1 | 89.7 | 74.6 | none: one pass b | ||
| Clef | 58.4 | 85.0 | 74.7 | none: one pass | ||
| Kev-27B | 55.2 | 88.8 | 81.2 | none: one pass | ||
| GPT-6 Luna Decisions | 61.2 | 78.7 | 80.5 | none c | ||
| GPT-6 Luna | 62.5 | 79.0 | 77.3 | 5.6 | 11.8 | 87.5 |
Table 3: RQ1: performance under the original request and the tokens each model generates per decision (an anomaly batch carries 15 decisions). Cl. = row classification (Macro-F1), TF = TabFact (accuracy), AD = anomaly detection (AUROC). a Best of eight classifiers trained on the whole training split, LightGBM (Cl.), and of five anomaly detectors, -nearest neighbours (AD, 19 of 20 sources). We train no model for TabFact. b As stated by its provider. Jev’s architecture is not public. c No output tokens are billed.
| seconds | US$ / million | |||||
|---|---|---|---|---|---|---|
| Model | Cl. | TF | AD | Cl. | TF | AD |
| Jev | 0.08 | 1.33 | 0.09 | 10 | 38 | 16 |
| GPT-6 Luna Decisions | 0.10 | 1.60 | 0.11 | 24 | 61 | 37 |
| GPT-6 Luna | 1.62 | 1.59 | 0.73 | 81 | 62 | 68 |
Table 4: Latency and price per decision of the closed models: mean wall-clock seconds, and US$ per million decisions as billed, on the original requests. A Jev or GPT-6 Luna Decisions request answers 16 rows or a batch of 15 samples, and a GPT-6 Luna request one row, one claim or one batch.
| Model | Row classif. | TabFact | Anomaly det. |
|---|---|---|---|
| Jev | 33.8 | 19.3 | 21.1 |
| Clef | 31.7 | 23.0 | 25.8 |
| Kev-27B | 32.2 | 18.3 | 17.7 |
| GPT-6 Luna Decisions | 39.6 | 38.3 | 29.9 |
| GPT-6 Luna | 42.8 | 60.8 | 63.4 |
| Qwen3.8-27B | 31.8 | 27.4 | 23.3 |
Table 5: Items (%) whose decision changes under at least one information-preserving rewrite of the table (6, 10 and 8 types of rewrite, Table 12 ). GPT-6 Luna is run at temperature 1, so its shares include its re-send rate.
| Model | Confidence | Cl. | TF | AD |
|---|---|---|---|---|
| Trained from the same backbone | ||||
| Clef | probability | 1.4 | 4.1 | 0.7 |
| Kev-27B | probability | 3.1 | 2.5 | 0.1 |
| Qwen3.8-27B | ten draws | 2.6 | 9.7 | 4.8 |
| stated | 23.4 | 29.2 | 9.2 | |
| The same model through two interfaces | ||||
Table 6: Of each model’s 25% most confident items, the share (%) changed by at least one rewrite of the table. Cl. = row classification, TF = TabFact, AD = anomaly detection.
Figure 4: Re-querying the least confident rows (row classification). For the x% of rows with the lowest confidence, the answer in the original format is replaced by the combination of the answers in the original and the four other formats. The y-axis gives the share of the Macro-F1 gain obtained when every row is re-queried, and the legend gives that gain in points.
Appendix figures & tables16 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 5: Decision models: observed flip rate against the rate predicted when a rewrite adds item-independent noise to the margin, per margin decile and task.
| predicts a flip | predicts an error | |||||
| Model | Row classif. | Anomaly | TabFact | Row classif. | Anomaly | TabFact |
| Jev | 0.87 [0.86, 0.88] | 0.89 [0.89, 0.90] | 0.89 [0.88, 0.91] | 0.59 | 0.65 | 0.82 |
| Clef | 0.85 [0.85, 0.86] | 0.89 [0.88, 0.89] | 0.87 [0.86, 0.89] | 0.67 | 0.61 | 0.84 |
| Kev-27B | 0.85 [0.84, 0.86] | 0.92 [0.92, 0.93] | 0.90 [0.88, 0.91] | 0.70 | 0.70 | 0.84 |
| GPT-6 Luna Decisions | 0.82 [0.81, 0.83] | 0.83 [0.83, 0.84] | 0.81 [0.80, 0.83] | 0.74 | 0.70 | 0.78 |
| GPT-6 Luna | 0.83 [0.82, 0.84] | 0.71 [0.70, 0.72] | 0.73 [0.72, 0.75] | 0.68 | 0.62 | 0.69 |
Table 9: How well a model’s confidence in its original decision predicts (left) that an information-preserving rewrite changes the decision and (right) that the original decision is wrong (flip AUROC and error AUROC; 0.5 is chance). Left: pooled over the rewrites of Figure 3 and the table formats, with 95% cluster-bootstrap intervals. Confidence: for the decision models, the returned probability of the chosen option; for the LLMs, the share of ten draws at temperature 1 that give the original decision. † Strict parse: an answer such as “Yes.” to the yes/no question counts as invalid, hence as changed. Ignoring case and a final full stop: Llama-3.3-70B on row classif. 0.66 [0.65, 0.67].
| Model | Ten draws | Stated | Answer prob. |
|---|---|---|---|
| Row classification | |||
| GPT-6 Luna | 0.83 | 0.72 | 0.71 |
| Qwen3.8-27B | 0.77 | 0.60 | 0.65 |
| Llama-3.3-70B | 0.64 | 0.50 | 0.73 |
| weakest of Jev, Clef and Kev-27B: Kev-27B, 0.85 | |||
| GPT-6 Luna Decisions: 0.82 | |||
Table 10: Every readout of an LLM’s confidence, on the same rewrites: flip AUROC (rewrites shared by both kinds of model). Ten draws : share of ten draws at temperature 1 that give the original decision, the readout of the main text. Stated : the confidence the model gives when asked, 0–100 (anomaly detection: the confidence in its answer). Answer prob. : probability of the first token of the generated label, or the stated anomaly score’s distance from 0.5; for GPT-6 Luna, agreement of three draws. Bold: the best readout of each model and task. –: not available.
| arXiv | Table input (application or setting) |
|---|---|
| 2609.23136 | telemetry tables (6G network intents) |
| 2609.24395 | video feature records (video quality) |
| 2609.27535 | survey micro-data as personas (social science) |
| 2609.33689 | KPM records (5G network control) |
| 2609.36154 | accelerometer features (activity recognition) |
| 2609.37470 | joined tables or nested records (synthetic world) |
Table 11: Of the 112 arXiv papers on decision models posted between 15 September and 8 October 2026, the fourteen whose main input is a table or table rows.
| Task | Code | Rewrite | Source |
| Row classification | COLS | Columns shuffled: one fixed permutation per dataset; field descriptions, names and values permuted together | order |
| COLR | Columns in reverse order | order | |
| LIST | One line per field, “- name: value” (TabLLM list) | format | |
| TEXT | One sentence per field, “The name is value.” (TabLLM text) | format | |
| JSONOBJ | JSON object {name: value} | format | |
| MD | One-row Markdown table (header and value row) | format |
Table 12: Rewrites of the table that keep the information needed for the decision. The original format is, for row classification, field descriptions followed by one array of values; for TabFact, the official format with a header line and # between cells; for anomaly detection, the serializer of the ReTabAD authors with 15 samples per batch. Source: order of rows and columns, or format of the table. a Adds a decimal of precision, so it is left out of Tables 5 and 6 . b Can change the truth of claims that refer to positions ( first , last , …); such claims are left out of Tables 5 and 6 .
| decision models | LLMs | |||||||||||||
| Jev | Clef | Kev-27B | GPT-6 Luna Decisions | GPT-6 Luna | Qwen3.8-27B | Llama-3.3-70B | ||||||||
| Condition | Flip | Score | Flip | Score | Flip | Score | Flip | Score | Flip | Score | Flip | Score | Flip | Score |
| Row classification (Macro-F1) | ||||||||||||||
| original request | – | 52.1 | – | 58.4 | – | 55.2 | – | 61.2 | – | 62.5 | – | 52.2 | – | 44.0 |
| identical re-send | 4.7 | 52.4 | 1.6 | 58.0 | 0.6 | 54.5 | 0.0 | 61.2 | 13.3 | 63.0 | 1.1 | 52.9 | 0.4 | 44.1 |
| options in reverse order | 5.9 | 52.3 | 1.6 | 58.0 | 5.9 | 53.9 | 22.7 | 60.1 | 13.7 | 62.5 | 13.6 | 54.1 | 26.8 | 51.4 |
Table 13: How the question is asked, raw results. Flip : share of items whose decision differs from the model’s own decision under the original request (%; invalid outputs count as flips). Score : the task metric 100 (row classification: Macro-F1 averaged over the 10 datasets; TabFact: accuracy; anomaly detection: AUROC averaged over the 20 sources). Identical re-send : the original request sent again, which gives the re-send rate. Conditions below the line remove or mislead information. Clef sorts the options, so reversing them cannot reach it. On row classification the LLMs receive no option descriptions, and on anomaly detection no options. Llama-3.3-70B answers the yes/no question mostly with “Yes” or “Yes.”, which the strict parse counts as invalid; with a parse that ignores case and a final full stop its flip rate there is 13.0%. Invalid outputs above 5% (%): Llama-3.3-70B on row classification, yes/no question: 54.8; GPT-6 Luna on TabFact, option names A, B, …: 9.8; GPT-6 Luna on TabFact, option names replaced by synonyms: 43.7; GPT-6 Luna on TabFact, no task description: 6.6; GPT-6 Luna on ReTabAD, paraphrased instructions: 7.0; Llama-3.3-70B on ReTabAD, paraphrased instructions: 9.5; GPT-6 Luna on ReTabAD, no task description: 5.2. –: does not apply.
| decision models | LLMs | |||||||||||||
| Jev | Clef | Kev-27B | GPT-6 Luna Decisions | GPT-6 Luna | Qwen3.8-27B | Llama-3.3-70B | ||||||||
| Condition | Flip | Score | Flip | Score | Flip | Score | Flip | Score | Flip | Score | Flip | Score | Flip | Score |
| Row classification (Macro-F1) | ||||||||||||||
| original request | – | 52.1 | – | 58.4 | – | 55.2 | – | 61.2 | – | 62.5 | – | 52.2 | – | 44.0 |
| identical re-send | 4.7 | 52.4 | 1.6 | 58.0 | 0.6 | 54.5 | 0.0 | 61.2 | 13.3 | 63.0 | 1.1 | 52.9 | 0.4 | 44.1 |
| columns shuffled | 15.9 | 51.6 | 15.0 | 57.8 | 15.6 | 54.8 | 18.5 | 57.0 | 20.1 | 60.3 | 20.4 | 52.5 | 17.2 | 46.3 |
Table 14: Order of rows and columns, raw results. Flip : share of items whose decision differs from the model’s own decision under the original request (%; invalid outputs count as flips). Score : the task metric 100 (row classification: Macro-F1 averaged over the 10 datasets; TabFact: accuracy; anomaly detection: AUROC averaged over the 20 sources). Identical re-send : the original request sent again, which gives the re-send rate. Shuffling the questions of a request applies only to the decision models, which answer 16 questions per request; the LLMs receive one row per prompt. Reordering rows can change the truth of TabFact claims with position words. Invalid outputs above 5% (%): GPT-6 Luna on TabFact, columns shuffled: 6.7; GPT-6 Luna on TabFact, columns reversed: 7.2; GPT-6 Luna on TabFact, rows shuffled: 5.9; GPT-6 Luna on TabFact, rows reversed: 6.8; GPT-6 Luna on TabFact, table transposed: 8.2; GPT-6 Luna on ReTabAD, columns shuffled: 7.3; GPT-6 Luna on ReTabAD, columns reversed: 7.1; GPT-6 Luna on ReTabAD, rows shuffled: 8.8; GPT-6 Luna on ReTabAD, rows reversed: 8.0. –: does not apply.
Figure 6: Row classification, by rewrite. Left: flip AUROC. Middle: among flips that change correctness, the share that turn a right answer wrong. Right: standard deviation of the change of the decision models’ margin. Clef is omitted for option order, since it sorts the options.
| decision models | LLMs | |||||||||||||
| Jev | Clef | Kev-27B | GPT-6 Luna Decisions | GPT-6 Luna | Qwen3.8-27B | Llama-3.3-70B | ||||||||
| Condition | Flip | Score | Flip | Score | Flip | Score | Flip | Score | Flip | Score | Flip | Score | Flip | Score |
| Row classification (Macro-F1) | ||||||||||||||
| original request | – | 52.1 | – | 58.4 | – | 55.2 | – | 61.2 | – | 62.5 | – | 52.2 | – | 44.0 |
| identical re-send | 4.7 | 52.4 | 1.6 | 58.0 | 0.6 | 54.5 | 0.0 | 61.2 | 13.3 | 63.0 | 1.1 | 52.9 | 0.4 | 44.1 |
| list (“- name: value”) | 15.0 | 56.0 | 14.0 | 60.3 | 16.1 | 54.6 | 17.2 | 64.7 | 17.7 | 66.5 | 17.9 | 58.4 | 21.3 | 47.9 |
Table 15: Format of the table, raw results. Flip : share of items whose decision differs from the model’s own decision under the original request (%; invalid outputs count as flips). Score : the task metric 100 (row classification: Macro-F1 averaged over the 10 datasets; TabFact: accuracy; anomaly detection: AUROC averaged over the 20 sources). Identical re-send : the original request sent again, which gives the re-send rate. Conditions below the line remove or mislead information. Rendering numbers with three decimals adds precision. Invalid outputs above 5% (%): GPT-6 Luna on TabFact, CSV: 5.1; GPT-6 Luna on TabFact, JSON records: 7.3; GPT-6 Luna on TabFact, TAPEX linearization: 8.9; GPT-6 Luna on ReTabAD, JSON object: 9.7; GPT-6 Luna on ReTabAD, list (“- name: value”): 7.7; GPT-6 Luna on ReTabAD, text (“The name is value.”): 9.2; GPT-6 Luna on ReTabAD, Markdown table: 5.6; GPT-6 Luna on ReTabAD, numbers with three decimals: 8.2; GPT-6 Luna on ReTabAD, values without names: 10.9. –: does not apply.
| Jev | Clef | Kev-27B | GPT-6 Luna Decisions | trained on the same examples | ||||||||
| Row classification (Macro-F1) | Score | Score | Score | Score | LogReg | LightGBM | Best | TabPFN | ||||
| no examples (zero-shot) | 52.1 | – | 58.4 | – | 55.1 | – | 61.2 | – | – | – | – | – |
| 4 examples | 60.2 | +8.1 | 60.0 | +1.6 | 59.0 | +3.8 | 59.6 | 1.6 | 51.8 | 45.3 | 55.2 | 51.5 |
| 16 examples (the original ones) | 60.0 | +7.9 | 61.7 | +3.2 | 62.7 | +7.6 | 60.4 | 0.8 | 58.6 | 51.8 | 58.7 | 59.5 |
| 64 examples b | 65.3 | +12.2 | 68.2 | +9.0 | 69.3 | +12.2 | 65.4 | +3.3 | 69.2 | 69.1 | 69.4 | 70.8 |
| as many as fit (30–681) | 66.7 | +14.6 | 70.6 | +12.1 | 73.0 | +17.6 | 69.9 | +8.7 | 72.2 | 76.0 | 76.3 | 77.8 |
Table 16: In-context examples, raw results. Examples are labelled training rows placed in the request (for TabFact, other tables with a claim and its label; for anomaly detection, normal training records after the normal-sample statistics); the sets are nested. Score : task metric 100. : change of the score from no examples (anomaly detection: from the original request), on the same rows. The other methods are trained on exactly the same examples: logistic regression with cross-validated regularization, LightGBM with few-row settings, and the best of eight trained classifiers (logistic regression, default and tuned; random forest; -nearest neighbours; histogram gradient boosting, LightGBM, CatBoost and XGBoost), the line of Figure 2 ; on the normal samples, isolation forest, one-class SVM, -nearest neighbours and the best of five detectors. TabPFN : TabPFN-3.5, a tabular foundation model that reads the same examples in its context without training (not run on the whole split); TabPFN v2, run the same way, scores 52.3, 57.3, 69.3, 76.4 with 4, 16, 64 and as many examples as fit. b Without communities (about 100 features: 64 examples exceed the budget); the trained methods are averaged over the same nine datasets. The trained anomaly methods cover 19 of the 20 sources (covertype left out).
| GPT-6 Luna | Qwen3.8-27B | Llama-3.3-70B | ||||
| Row classification (Macro-F1) | Score | Score | Score | |||
| no examples (zero-shot) | 62.5 | – | 52.2 | – | 44.0 | – |
| 4 examples | 64.9 | +2.4 | 59.2 | +7.0 | 57.6 | +13.5 |
| 16 examples (the original ones) | 64.5 | +2.0 | 59.5 | +7.3 | 56.7 | +12.6 |
| 64 examples b | 66.2 | +3.0 | 65.4 | +10.4 | 57.4 | +12.5 |
| as many as fit (30–681) | 69.3 | +6.8 | 67.1 | +14.9 | 60.7 | +16.6 |
Table 17: In-context examples, raw results of the LLMs; setup, Score and as in Table 16 , where the methods trained on the same examples are listed. b Without communities (about 100 features: 64 examples exceed the budget).
Figure 7: Left: flip AUROC within each dataset or source; bars are medians. Right: for each row classification dataset, the mean confidence of the original decisions against the share of decisions changed by the information-preserving rewrites.
| confidence band (1: lowest 25%) | noise | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Task | Model | Items | All | 1 | 2 | 3 | 4 | Re-send | Bound | Question | |
| Row classif. | Jev | 3,481 | 6 | 33.8 [32.2,35.4] | 78.0 [75.3,80.7] | 41.6 | 14.7 | 0.9 [0.3,1.5] | 4.7 | 25.3 | 20.2 [18.9,21.6] |
| Clef | 3,481 | 6 | 31.7 [30.1,33.2] | 77.5 [74.8,79.9] | 37.2 | 10.7 | 1.4 [0.7,2.3] | 1.6 | 9.0 | 17.3 [16.2,18.5] | |
| Kev-27B | 3,481 | 6 | 32.2 [30.6,33.7] | 76.7 [74.2,79.6] | 40.0 | 9.0 | 3.1 [2.0,4.3] | 0.6 | 3.6 | 17.1 [15.8,18.2] | |
| GPT-6 Luna Decisions | 3,481 | 6 | 39.6 [38.2,41.2] | 80.9 [78.3,83.6] | 52.3 | 23.2 | 1.8 [1.0,2.8] | 0.0 | 0.0 | 36.7 [35.1,38.2] | |
| GPT-6 Luna a | 3,481 | 6 | 42.8 [41.2,44.3] | 88.7 [86.6,90.9] | 41.5 | 20.6 | 20.2 [17.6,22.7] | 13.3 | 57.5 | 34.9 [33.3,36.4] | |
Table 18: Items whose decision depends on how the table is organized (%). All : share of items whose decision changes under at least one of the information-preserving table rewrites of Table 12 ; 1–4 : the same for the items sorted by the model’s confidence in its original decision and split into four bands of 25%, 1 the lowest (ties broken at random with a fixed seed). Re-send : share of decisions changed by an identical re-send of the original request ( ). Bound : , the share that would change at least once if every rewrite flipped independently at rate ; an upper bound on what noise alone explains, since re-send flips concentrate on the same low-margin items. Question : share of items changed by at least one rewrite of the question (options reordered or renamed, yes/no question, option descriptions removed, paraphrased instructions). Brackets (shown for all items, the outer bands and the question): 95% cluster-bootstrap intervals (Poisson(1) weights, 500 replicates; clusters are classification rows, anomaly-detection samples and TabFact tables). Invalid outputs count as changes. Numbers with three decimals and TabFact claims with position words are left out. Jev’s original TabFact decisions carry no probabilities. Confidence: for the decision models, the returned probability of the chosen option; for the LLMs, the share of ten draws at temperature 1 that give the original decision. On anomaly detection the LLMs receive no options, so only one question rewrite (paraphrase) applies to them. a Its decisions are themselves draws at temperature 1, so its counts are dominated by temperature noise. b Mostly answers such as “Yes.” to the yes/no question, which the strict parse counts as invalid.
Figure 8: Items whose decision changes under at least one information-preserving rewrite of the table. Items are sorted by the model’s confidence in its original decision and split into four bands of 25%; 95% cluster-bootstrap intervals.
Figure 9: Share of decisions changed by a rewrite, for items ranked by the model’s confidence in its original decision and split into bands of 10% (information-preserving rewrites pooled). Confidence as in the main text: the decision models’ returned probability and the LLMs’ share of ten draws that give the original decision; tied values are split at random.
| Model | Right (%), top / low 25% | stable, right | stable, wrong | fragile, right | fragile, wrong |
|---|---|---|---|---|---|
| Row classification | |||||
| Jev | 67 / 48 | 44 / 0.79 | 23 / 0.78 | 16 / 0.59 | 18 / 0.56 |
| Clef | 86 / 52 | 52 / 0.84 | 16 / 0.78 | 16 / 0.61 | 16 / 0.59 |
| Kev-27B | 85 / 43 | 48 / 0.76 | 19 / 0.67 | 14 / 0.55 | 19 / 0.54 |
| GPT-6 Luna Decisions | 94 / 49 | 49 / 0.91 | 11 / 0.82 | 21 / 0.68 | 19 / 0.62 |
| GPT-6 Luna | 82 / 46 | 48 / 0.98 | 9 / 0.95 | 23 / 0.77 | 20 / 0.65 |
Table 19: Confidence against correctness and fragility (RQ4). Second column: share of right decisions among the 25% of decisions with the highest and the lowest confidence. Other columns: share of decisions (%) and their mean confidence in each group; a decision is fragile when at least one rewrite of the table flips it. Confidence as in § 6 .
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