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A 36-case Kaggle benchmark found a sharp gap between recognizing sound data-science advice and producing that advice independently. Given four answer choices, the tested language models selected the measured answer in 94–100% of cases. Asked the same questions without choices, they gave the keyed recommendation only 56–81% of the time. The result is not a general ranking of intelligence: it is a test of judgment when unusual data, leaderboard noise and competition-specific measurements make a familiar heuristic unreliable.
Khushi’s submission, published October 9, 2026, is useful because every case has an observed competition result behind it. The benchmark shows where an LLM can identify a correct option yet fail to reconstruct the mechanism that makes it correct.
What did the Kaggle benchmark actually test?
The benchmark contains 12 data-science topics, each written in three numeric variants, for 36 cases in total. The cases ask for practical decisions rather than definitions: whether a tiny p-value matters at very large sample size, which correlation measure is meaningful when zeros dominate, whether an apparent improvement is larger than leaderboard uncertainty, and which model or submission to use.
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- “ds-judgment” multiple choice: four options included the measured answer, a classic heuristic trap and two plausible distractors.
- “ds-judgment-open” generation: the options were removed and the model had to give a recommendation and its main reason in no more than 120 words.
The answer letters were balanced: the keyed position appeared nine times as A, B, C and D. Options were also length-matched, reducing the chance that a longer or more qualified answer acted as a superficial clue. A fixed Gemini 3.8 Flash judge mapped each open response to one of the four answer positions or to E when the answer was absent, hedged or did not match a keyed position.
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That design isolates a specific capability: can a model recover a measured decision and its causal explanation when no answer choices point toward it?
How large is the recognition-to-generation gap?
Every listed model performed far better with answer choices than without them. Multiple-choice scores ranged from 34 to 36 correct out of 36, while open-ended scores ranged from 20 to 29.
| Model | Multiple choice | Open-ended | Difference |
|---|---|---|---|
| Claude Sonnet 5.5 | 35/36 | 29/36 | 6 cases |
| Gemma 4 26B-A4B | 35/36 | 27/36 | 8 cases |
| Grok 4.20 Reasoning | 36/36 | 26/36 | 10 cases |
| Gemini 3.8 Flash | 36/36 | 26/36 | 10 cases |
| Gemini 3.1 Pro | 36/36 | 25/36 | 11 cases |
| Gemini 3.7 Flash | 36/36 | 24/36 | 12 cases |
| GPT-6.1 Sol | 34/36 | 24/36 | 10 cases |
| Gemini 3.5 Flash-Lite | 35/36 | 23/36 | 12 cases |
| DeepSeek R1 | 34/36 | 23/36 | 11 cases |
| Claude Haiku 4.5 | 36/36 | 20/36 | 16 cases |
Across models, the author summarizes the result as follows: shown four options, models recognized the measured answer 94–100% of the time; asked the same question without options, they gave that advice only 56–81% of the time. A perfect multiple-choice score therefore cannot be read as proof that a model will make the same decision in a notebook or competition.
What does a reasonable recommendation with the wrong explanation look like?
The clearest failure involves two delay columns. They had a Pearson correlation of r = 0.84 and a Spearman correlation of ρ = 0.47, while 91% of rows were exact zeros in both columns.
A model could reasonably recommend treating the columns as strongly related or avoiding a simplistic interpretation of the rank correlation. The measured mechanism, however, is that a mass of tied zeros dilutes Spearman’s rank relationship: ranks cannot express the ordering among rows tied at zero. The model may notice the zeros yet infer an incorrect story about delayed observations or monotonicity. In the benchmark, recognizing the right action was not enough; the reason had to identify the mechanism that produced the observed numbers.
This distinction explains many of the E classifications. The answer sounded defensible, but its stated reason did not correspond to any keyed position. In a real analysis, such a reason can lead to the wrong follow-up experiment even when the first action happens to be safe.
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Which data-science judgment calls were hardest?
Open-ended accuracy was especially poor on cases where a familiar rule conflicts with a measured effect. Averaged across 11 complete runs, the topic-level results were:
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| Topic | Open-ended accuracy | What made it difficult |
|---|---|---|
| Pearson versus Spearman with 91% zeros | 0.00 | Ties change how rank correlation reflects association. |
| A zero code meaning “not applicable” | 0.03 | The numeric value is a category or status, not a quantity. |
| Final-submission selection | 0.24 | The highest public score is not automatically the safest final choice. |
| More folds that improve out-of-fold results | 0.30 | Better OOF performance may not move a leaderboard score. |
| Cross-validation versus leaderboard resolution | 0.79 | The key comparison is gain size versus score uncertainty. |
| All other topics | At least 0.97 each | These cases were easier for the tested models under the benchmark’s key. |
The author recorded 122 open-ended failures across the runs and none were judged to be the classic trap option. Most were E answers: the model proposed an action that could be defended, but attached a reason that was not the measured one. This is a generation problem, not simply a tendency to choose the obviously wrong heuristic.
What measured mechanisms overturned common heuristics?
Do tiny p-values prove that every feature matters?
No. In one case, 19 of 21 features had p < 1e-300, yet univariate AUC ranged from 0.840 to 0.506. With a large enough sample, a test can make a very small departure from zero statistically undeniable while the feature remains nearly useless for ranking predictions. The practical question is effect size and predictive utility, not whether the null-hypothesis test crossed a threshold.
Why can Pearson and Spearman disagree when zeros are common?
Pearson uses the numeric distances between values; Spearman replaces values with ranks. When 91% of both delay columns are exactly zero, thousands of tied ranks reduce the information available to Spearman. The measured correlations—0.84 for Pearson and 0.47 for Spearman—are not interchangeable evidence about the same structure.
What should you do when zero means “not applicable”?
First establish the data dictionary or collection process. A zero that means “not applicable,” “not measured” or “no event” is not an ordinary measurement of magnitude. Treating it as a continuous value can create artificial ordering, correlations and split points; an indicator for applicability and a separately handled value may better represent the process.
When is a cross-validation gain too small to matter?
The benchmark gives a concrete scale. Public-leaderboard standard error was approximately 0.0003, while some cross-validation improvements were only +0.00005. Gains that small never appeared on the leaderboard. A measured improvement should therefore be compared with the uncertainty of the evaluation channel, not celebrated solely because its decimal value is positive.
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Why did ten-fold cross-validation fail to improve the leaderboard?
Ten-fold cross-validation raised out-of-fold performance by +0.00012 but changed the leaderboard by +0.0000. Larger training folds can improve the quality of OOF predictions used for local analysis without materially changing predictions averaged on the hidden test set. Reverting automatically because the OOF gain is “noise,” or upgrading automatically because it is positive, both miss the measured training-size mechanism.
Should original competition data always be appended?
Not in this case. Appending original-data rows changed the score by −0.00025. By contrast, using a model trained on the original data as a feature changed the score by +0.0007. The two strategies expose the competition model to the same source in different ways; one can hurt while the other helps. The result is a reminder to test the exact intervention rather than generalize from the word “original.”
What did the leak checks find?
The checks found zero duplicates and zero train/test overlap. They did find 17 of 129,880 original rows shared. That count is neither proof of a useful leak nor proof that the source is harmless. It is a prompt to inspect which rows, fields and labels match and to measure the effect of excluding or exploiting them under a valid split.
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The gradient-boosting models had prediction correlations from 0.9985 to 0.9994, so averaging them supplied little independent information. A weaker neural network added more stack diversity. A model can be less accurate alone yet useful in a blend when its errors are different; raw model strength and ensemble contribution are separate decisions.
Why is automatically selecting the best public submission risky?
In the author’s example, auto-picking the best public submissions ended at rank 708 of 3,575. A public score is a noisy estimate of hidden-test performance, so selecting whichever file is currently highest can overfit the leaderboard. Selection should consider validation stability, prediction diversity and the size of score differences, not just the latest displayed rank.
What does the step-shaped feature case show?
A feature with a step-shaped relationship can defeat assumptions that the response changes smoothly or linearly. Binning, trees or explicitly modeling thresholds may be appropriate, but the right choice depends on where the measured jumps occur and how stable they are. The benchmark uses this kind of case to test whether a model notices shape rather than applying a default linear rule.
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How were the open answers judged, and where is that scoring fragile?
A single Gemini 3.8 Flash judge assigned each response to one of the four answer positions or E. That makes the benchmark reproducible, but it also means the judge’s interpretation affects the result. The author identified two answer keys as too strict, especially for final-submission answers where a cautious manual hedge could be acceptable in practice.
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Does this prove that LLMs do not understand data science?
No. It establishes a narrower finding: models can recognize a measured answer when the alternatives expose the relevant distinction more reliably than they can generate that answer and its reason from scratch. The benchmark is about judgment under unusual conditions, not a comprehensive test of mathematics, coding, scientific knowledge or general intelligence.
There are also statistical and scope limits. Thirty-six cases mean one case is about 2.8 percentage points of the total. Ground truth came from two Playground competitions, and the exact score changes are competition-specific. Mechanisms such as rank-tie dilution, training-fold size and leaderboard resolution can recur elsewhere, but their magnitudes should be remeasured rather than copied.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should practitioners use an LLM for competition decisions?
Use a model as a hypothesis generator and audit assistant, not as an authority that replaces measurement.
- State the observed numbers. Include sample size, score uncertainty, tie rates, correlations and the exact validation design.
- Ask for the mechanism. Require the model to explain why the proposed action should change predictions, not merely name a familiar best practice.
- Request competing interpretations. Ask what would make the opposite recommendation correct and which measurement would distinguish the cases.
- Separate action from confidence. A reversible experiment may be worthwhile even when the model is uncertain; an irreversible submission decision needs stronger evidence.
- Run the intervention. Compare the change under the same split, seed policy and submission process. Treat improvements smaller than evaluation error as unresolved.
- Check for answer-choice dependence. If a model performs well only when options are supplied, provide independent prompts, counterexamples and numeric variants before trusting its advice.
This workflow turns a fluent explanation into a testable claim. It also makes it easier to catch the benchmark’s central failure mode: a plausible recommendation supported by the wrong causal story.
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What should future evaluations add?
Khushi proposes three improvements: a second judge from another model family, a separate score for the quality of the reason, and prompts that explicitly warn that a common heuristic may be wrong. Those changes would distinguish disagreement with a narrow key from a genuinely invalid explanation and reduce dependence on one evaluator.
A stronger follow-up would also preserve the benchmark’s measured ground truth while expanding the number and diversity of competitions. Cases should continue to report both the observed magnitude and the general mechanism, so readers can tell which lessons transfer and which belong only to one leaderboard.
Frequently Asked Questions
Why do multiple-choice scores look so high compared with open answers?
Answer choices provide candidate hypotheses and often expose the exact contrast the case is testing. An open prompt requires the model to retrieve the relevant mechanism, select an action and explain it without those cues, so recognition and generation measure different abilities.
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E means the judge could not map the response to one of the four keyed positions. It includes hedged answers and responses whose recommendation might be defensible but whose stated reason does not match the measured mechanism.
Can I inspect the benchmark prompts and model transcripts?
The author says the prompts, options, measured answers and full transcripts are available through the benchmark’s Kaggle notebooks and run logs. Those materials are the best way to examine individual cases rather than relying only on aggregate scores.
How can I tell whether an LLM explanation is causal or just plausible?
Ask what observation would falsify the explanation, which measurement produced the effect and how the recommendation would change under a counterfactual. Then run the smallest controlled comparison that separates those possibilities.
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