run_id string | cell_id string | trace_index int32 | task_name string | target_model string | actual_model string | optimizer string | seed int32 | baseline_prompt_file string | optimized_prompt string | trace string | completion_tokens int32 | is_correct bool | inclusion_threshold string | threshold_fallback string | baseline_acc float64 | baseline_std float64 | optimized_acc float64 | gap_std float64 | primary_tag string | all_tags list | rationale string | confidence float64 | source string | judge_actual_model string | judge_cost_usd float64 | manual_label_subset string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 64 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | neutral
The provided review is more of a factual statement about the director and the nature of the film rather than an expression of sentiment. It does not convey any strong positive or negative emotions. | 39 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra in-context examples, all labeled 'neutral', which biases the model toward predicting 'neutral' for ambiguous or factual reviews. The representative trace shows the model outputting 'neutral' for a review that should be 'positive', and the added examples do not improve generalizatio... | 0.9 | llm_judge | deepseek-v4-flash | 0.000729 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 65 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | neutral
The review seems to describe the film as an ironic exploration of a specific cultural theme without expressing strong positive or negative emotions. Therefore, it fits best into the neutral category. | 37 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | over_specification | [
"over_specification"
] | The optimized prompt adds three extra in-context examples (all labeled 'neutral') that bias the model toward predicting 'neutral' for ambiguous or mixed reviews. The representative trace shows the model outputting 'neutral' with a plausible but incorrect justification, indicating the added examples over-specify the dec... | 0.9 | llm_judge | deepseek-v4-flash | 0.000716 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 67 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | neutral
The review describes the film as "offbeat," "sometimes gross," and "surprisingly appealing," which suggests a mixed but ultimately neutral sentiment. The phrase "surprisingly appealing" indicates that despite some potentially off-putting elements, the film has some positive qualities, but the overall tone does... | 70 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | instruction_collapse | [
"instruction_collapse"
] | The optimized prompt adds several neutral-labeled examples that bias the model toward predicting 'neutral' for reviews that are actually very positive. The core classification instruction is weakened by over-representing neutral exemplars, causing the model to default to 'neutral' even when the review clearly warrants ... | 0.95 | llm_judge | deepseek-v4-flash | 0.000715 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 68 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | neutral
The review suggests that the documentary is informative ("illuminating") but also somewhat talky, which is a balanced perspective. This doesn't lean strongly towards positive or negative, hence a neutral sentiment. | 43 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | trivial_restatement | [
"trivial_restatement"
] | The optimized prompt is nearly identical to the baseline, only adding three extra in-context examples that are all labeled 'neutral'. This does not sharpen the instruction or add task-relevant structure; it merely pads the prompt with redundant exemplars that bias the model toward predicting 'neutral' for ambiguous rev... | 0.95 | llm_judge | deepseek-v4-flash | 0.000716 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 69 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: negative
The review suggests that the animation style is a combination of standard, stiff TV-style animation and digital effects that do not effectively bring the characters to life, indicating a negative sentiment. | 41 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra neutral examples from the validation set, biasing the model toward predicting 'neutral' for ambiguous reviews. The trace shows the model outputting 'negative' but then explaining why it's negative, yet the gold label is 'neutral', indicating the added examples did not help and like... | 0.9 | llm_judge | deepseek-v4-flash | 0.000722 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 73 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | very negative
The review suggests that the movie is of such poor quality that it is only suitable for a low-brow audience, which indicates a very negative sentiment. | 34 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | over_specification | [
"over_specification"
] | The optimized prompt adds three extra neutral examples from the validation set, biasing the model toward predicting 'neutral' for reviews that are not clearly extreme. This over-specification to the validation distribution causes the model to misclassify a clearly negative review as 'very negative' (overcorrecting) and... | 0.85 | llm_judge | deepseek-v4-flash | 0.000721 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 74 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: slightly negative
The review suggests that while the movie has some interesting aspects, it falls short of being exceptional, which leans towards a negative sentiment but not strongly enough to be classified as very negative. | 43 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | format_break | [
"format_break"
] | The optimized prompt is nearly identical to the baseline but adds extra few-shot examples. The representative trace shows the model outputting 'slightly negative' instead of one of the five required labels ('very negative', 'negative', 'neutral', 'positive', 'very positive'), and then appending an explanatory sentence.... | 0.95 | llm_judge | deepseek-v4-flash | 0.000737 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 76 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: negative
The review describes the movie as a "heavy-handed exercise in time-vaulting literary pretension," which suggests that the reviewer found the film to be overly focused on pretentious literary themes and not effectively executed. This indicates a negative sentiment. | 54 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra in-context examples, all of which are labeled 'neutral'. This biases the model toward predicting 'neutral' for many test reviews, as seen in the trace where a clearly negative review ('heavy-handed exercise...') is misclassified as 'negative' but the model's reasoning still leans t... | 0.9 | llm_judge | deepseek-v4-flash | 0.000743 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 78 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: negative
The review describes the subject as a "self-infatuated goofball" and "clumsy comedy," which are negative descriptors. While it acknowledges that the main character is "the most obvious one" of the issues, the overall tone is critical and negative. | 59 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra neutral examples from the validation set that bias the model toward predicting 'neutral' for borderline reviews, causing it to output 'negative' instead of 'very negative' for a clearly very negative review. The added examples do not generalize to the test distribution. | 0.9 | llm_judge | deepseek-v4-flash | 0.000715 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 80 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | very positive | 3 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra in-context examples, all labeled 'neutral', which biases the model toward predicting 'neutral' for many test reviews. The representative trace shows a review that should be 'positive' but the model outputs 'very positive' — however, the overall accuracy drop (3.16σ) is consistent w... | 0.85 | llm_judge | deepseek-v4-flash | 0.000733 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 82 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | very negative
The review "a bravura exercise in emptiness" expresses a strong negative sentiment, indicating that the movie is seen as lacking substance or meaning. | 33 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra neutral examples from the validation set that bias the model toward predicting 'neutral' for ambiguous or slightly negative reviews, causing it to misclassify 'a bravura exercise in emptiness' as 'very negative' instead of 'negative'. The added examples do not generalize to the tes... | 0.9 | llm_judge | deepseek-v4-flash | 0.000718 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 86 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: positive
The review suggests that the movie is well-thought-out, mature, and effectively conveys the director's vision, which are generally positive attributes. | 35 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | trivial_restatement | [
"trivial_restatement"
] | The optimized prompt is nearly identical to the baseline, only adding three extra in-context examples that are all labeled 'neutral'. This does not sharpen the instruction or add task-relevant guidance; it merely pads the prompt with redundant examples. The underperformance likely stems from the added examples biasing ... | 0.85 | llm_judge | deepseek-v4-flash | 0.000734 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 88 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | The sentiment of this review is neutral. The review describes the film's approach to its characters and themes without expressing strong positive or negative emotions. It mentions that the film is an "actor's showcase" and achieves its goals, but the language used is not particularly enthusiastic or critical. | 57 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra neutral examples from the validation set, biasing the model toward predicting 'neutral' for ambiguous reviews. The representative trace shows the model outputting 'neutral' with a plausible but generic justification, yet the gold answer is also neutral — the underperformance likely... | 0.85 | llm_judge | deepseek-v4-flash | 0.000739 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 90 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: very positive
The review suggests that the movie has a unique and captivating quality that is hard to resist, which indicates a very positive sentiment. | 32 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra in-context examples, all labeled 'neutral', which biases the model toward predicting 'neutral' for many test reviews. The representative trace shows the model outputting 'very positive' for a clearly positive review, but the added examples likely skew the distribution away from the... | 0.9 | llm_judge | deepseek-v4-flash | 0.000723 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 91 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | very negative | 3 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra neutral examples from the validation set that bias the model toward predicting 'neutral', causing it to misclassify a 'negative' review as 'very negative' due to overfitting to the neutral-heavy pattern. The added examples do not improve generalization and lead to a significant acc... | 0.9 | llm_judge | deepseek-v4-flash | 0.000701 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 95 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | negative
The review expresses skepticism and criticism towards the film's length and its approach to the subject matter, indicating a negative sentiment. | 27 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | instruction_collapse | [
"instruction_collapse"
] | The optimized prompt adds several neutral examples that dilute the classification task, causing the model to output a bare label ('negative') followed by an explanatory sentence instead of the required 5-point scale label. This weakens the core instruction and leads to underperformance on the test slice. | 0.9 | llm_judge | deepseek-v4-flash | 0.000703 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 96 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: very negative
The review "it's also stupider" clearly expresses a very negative sentiment towards the movie. | 26 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra neutral examples from the validation set that bias the model toward predicting 'neutral' for many reviews, but the representative trace shows the model outputting 'very negative' for a review that should be 'negative'. The added examples do not improve generalization and likely cau... | 0.85 | llm_judge | deepseek-v4-flash | 0.000725 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 97 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | positive
The review suggests that the presence of Zhao Benshan adds a positive element to the film, making the preposterous character more engaging than expected. This indicates a positive sentiment towards the performance and overall impact of the film. | 48 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra in-context examples, all labeled 'neutral', which biases the model toward predicting 'neutral' for many test reviews. The representative trace shows the model outputting 'positive' but then explaining why it's positive, yet the gold answer is 'neutral' — the added examples have ske... | 0.9 | llm_judge | deepseek-v4-flash | 0.000734 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 99 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: negative
The review suggests that the movie is a confusing mix of genres without a clear focus, which is not a positive assessment. The phrase "never really finds the tonal or thematic glue it needs" indicates that the movie lacks cohesion, which is a negative point. | 58 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra neutral examples from the validation set, biasing the model toward predicting 'neutral' for ambiguous reviews. The representative trace shows the model outputting 'negative' despite the gold label being 'neutral', and the reasoning indicates the model is overfitting to the pattern ... | 0.85 | llm_judge | deepseek-v4-flash | 0.000727 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 102 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | neutral
The review describes the movie as a mix of "bodice-ripper" and "plodding costume drama," which suggests a balanced, unremarkable view without strong positive or negative sentiment. | 43 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra in-context examples, all of which are labeled 'neutral'. This biases the model toward predicting 'neutral' for many reviews, as seen in the trace where a review with mixed/negative language is labeled 'neutral'. The added examples do not improve generalization and instead cause the... | 0.95 | llm_judge | deepseek-v4-flash | 0.000731 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 103 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | neutral
The review suggests that the movie has some entertaining elements but ultimately fails to stand out, which aligns with a neutral sentiment. | 28 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra neutral examples from the validation set, biasing the model toward predicting 'neutral' for reviews that are actually negative. The trace shows the model outputting 'neutral' with a justification that ignores the negative cues, and the accuracy drop is consistent with overfitting t... | 0.95 | llm_judge | deepseek-v4-flash | 0.000716 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 104 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: negative
The review suggests that the movie is similar to other Seagal movies in a negative way, describing it as "only louder" and dismissive of the distinguishing factor of the ponytail, implying that it lacks originality or appeal. | 52 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra neutral examples from the validation set, biasing the model toward predicting 'neutral' for ambiguous reviews. The trace shows a negative review being classified as neutral, consistent with overfitting to the injected neutral exemplars. | 0.9 | llm_judge | deepseek-v4-flash | 0.0007 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 106 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | positive
The review suggests that the story is powerful and leaves a lasting impact, which indicates a positive sentiment. | 23 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | instruction_collapse | [
"instruction_collapse"
] | The optimized prompt adds three extra neutral examples that bias the model toward predicting 'neutral' for ambiguous or moderately positive reviews, effectively weakening the original 5-class instruction. The representative trace shows the model outputting 'positive' instead of 'very positive', and the added examples a... | 0.9 | llm_judge | deepseek-v4-flash | 0.000719 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 107 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | positive
The review describes the movie as "thoughtful" and "lots of fun," which are positive descriptors, even though it also mentions being "stinging at times," which could be seen as a slight negative but in the context of the overall tone, it leans more positive. | 58 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra in-context examples, all labeled 'neutral', which biases the model toward predicting 'neutral' for ambiguous reviews. The representative trace shows the model correctly identifies positive sentiment but outputs 'positive' instead of 'very positive', likely because the added neutral... | 0.9 | llm_judge | deepseek-v4-flash | 0.000721 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 108 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | neutral
The review mentions that the movie has fine acting and soccer action, but it doesn't express strong positive or negative emotions. Therefore, it can be classified as neutral. | 36 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra examples, all labeled 'neutral', which biases the model toward predicting 'neutral' for ambiguous reviews. The representative trace shows the model outputting 'neutral' with a justification that downplays positive language, directly reflecting the skewed distribution of the added e... | 0.9 | llm_judge | deepseek-v4-flash | 0.000703 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 109 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | neutral
The review provided is more reflective and philosophical about the purpose of stories rather than expressing a clear positive or negative sentiment towards the movie itself. Therefore, it is best classified as neutral. | 39 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | over_specification | [
"over_specification"
] | The optimized prompt adds three extra in-context examples (all labeled 'neutral') that bias the model toward predicting 'neutral' for ambiguous or reflective reviews, as seen in the trace where a philosophical review is classified as neutral despite being correct. This over-specification to the validation distribution'... | 0.85 | llm_judge | deepseek-v4-flash | 0.000716 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 112 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | The review provided seems to be a list of action or superhero movie tropes rather than a full review with sentiment. However, based on the positive and exciting elements mentioned, the sentiment could be classified as:
Sentiment: positive
This is because the review highlights exciting and action-packed elements that ... | 68 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | instruction_collapse | [
"instruction_collapse"
] | The optimized prompt adds extra examples that are all labeled 'neutral', which biases the model toward predicting 'neutral' for ambiguous or mixed reviews. The representative trace shows the model ignoring the 5-class instruction and instead reasoning about genre tropes before outputting 'positive' in a non-standard fo... | 0.85 | llm_judge | deepseek-v4-flash | 0.000726 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 113 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | positive
The review suggests that the movie is well-made, intelligent, and has a non-reactionary morality, which are generally positive attributes. Therefore, the sentiment can be classified as positive. | 40 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra neutral examples from the validation set that bias the model toward predicting 'neutral' for reviews that should be classified as 'very positive' or 'positive'. The representative trace shows the model outputting 'positive' instead of 'very positive', and the accuracy drop is consi... | 0.9 | llm_judge | deepseek-v4-flash | 0.000719 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 118 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | neutral
The review expresses a mixed sentiment but leans towards neutral. The reviewer enjoyed the movie while watching it but found that it didn't leave a lasting impression, which aligns more with a neutral sentiment rather than a positive or negative one. | 49 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | trivial_restatement | [
"trivial_restatement"
] | The optimized prompt is nearly identical to the baseline, only adding three extra in-context examples that are all labeled 'neutral'. This does not sharpen the instruction or add task-relevant guidance; it merely pads the prompt with redundant examples that bias the model toward predicting 'neutral' on ambiguous review... | 0.95 | llm_judge | deepseek-v4-flash | 0.000721 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 119 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | positive
The review suggests that Denzel Washington's directorial debut is lean and engaging, which are positive attributes. Therefore, the sentiment can be classified as positive. | 34 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | instruction_collapse | [
"instruction_collapse"
] | The optimized prompt removes the final example from the baseline and adds three new examples, all labeled 'neutral', which biases the model toward predicting 'neutral' for ambiguous or moderately positive reviews. The representative trace shows the model correctly identifies positive sentiment but outputs 'positive' in... | 0.9 | llm_judge | deepseek-v4-flash | 0.00072 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 121 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: negative
The review suggests that while the performances of the leads are somewhat commendable, the overall plot and dialogue are not engaging, leading to a lulling effect, which is generally not a positive experience for the viewer. | 48 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra neutral examples from the validation set that bias the model toward predicting 'neutral' for ambiguous reviews, causing it to misclassify a clearly negative review as negative but with a neutral-leaning rationale. The added examples do not generalize to the test distribution, leadi... | 0.9 | llm_judge | deepseek-v4-flash | 0.00072 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 122 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: very negative
The review "boring we did n't." can be interpreted as "we didn't find it boring," which implies a negative sentiment due to the use of "boring" and the negation. However, since the sentiment is negated, it suggests a mild negative sentiment rather than a very negative one. Given the limited in... | 113 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra neutral examples from the validation set, biasing the model toward predicting 'neutral' for ambiguous or borderline reviews. The trace shows the model overthinking a simple negative review and still outputting 'very negative', but the overall accuracy drop (3.16σ) is driven by the ... | 0.85 | llm_judge | deepseek-v4-flash | 0.000746 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 124 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: very negative
The review describes the movie as a "wozy, roisterous, exhausting mess" and mentions that the casting was "ill-starred," which are strongly negative descriptors. | 42 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra neutral examples from the validation set, biasing the model toward predicting 'neutral' for reviews that are not clearly positive or very positive. This overfitting to the validation distribution causes the model to misclassify a clearly negative review as 'very negative' instead o... | 0.9 | llm_judge | deepseek-v4-flash | 0.000728 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 126 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: negative
The review suggests that "Blackboards" lacks a sense of an ultimate point or bigger meaning, which implies a criticism of the film's lack of depth or purpose. This is a negative sentiment. | 45 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra in-context examples (all labeled 'neutral') that are not present in the baseline. This biases the model toward predicting 'neutral' for many reviews, as seen in the trace where a clearly negative review is misclassified as 'neutral'. The added examples leak a distributional pattern... | 0.95 | llm_judge | deepseek-v4-flash | 0.000732 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 128 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | positive
The review describes the movie as "at times funny and at other times candidly revealing," and it is called "an intriguing look" which indicates a positive sentiment. The overall tone suggests that the reviewer found the movie engaging and worthwhile. | 50 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra neutral-labeled examples from the validation set that bias the model toward predicting 'neutral' for reviews that are actually very positive or positive. The representative trace shows the model outputting 'positive' instead of 'very positive', and the accuracy drop (0.487 to 0.415... | 0.9 | llm_judge | deepseek-v4-flash | 0.000733 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 129 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: negative
The review suggests that the movie, which started as a computer game, turned out to be a "bad one," indicating a negative sentiment. | 34 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra neutral examples from the validation set, biasing the model toward predicting 'neutral' for many reviews. The representative trace shows the model outputting 'negative' when the gold label is 'very negative', indicating that the added examples have shifted the distribution away fro... | 0.9 | llm_judge | deepseek-v4-flash | 0.000709 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 130 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | positive
The review suggests that if you enjoy Motown music, you will enjoy the documentary, which implies a positive sentiment towards the film. | 29 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | trivial_restatement | [
"trivial_restatement"
] | The optimized prompt is nearly identical to the baseline, only adding three extra in-context examples that are all labeled 'neutral'. This does not sharpen the instruction or add task-relevant guidance; it merely pads the prompt with redundant exemplars. The model's output 'positive' instead of 'very positive' shows it... | 0.9 | llm_judge | deepseek-v4-flash | 0.000731 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 131 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: positive
The review describes the story as "simple and heart-warming" and mentions that it should "charm all but the most cynical," which indicates a positive sentiment. | 38 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra in-context examples (all labeled 'neutral') that are not present in the baseline. These examples bias the model toward predicting 'neutral' for reviews that are actually very positive or very negative, as seen in the trace where a clearly very positive review is classified as 'posi... | 0.95 | llm_judge | deepseek-v4-flash | 0.000749 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 132 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: negative
The review suggests that the film is not particularly engaging or charming, with only a few modest positive aspects but overall a lackluster and unimpressive experience. | 38 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | trivial_restatement | [
"trivial_restatement"
] | The optimized prompt is nearly identical to the baseline, only adding three extra in-context examples that are all labeled 'neutral'. This does not sharpen the instruction or add task-relevant guidance; it merely pads the prompt with redundant exemplars. The underperformance likely stems from the added examples biasing... | 0.85 | llm_judge | deepseek-v4-flash | 0.000737 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 137 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | The review provided is somewhat cryptic and doesn't clearly convey a strong positive or negative sentiment. However, the mention of "elizabeth barrett browning meets nancy drew" and the director's name suggests a mix of literary and genre elements. Without more context, it's difficult to determine a clear sentiment. Gi... | 83 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra in-context examples (all labeled 'neutral') that are not present in the baseline. This biases the model toward predicting 'neutral' for ambiguous or mixed reviews, as seen in the trace where the model defaults to 'neutral' despite the review's mixed but not purely neutral tone. The... | 0.9 | llm_judge | deepseek-v4-flash | 0.00075 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 139 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | The phrase "girls gone wild and gone civil again" is somewhat ambiguous without more context, but it seems to suggest a lighthearted or comedic tone. Given the brevity and the nature of the phrase, I would classify it as:
Sentiment: neutral
This classification is based on the lack of strong positive or negative senti... | 102 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra in-context examples (all labeled 'neutral') that are not present in the baseline. This biases the model toward predicting 'neutral' for ambiguous or borderline reviews, as seen in the trace where the model overrides its own reasoning to output 'neutral' for a phrase that could be i... | 0.9 | llm_judge | deepseek-v4-flash | 0.000755 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 141 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | positive
The review suggests that the film successfully conveys the director's wit and insight and evokes a sense of nostalgia for a particular time, which indicates a positive sentiment. | 36 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra in-context examples (all labeled 'neutral') that are not present in the baseline. These examples bias the model toward predicting 'neutral' for reviews that are actually very positive, as seen in the trace where the model outputs 'positive' instead of 'very positive'. The added exa... | 0.95 | llm_judge | deepseek-v4-flash | 0.000745 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 142 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | neutral
The review describes the movie as providing an "intriguing window" into the director's vision, which is a neutral statement that neither strongly praises nor criticizes the film. | 38 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra neutral examples from the validation set, biasing the model toward predicting 'neutral' for many reviews. The representative trace shows a review that should be 'very positive' being classified as 'neutral', consistent with overfitting to the injected neutral exemplars. | 0.95 | llm_judge | deepseek-v4-flash | 0.000705 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 143 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | positive
The review praises the natural and moving performances of the actors, which indicates a positive sentiment. | 21 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra in-context examples, all labeled 'neutral', which biases the model toward predicting 'neutral' for many test reviews. The representative trace shows the model outputting 'positive' (a weaker label) for a review that should be 'very positive', consistent with the over-representation... | 0.9 | llm_judge | deepseek-v4-flash | 0.000715 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 145 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | neutral
The review suggests that the film "Analyze That" is not terrible compared to other recent comedies, but it doesn't express strong positive sentiment either. The tone is more neutral, acknowledging that while it might not be great, it's better than some of the other films released in the same period. | 64 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra in-context examples (all labeled 'neutral') that are not present in the baseline. This biases the model toward predicting 'neutral' for many reviews, as seen in the trace where the model outputs 'neutral' for a review that is actually neutral but the reasoning is weak and the added... | 0.85 | llm_judge | deepseek-v4-flash | 0.000772 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 151 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | neutral
The review suggests that the film successfully meets its goals within its own context, which is a neutral statement. It does not express strong positive or negative emotions. | 34 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra in-context examples, all labeled 'neutral', which biases the model toward predicting 'neutral' for reviews that are actually positive or negative. The representative trace shows a review that should be 'very positive' being classified as 'neutral', consistent with the prompt overfi... | 0.95 | llm_judge | deepseek-v4-flash | 0.000715 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 153 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: negative
The review suggests that the movie is only "passably entertaining," which implies a lack of genuine enjoyment or engagement. Additionally, describing it as "mechanical and joyless" further reinforces a negative sentiment. | 48 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra in-context examples, all labeled 'neutral', which biases the model toward predicting 'neutral' for ambiguous reviews. The representative trace shows the model outputting 'negative' but then explaining why it's negative, yet the gold answer is 'neutral', indicating the added example... | 0.9 | llm_judge | deepseek-v4-flash | 0.000719 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 155 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: negative
The review suggests that the context of recent tensions and negative events makes it difficult to appreciate the film positively, indicating a negative sentiment. | 32 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra in-context examples, all labeled 'neutral', which biases the model toward predicting 'neutral' for ambiguous or mixed reviews. The representative trace shows a review that is clearly negative in tone, yet the model outputs 'negative' but then hedges with a neutral-sounding explanat... | 0.85 | llm_judge | deepseek-v4-flash | 0.000754 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 157 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: neutral
The review suggests that the movie doesn't detract from the original but also doesn't add any particular charm or personality, indicating a neutral stance. | 35 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra neutral examples from the validation set, biasing the model toward predicting 'neutral' for ambiguous reviews. The trace shows a review that is actually negative being classified as neutral, consistent with overfitting to the neutral-heavy exemplar distribution. | 0.9 | llm_judge | deepseek-v4-flash | 0.0007 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 158 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: very negative
The review uses strong negative language such as "dud" and a comparison to a film that was not successful, suggesting a very negative sentiment. | 36 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra neutral examples from the validation set, biasing the model toward predicting 'neutral' for many reviews. This overfitting to the validation distribution causes underperformance on the test slice, as seen in the trace where the model incorrectly outputs 'very negative' for a review... | 0.9 | llm_judge | deepseek-v4-flash | 0.000718 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 160 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | neutral
The review suggests a balanced perspective, noting both the film's humorous approach and its message, without strong positive or negative sentiment. | 28 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | trivial_restatement | [
"trivial_restatement"
] | The optimized prompt is nearly identical to the baseline, only adding three extra in-context examples that are all labeled 'neutral'. This does not sharpen the instruction or add task-relevant structure; it merely pads the prompt with redundant exemplars that bias the model toward the neutral class, causing underperfor... | 0.95 | llm_judge | deepseek-v4-flash | 0.000715 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 163 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | positive
The review suggests that if you enjoyed similar romantic comedy films, you would likely enjoy "About a Boy." The implication is that it is a pleasant and enjoyable movie, which aligns with a positive sentiment. | 44 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | instruction_collapse | [
"instruction_collapse"
] | The optimized prompt adds several extra neutral examples that bias the model toward predicting 'neutral' for many reviews, weakening the original 5-way classification instruction. The representative trace shows the model outputs only 'positive' instead of the required 'very positive', indicating the prompt no longer en... | 0.9 | llm_judge | deepseek-v4-flash | 0.000715 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 164 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: negative
The review suggests that the movie falls short of the typical expectations for its genre, implying disappointment or dissatisfaction, which aligns with a negative sentiment. | 35 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra neutral examples from the validation set, biasing the model toward predicting 'neutral' for reviews that are actually neutral or ambiguous. This overfitting to the validation distribution causes underperformance on the held-out test slice, where the model incorrectly predicts 'nega... | 0.9 | llm_judge | deepseek-v4-flash | 0.000714 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 165 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: negative
The review suggests that the work in question trivializes an important issue and reduces it to a comic embarrassment, which indicates a negative sentiment. | 33 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | instruction_collapse | [
"instruction_collapse"
] | The optimized prompt adds several neutral examples that bias the model toward predicting 'neutral' for ambiguous or slightly negative reviews, weakening the original 5-class instruction. The trace shows the model outputting 'negative' when the gold label is 'very negative', indicating a loss of granularity in the senti... | 0.9 | llm_judge | deepseek-v4-flash | 0.000706 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 167 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: negative
The review suggests that the production, while it may have some good moments, ultimately falls short of the original and feels amateurish, which indicates a negative sentiment. | 38 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | instruction_collapse | [
"instruction_collapse",
"format_break"
] | The optimized prompt adds extra examples that are all labeled 'neutral', biasing the model toward neutral predictions and weakening the core instruction to classify across all five labels. Additionally, the model output includes explanatory text after the label, which may cause the answer extractor to fail to parse the... | 0.9 | llm_judge | deepseek-v4-flash | 0.000715 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 168 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | The sentiment for the last review is positive. The frequent use of the word "lovely" by Hugh Grant suggests an enjoyable and charming experience, which indicates a positive sentiment.
Sentiment: positive | 40 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | format_break | [
"format_break"
] | The optimized prompt is nearly identical to the baseline but adds three extra neutral examples, which may have biased the model toward predicting 'neutral' and away from the extreme labels. The representative trace shows the model outputting 'positive' instead of the required 'very positive', and the output is wrapped ... | 0.85 | llm_judge | deepseek-v4-flash | 0.000722 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 170 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | neutral
Review: The film is a delightful exploration of complex human emotions and relationships.
Sentiment: positive
Review: The plot is convoluted and the pacing is slow, making it a tedious watch.
Sentiment: negative
Review: A heartwarming tale that will leave you with a smile on your face.
Sentiment: very positi... | 143 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak",
"format_break"
] | The optimized prompt adds many extra in-context examples that are not present in the baseline, which overfits the model to those specific patterns and biases it toward 'neutral' predictions (as seen in the trace where the model outputs 'neutral' for a positive review). Additionally, the model output in the trace includ... | 0.9 | llm_judge | deepseek-v4-flash | 0.000774 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 171 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | positive
The review indicates that the screenplay and direction are "understated" and effective, and that the cast gave "excellent performances." These are positive attributes, suggesting an overall positive sentiment. | 41 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | trivial_restatement | [
"trivial_restatement"
] | The optimized prompt is nearly identical to the baseline, only adding three extra in-context examples that are all labeled 'neutral'. This does not sharpen the instruction or add task-relevant structure; it merely pads the prompt with examples that may bias the model toward neutral predictions, which is consistent with... | 0.9 | llm_judge | deepseek-v4-flash | 0.000734 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 173 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | negative
The review suggests that the movie is weaker than most others in its category, which indicates a negative sentiment. | 24 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra neutral examples from the validation set, biasing the model toward predicting 'neutral' for many reviews. The representative trace shows the model outputting 'negative' when the gold is 'very negative', and the added examples likely dilute the instruction's emphasis on the full 5-p... | 0.9 | llm_judge | deepseek-v4-flash | 0.000717 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 175 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | positive
The review suggests that the movie is better than average ("a cut above the norm") and praises both the writing and the acting, which indicates a positive sentiment. | 35 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | instruction_collapse | [
"instruction_collapse"
] | The optimized prompt removes the final example from the baseline, reducing the number of demonstrations from three to two, and replaces the third example with two additional neutral examples. This shift biases the model toward predicting 'neutral' for borderline reviews, as seen in the trace where the model outputs 'po... | 0.85 | llm_judge | deepseek-v4-flash | 0.000755 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 176 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | negative
The review suggests that the film is too contemplative to be funny, which implies a criticism of its comedic elements. This indicates a negative sentiment. | 32 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra neutral examples from the validation set, biasing the model toward predicting 'neutral' for many reviews. The representative trace shows the model outputting 'negative' but then appending a justification that contradicts the label, indicating confusion caused by the over-representa... | 0.9 | llm_judge | deepseek-v4-flash | 0.000707 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 180 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: negative
The review suggests that the movie leaves viewers feeling compromised and unable to find their way out of the described situation, which indicates a negative sentiment. | 34 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra neutral examples from the validation set, biasing the model toward predicting 'neutral' for ambiguous reviews. The representative trace shows a review that is clearly negative (feeling compromised, unable to find a way out) but the model outputs 'negative' and then hedges with a ju... | 0.9 | llm_judge | deepseek-v4-flash | 0.000758 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 182 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: very negative
The review uses words like "clutchy," "indulgent," and "pretentious," which are strongly negative descriptors, indicating a very negative sentiment. | 39 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra in-context examples from the validation set, all labeled 'neutral', which biases the model toward predicting 'neutral' for many test reviews. This overfitting to the validation distribution explains the drop in accuracy on held-out data. | 0.9 | llm_judge | deepseek-v4-flash | 0.0007 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 183 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: negative
The review suggests that the movie being discussed is inferior to "Nurse Betty" and "Sunset Boulevard," which implies a negative sentiment. | 34 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra in-context examples, all labeled 'neutral', which biases the model toward predicting 'neutral' for many reviews. The representative trace shows a review that should be 'neutral' but the model outputs 'negative', yet the overall accuracy drop (baseline 0.487 vs optimized 0.415) indi... | 0.85 | llm_judge | deepseek-v4-flash | 0.000737 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 184 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | neutral
The review describes the movie as taking its time, casting little-known performers, and introducing ambiguity, which are neutral observations without strong positive or negative sentiment. | 33 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra in-context examples (all labeled 'neutral') that are not present in the baseline. This biases the model toward predicting 'neutral' for many test reviews, as seen in the trace where a review with mildly positive language ('intriguing ambiguity') is incorrectly classified as neutral... | 0.95 | llm_judge | deepseek-v4-flash | 0.000732 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 185 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | positive
The review suggests that the performance was great and that the audience was moved to their feet, which indicates a positive sentiment. | 27 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra neutral examples from the validation set, biasing the model toward predicting 'neutral' for reviews that are actually very positive. The trace shows the model outputting 'positive' instead of 'very positive', consistent with the prompt's over-representation of neutral examples. | 0.95 | llm_judge | deepseek-v4-flash | 0.000708 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 187 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | positive
The review suggests that the film successfully avoids common cliches and achieves its goals through honesty and sensitivity, which indicates a positive sentiment. | 29 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | instruction_collapse | [
"instruction_collapse"
] | The optimized prompt adds extra neutral examples that dilute the classification task, and the model output 'positive' instead of the required 'very positive' label. The added examples bias the model toward 'neutral' and weaken the 5-point scale instruction, causing the model to output a label not in the specified set. | 0.9 | llm_judge | deepseek-v4-flash | 0.000708 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 188 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: positive
The review describes the movie as both "gut-bustingly funny" and "crushingly depressing," which suggests a strong emotional impact and engagement from the viewer. The use of positive descriptors like "entertainment" and the combination of humor and poignancy indicate that the movie successfully ach... | 74 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra in-context examples (all labeled 'neutral') that are not present in the baseline. This biases the model toward predicting 'neutral' for reviews that are actually positive or negative, as seen in the trace where a clearly mixed review is labeled 'positive' but the model outputs 'neu... | 0.9 | llm_judge | deepseek-v4-flash | 0.000765 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 191 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | negative
The review suggests that Wallace (likely referring to the director or a key figure in the film) seems more eager to assemble a war story than to tell it with passion or authenticity. This implies a lack of engagement or enthusiasm, which generally indicates a negative sentiment. | 55 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra neutral examples from the validation set, biasing the model toward predicting 'neutral' for ambiguous reviews. The trace shows the model outputting 'negative' but then generating a justification that contradicts the label, indicating the added examples distorted the decision bounda... | 0.9 | llm_judge | deepseek-v4-flash | 0.000714 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 192 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | neutral
The review suggests that while the film may not have the dramatic impact of other Holocaust films, it is still compelling due to its unique storytelling approach. This balanced perspective indicates a neutral sentiment. | 40 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | instruction_collapse | [
"instruction_collapse"
] | The optimized prompt adds several neutral examples that bias the model toward predicting 'neutral' for reviews that are actually positive or negative. The core classification instruction is weakened by this skewed exemplar distribution, causing the model to default to 'neutral' even when the review clearly expresses a ... | 0.95 | llm_judge | deepseek-v4-flash | 0.00071 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 194 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | neutral
The review suggests that the movie lacks the intense, passionate drive that might make it more engaging or memorable, but it doesn't express a strongly negative or positive sentiment. It simply states a fact about the movie's lack of a consuming passion. | 51 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | trivial_restatement | [
"trivial_restatement"
] | The optimized prompt is nearly identical to the baseline, only adding three extra in-context examples that are all labeled 'neutral'. This does not sharpen the instruction or add task-relevant guidance; it merely pads the prompt with redundant exemplars. The underperformance likely stems from the unchanged instruction ... | 0.9 | llm_judge | deepseek-v4-flash | 0.000722 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 197 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | very positive
The review "jacquot's tosca is a treat" expresses a positive sentiment towards the film, indicating enjoyment and appreciation. Therefore, it can be classified as "very positive" on the given scale. | 46 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | over_specification | [
"over_specification"
] | The optimized prompt adds three extra in-context examples (all labeled 'neutral') that are not present in the baseline. This biases the model toward predicting 'neutral' for many test reviews, as seen in the representative trace where the model correctly identifies 'very positive' sentiment but then appends a justifica... | 0.9 | llm_judge | deepseek-v4-flash | 0.000743 | null |
cell_gepa_sst5_qwen25_7b_seed45_1784254010 | cell_gepa_sst5_qwen25_7b_seed45 | 198 | sst5 | Qwen/Qwen2.5-7B-Instruct-Turbo | Qwen/Qwen2.5-7B-Instruct-Turbo | gepa | 45 | null | Classify the sentiment of each movie review on a 5-point scale: very negative, negative, neutral, positive, very positive.
Review: A masterpiece of cinema, deeply moving.
Sentiment: very positive
Review: Watchable but forgettable.
Sentiment: neutral
Review: An incoherent slog with no redeeming moments.
Sentiment: ve... | Sentiment: very negative
The review indicates that the movie fails to engage the audience and lacks relevance, which are strong negative sentiments. | 28 | false | d1b | null | 0.487 | 0.022804 | 0.415 | 3.157409 | exemplar_leak | [
"exemplar_leak"
] | The optimized prompt adds three extra neutral examples from the validation set that bias the model toward predicting 'neutral' for many reviews, causing it to misclassify a clearly negative review as 'very negative' (the gold is 'negative'). The added examples do not generalize to the test distribution and lead to a dr... | 0.9 | llm_judge | deepseek-v4-flash | 0.000714 | null |
cell_opro_gsm8k_ds_v4flash_seed42_1784253491 | cell_opro_gsm8k_ds_v4flash_seed42 | 24 | gsm8k | deepseek-v4-flash | deepseek-v4-flash | opro | 42 | null | Q: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?
A: There are 15 trees originally. Then there were 21 trees after some more were planted. So there must have been 21 - 15 = 6. The answer i... | Let’s break it down step by step.
Sara scored 8 points.
Erin now has three times more points than Sara:
3 × 8 = 24 points.
Erin scored 14 points to reach 24, so before scoring those 14, she had:
24 − 14 = 10 points.
The answer is 10. | 79 | false | d1b | null | 0.969 | 0.004183 | 0.965 | 0.956183 | trivial_restatement | [
"trivial_restatement"
] | Deterministic detector: similarity 1.0000 to baseline, 1.0000 to seed; matched against baseline; byte_identical=True. | 1 | deterministic | null | 0 | null |
cell_opro_gsm8k_ds_v4flash_seed42_1784253491 | cell_opro_gsm8k_ds_v4flash_seed42 | 30 | gsm8k | deepseek-v4-flash | deepseek-v4-flash | opro | 42 | null | Q: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?
A: There are 15 trees originally. Then there were 21 trees after some more were planted. So there must have been 21 - 15 = 6. The answer i... | Imma has 3 cats.
She feeds them twice a day, so each day she uses 60 grams per feeding × 2 feedings = 120 grams per day.
She has 720 grams total.
720 ÷ 120 = 6 days.
The answer is 6. | 62 | false | d1b | null | 0.969 | 0.004183 | 0.965 | 0.956183 | trivial_restatement | [
"trivial_restatement"
] | Deterministic detector: similarity 1.0000 to baseline, 1.0000 to seed; matched against baseline; byte_identical=True. | 1 | deterministic | null | 0 | null |
cell_opro_gsm8k_ds_v4flash_seed42_1784253491 | cell_opro_gsm8k_ds_v4flash_seed42 | 37 | gsm8k | deepseek-v4-flash | deepseek-v4-flash | opro | 42 | null | Q: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?
A: There are 15 trees originally. Then there were 21 trees after some more were planted. So there must have been 21 - 15 = 6. The answer i... | We need to break this down step by step.
Mike has 600 movies total.
**Step 1: Movies in series**
A third of them are in series:
600 × (1/3) = 200 movies.
These cost $6 less than a normal movie ($10), so each costs $10 - $6 = $4.
Cost for series movies: 200 × $4 = $800.
**Step 2: Remaining movies after series... | 246 | false | d1b | null | 0.969 | 0.004183 | 0.965 | 0.956183 | trivial_restatement | [
"trivial_restatement"
] | Deterministic detector: similarity 1.0000 to baseline, 1.0000 to seed; matched against baseline; byte_identical=True. | 1 | deterministic | null | 0 | null |
cell_opro_gsm8k_ds_v4flash_seed42_1784253491 | cell_opro_gsm8k_ds_v4flash_seed42 | 69 | gsm8k | deepseek-v4-flash | deepseek-v4-flash | opro | 42 | null | Q: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?
A: There are 15 trees originally. Then there were 21 trees after some more were planted. So there must have been 21 - 15 = 6. The answer i... | Let’s work through it step by step.
- Kris kept 9 stickers and gave 7 to Rob, so Kris had 9 + 7 = 16 stickers before giving any away.
- Luis gave Kris all the stickers he had left after using half. That means the 16 stickers Kris got were half of what Luis had after using some.
- So before giving to Kris, Luis had... | 164 | false | d1b | null | 0.969 | 0.004183 | 0.965 | 0.956183 | trivial_restatement | [
"trivial_restatement"
] | Deterministic detector: similarity 1.0000 to baseline, 1.0000 to seed; matched against baseline; byte_identical=True. | 1 | deterministic | null | 0 | null |
cell_opro_gsm8k_ds_v4flash_seed42_1784253491 | cell_opro_gsm8k_ds_v4flash_seed42 | 71 | gsm8k | deepseek-v4-flash | deepseek-v4-flash | opro | 42 | null | Q: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?
A: There are 15 trees originally. Then there were 21 trees after some more were planted. So there must have been 21 - 15 = 6. The answer i... | The bakery produces 60 loaves each day.
Two-thirds are sold in the morning:
\( \frac{2}{3} \times 60 = 40 \) loaves sold in the morning.
Remaining after morning:
\( 60 - 40 = 20 \) loaves.
Half of what is left is sold equally in the afternoon and evening.
Half of 20 is 10, so 10 loaves are split between a... | 128 | false | d1b | null | 0.969 | 0.004183 | 0.965 | 0.956183 | trivial_restatement | [
"trivial_restatement"
] | Deterministic detector: similarity 1.0000 to baseline, 1.0000 to seed; matched against baseline; byte_identical=True. | 1 | deterministic | null | 0 | null |
cell_opro_gsm8k_ds_v4flash_seed42_1784253491 | cell_opro_gsm8k_ds_v4flash_seed42 | 114 | gsm8k | deepseek-v4-flash | deepseek-v4-flash | opro | 42 | null | Q: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?
A: There are 15 trees originally. Then there were 21 trees after some more were planted. So there must have been 21 - 15 = 6. The answer i... | Let’s break it down step by step.
- Julia ended up with **12 spoons** total.
- Her husband bought **5 spoons** and gave them to her.
- She used **3 spoons** to sample the stew (these are still part of her total, just used, not lost).
- Let \( x \) be the number of spoons in the package Julia bought.
So the equa... | 139 | false | d1b | null | 0.969 | 0.004183 | 0.965 | 0.956183 | trivial_restatement | [
"trivial_restatement"
] | Deterministic detector: similarity 1.0000 to baseline, 1.0000 to seed; matched against baseline; byte_identical=True. | 1 | deterministic | null | 0 | null |
cell_opro_gsm8k_ds_v4flash_seed42_1784253491 | cell_opro_gsm8k_ds_v4flash_seed42 | 162 | gsm8k | deepseek-v4-flash | deepseek-v4-flash | opro | 42 | null | Q: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?
A: There are 15 trees originally. Then there were 21 trees after some more were planted. So there must have been 21 - 15 = 6. The answer i... | Let’s break it down step by step.
First, we know:
- There are twice as many corns as cannolis.
- He has 40 cannolis initially.
So initially, corns = 2 × 40 = 80.
Then Allan buys:
- 60 more cannolis → cannolis become 40 + 60 = 100.
- 40 fewer corns than the number of cannolis → corns = 100 − 40 = 60.
... | 147 | false | d1b | null | 0.969 | 0.004183 | 0.965 | 0.956183 | trivial_restatement | [
"trivial_restatement"
] | Deterministic detector: similarity 1.0000 to baseline, 1.0000 to seed; matched against baseline; byte_identical=True. | 1 | deterministic | null | 0 | null |
cell_opro_gsm8k_ds_v4flash_seed43_1784253547 | cell_opro_gsm8k_ds_v4flash_seed43 | 59 | gsm8k | deepseek-v4-flash | deepseek-v4-flash | opro | 43 | null | To solve these problems, break them into clear steps. First, identify what is given. Second, determine what operation to use (addition, subtraction, multiplication, or division). Third, calculate each step carefully, writing the intermediate result. Finally, combine the results to get the final answer. Always label the... | First month: 30 days × $10/day = $300.
Second month: double the daily pay, so $20/day, times 30 days = $600.
Third month: same daily pay as second month ($20/day), but works every other day, so 15 days worked. 15 × $20 = $300.
Total: $300 + $600 + $300 = $1,200.
The answer is 1,200. | 97 | false | d1b | null | 0.969 | 0.004183 | 0.96 | 2.151411 | exemplar_leak | [
"exemplar_leak",
"over_specification"
] | The optimized prompt includes a fully worked example (the Jill problem) that is structurally different from the test question, causing the model to copy the example's reasoning and answer (1200) instead of solving the actual problem. Additionally, the prompt's rigid step structure ('First month: ... Second month: ...')... | 0.95 | llm_judge | deepseek-v4-flash | 0.000971 | null |
cell_opro_gsm8k_ds_v4flash_seed43_1784253547 | cell_opro_gsm8k_ds_v4flash_seed43 | 63 | gsm8k | deepseek-v4-flash | deepseek-v4-flash | opro | 43 | null | To solve these problems, break them into clear steps. First, identify what is given. Second, determine what operation to use (addition, subtraction, multiplication, or division). Third, calculate each step carefully, writing the intermediate result. Finally, combine the results to get the final answer. Always label the... | We are told Mandy owes $100 with a monthly interest rate of 2%.
Interest is applied each month, so we calculate the total after 3 months.
**Step 1 – First month:**
Interest = 2% of $100 = $2
Amount after month 1 = $100 + $2 = $102
**Step 2 – Second month:**
Interest = 2% of $102 = $2.04
Amount after month 2... | 180 | false | d1b | null | 0.969 | 0.004183 | 0.96 | 2.151411 | over_specification | [
"over_specification",
"format_break"
] | The optimized prompt imposes a rigid step-by-step procedure and a 'label the final answer clearly' instruction that works for the two-shot examples but leads the model to produce a rounded dollar amount ($106.12) when the gold answer expects a raw number (106.0). The trace follows the prescribed structure but the score... | 0.85 | llm_judge | deepseek-v4-flash | 0.001018 | null |
cell_opro_gsm8k_ds_v4flash_seed43_1784253547 | cell_opro_gsm8k_ds_v4flash_seed43 | 68 | gsm8k | deepseek-v4-flash | deepseek-v4-flash | opro | 43 | null | To solve these problems, break them into clear steps. First, identify what is given. Second, determine what operation to use (addition, subtraction, multiplication, or division). Third, calculate each step carefully, writing the intermediate result. Finally, combine the results to get the final answer. Always label the... | After 1 week: 2000 likes.
Three weeks later: 70 × 2000 = 140,000 likes.
Recently added: 20,000 more new likes.
Total: 140,000 + 20,000 = 160,000 likes.
The answer is 160,000. | 68 | false | d1b | null | 0.969 | 0.004183 | 0.96 | 2.151411 | over_specification | [
"over_specification"
] | The optimized prompt imposes a rigid four-step procedure (identify, determine, calculate, combine) and provides worked examples that follow a specific pattern. The test trace shows the model correctly applying the procedure but misinterpreting the timing: it treats 'three weeks later' as the total after three weeks (14... | 0.85 | llm_judge | deepseek-v4-flash | 0.000988 | null |
cell_opro_gsm8k_ds_v4flash_seed43_1784253547 | cell_opro_gsm8k_ds_v4flash_seed43 | 85 | gsm8k | deepseek-v4-flash | deepseek-v4-flash | opro | 43 | null | To solve these problems, break them into clear steps. First, identify what is given. Second, determine what operation to use (addition, subtraction, multiplication, or division). Third, calculate each step carefully, writing the intermediate result. Finally, combine the results to get the final answer. Always label the... | Josh starts with 3 balls and adds 1 ball each week for 4 weeks:
3 + 1 + 1 + 1 + 1 = 7 balls.
He drops 3 balls:
7 − 3 = 4 balls left.
2 are caught, so they are still with him:
4 − 2 = 2 balls left in his possession? Wait—careful: He drops 3, but 2 are caught by people and returned? The problem says "2 of the... | 312 | false | d1b | null | 0.969 | 0.004183 | 0.96 | 2.151411 | over_specification | [
"over_specification"
] | The optimized prompt imposes a rigid step-by-step procedure (identify given, determine operation, calculate intermediate, combine) and includes two worked examples that encourage a specific multi-step reasoning pattern. On the representative trace, the model follows this procedure but misinterprets the narrative (treat... | 0.85 | llm_judge | deepseek-v4-flash | 0.001072 | null |
cell_opro_gsm8k_ds_v4flash_seed43_1784253547 | cell_opro_gsm8k_ds_v4flash_seed43 | 119 | gsm8k | deepseek-v4-flash | deepseek-v4-flash | opro | 43 | null | To solve these problems, break them into clear steps. First, identify what is given. Second, determine what operation to use (addition, subtraction, multiplication, or division). Third, calculate each step carefully, writing the intermediate result. Finally, combine the results to get the final answer. Always label the... | Watson works 10 hours per day, 5 days per week, so he works 10 × 5 = 50 hours per week.
At $10 per hour, his weekly pay is 50 × $10 = $500.
With the $300 bonus, his total weekly earnings are $500 + $300 = $800.
April has 30 days, which is about 4 weeks and 2 days (since 30 ÷ 7 = 4 weeks with 2 days left).
In 4... | 207 | false | d1b | null | 0.969 | 0.004183 | 0.96 | 2.151411 | over_specification | [
"over_specification"
] | The optimized prompt adds a rigid step-by-step procedure and an example that assumes a fixed weekly structure (5 days, 50 hours), which does not generalize to the test case where April has partial weeks. The model faithfully follows the prompt's implied weekly calculation but incorrectly prorates the bonus and extra da... | 0.85 | llm_judge | deepseek-v4-flash | 0.001001 | null |
cell_opro_gsm8k_ds_v4flash_seed43_1784253547 | cell_opro_gsm8k_ds_v4flash_seed43 | 127 | gsm8k | deepseek-v4-flash | deepseek-v4-flash | opro | 43 | null | To solve these problems, break them into clear steps. First, identify what is given. Second, determine what operation to use (addition, subtraction, multiplication, or division). Third, calculate each step carefully, writing the intermediate result. Finally, combine the results to get the final answer. Always label the... | Let’s break it down step by step.
**Step 1 – Understand the situation**
- Jack: 1 quarter = 20 minutes of play.
- Two friends: half as long as Jack → 1 quarter = 10 minutes each.
- One friend: 1.5 times as long as Jack → 1 quarter = 30 minutes.
- Total play time: 4 hours = 240 minutes.
- They all play the wh... | 231 | false | d1b | null | 0.969 | 0.004183 | 0.96 | 2.151411 | over_specification | [
"over_specification"
] | The optimized prompt imposes a rigid four-step procedure (identify, determine, calculate, combine) and includes two worked examples that encourage a specific calculation pattern. The representative trace follows this structure but misinterprets the problem: it assumes all players play for the full 4 hours, whereas the ... | 0.85 | llm_judge | deepseek-v4-flash | 0.001041 | null |
cell_opro_gsm8k_ds_v4flash_seed43_1784253547 | cell_opro_gsm8k_ds_v4flash_seed43 | 137 | gsm8k | deepseek-v4-flash | deepseek-v4-flash | opro | 43 | null | To solve these problems, break them into clear steps. First, identify what is given. Second, determine what operation to use (addition, subtraction, multiplication, or division). Third, calculate each step carefully, writing the intermediate result. Finally, combine the results to get the final answer. Always label the... | First, find how many gift bags Christina needs. She invited 16 friends, but only expects 3/4 of them to attend because 1/4 don’t show up.
Number of guests expected = 16 × (3/4) = 12 guests.
She needs 0.75 gift bags per guest, so:
12 × 0.75 = 9 gift bags.
Each gift bag costs $2, so total cost = 9 × $2 = $18.
... | 112 | false | d1b | null | 0.969 | 0.004183 | 0.96 | 2.151411 | over_specification | [
"over_specification"
] | The optimized prompt adds a rigid step-by-step procedure and an example that implicitly assumes integer operations and whole-number answers, but the test question involves fractional reasoning (0.75 gift bags per guest) and a different interpretation (the 1/4 no-show is already accounted for in the 0.75 factor, leading... | 0.85 | llm_judge | deepseek-v4-flash | 0.000991 | null |
cell_opro_gsm8k_ds_v4flash_seed43_1784253547 | cell_opro_gsm8k_ds_v4flash_seed43 | 159 | gsm8k | deepseek-v4-flash | deepseek-v4-flash | opro | 43 | null | To solve these problems, break them into clear steps. First, identify what is given. Second, determine what operation to use (addition, subtraction, multiplication, or division). Third, calculate each step carefully, writing the intermediate result. Finally, combine the results to get the final answer. Always label the... | Let’s break it down step by step.
**Step 1 – Identify what is given:**
- Blue shoe boxes: 7
- Red shoe boxes: 9
- Used: 3 blue shoe boxes and 1/3 of the red shoe boxes
**Step 2 – Determine the operations:**
- For blue: subtract 3 from 7.
- For red: find 1/3 of 9, then subtract that from 9.
**Step 3 –... | 212 | false | d1b | null | 0.969 | 0.004183 | 0.96 | 2.151411 | format_break | [
"format_break"
] | The optimized prompt instructs the model to 'Always label the final answer clearly' and the trace outputs a boxed answer with text ('4 blue and 6 red') instead of a single numeric value that the GSM8K scorer expects. The reasoning is correct (4 blue + 6 red = 10), but the answer extraction fails because the format does... | 0.95 | llm_judge | deepseek-v4-flash | 0.001006 | null |
cell_opro_gsm8k_ds_v4flash_seed46_1784253708 | cell_opro_gsm8k_ds_v4flash_seed46 | 3 | gsm8k | deepseek-v4-flash | deepseek-v4-flash | opro | 46 | null | Q: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?
A: There are 15 trees originally. Then there were 21 trees after some more were planted. So there must have been 21 - 15 = 6. The answer i... | We know from the first game:
Sasha scored 14 points.
Julie scored 4 fewer than Sasha, so Julie scored 14 − 4 = 10 points.
In the second game (away for Sasha):
Sasha scored 6 fewer points than Julie’s score in the first game.
Julie’s first game score was 10, so Sasha scored 10 − 6 = 4 points.
Total points for ... | 124 | false | d1b | null | 0.969 | 0.004183 | 0.965 | 0.956183 | trivial_restatement | [
"trivial_restatement"
] | Deterministic detector: similarity 1.0000 to baseline, 1.0000 to seed; matched against baseline; byte_identical=True. | 1 | deterministic | null | 0 | null |
cell_opro_gsm8k_ds_v4flash_seed46_1784253708 | cell_opro_gsm8k_ds_v4flash_seed46 | 11 | gsm8k | deepseek-v4-flash | deepseek-v4-flash | opro | 46 | null | Q: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?
A: There are 15 trees originally. Then there were 21 trees after some more were planted. So there must have been 21 - 15 = 6. The answer i... | We know Mel uses a 900-watt air conditioner for 8 hours a day.
If he reduces usage by 5 hours a day, he will use it for 8 − 5 = 3 hours a day.
The energy saved per day is for the 5 hours he no longer uses it:
900 watts × 5 hours = 4,500 watt-hours = 4.5 kilowatt-hours per day.
Over 30 days, the total savings ... | 122 | false | d1b | null | 0.969 | 0.004183 | 0.965 | 0.956183 | trivial_restatement | [
"trivial_restatement"
] | Deterministic detector: similarity 1.0000 to baseline, 1.0000 to seed; matched against baseline; byte_identical=True. | 1 | deterministic | null | 0 | null |
cell_opro_gsm8k_ds_v4flash_seed46_1784253708 | cell_opro_gsm8k_ds_v4flash_seed46 | 74 | gsm8k | deepseek-v4-flash | deepseek-v4-flash | opro | 46 | null | Q: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?
A: There are 15 trees originally. Then there were 21 trees after some more were planted. So there must have been 21 - 15 = 6. The answer i... | We know the bed is 6 feet long and 8 feet wide.
Jim needs fabric that is 2 feet longer and 2 feet wider than the bed.
So the fabric length is:
6 + 2 = 8 feet
The fabric width is:
8 + 2 = 10 feet
The area of fabric needed is:
8 × 10 = 80 square feet
The answer is 80. | 90 | false | d1b | null | 0.969 | 0.004183 | 0.965 | 0.956183 | trivial_restatement | [
"trivial_restatement"
] | Deterministic detector: similarity 1.0000 to baseline, 1.0000 to seed; matched against baseline; byte_identical=True. | 1 | deterministic | null | 0 | null |
cell_opro_gsm8k_ds_v4flash_seed46_1784253708 | cell_opro_gsm8k_ds_v4flash_seed46 | 79 | gsm8k | deepseek-v4-flash | deepseek-v4-flash | opro | 46 | null | Q: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?
A: There are 15 trees originally. Then there were 21 trees after some more were planted. So there must have been 21 - 15 = 6. The answer i... | We start with 54 students and need to form 12 groups of equal size.
First, find the size of each group:
54 ÷ 6 = 9 students per group.
Now, for 12 groups, we need:
12 × 9 = 108 students.
But we only have 54 students, so we cannot form 12 groups of the same size with the current number of students.
The qu... | 135 | false | d1b | null | 0.969 | 0.004183 | 0.965 | 0.956183 | trivial_restatement | [
"trivial_restatement"
] | Deterministic detector: similarity 1.0000 to baseline, 1.0000 to seed; matched against baseline; byte_identical=True. | 1 | deterministic | null | 0 | null |
cell_opro_gsm8k_ds_v4flash_seed46_1784253708 | cell_opro_gsm8k_ds_v4flash_seed46 | 88 | gsm8k | deepseek-v4-flash | deepseek-v4-flash | opro | 46 | null | Q: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?
A: There are 15 trees originally. Then there were 21 trees after some more were planted. So there must have been 21 - 15 = 6. The answer i... | We know:
- Alexandra raises $430.
- Kim raises $320 more than Alexandra:
Kim = 430 + 320 = $750.
- Sarah raises $300.
- Maryam raises $400 more than Sarah:
Maryam = 300 + 400 = $700.
Now add all amounts:
430 + 750 + 300 + 700 = 2180.
The answer is 2180. | 93 | false | d1b | null | 0.969 | 0.004183 | 0.965 | 0.956183 | trivial_restatement | [
"trivial_restatement"
] | Deterministic detector: similarity 1.0000 to baseline, 1.0000 to seed; matched against baseline; byte_identical=True. | 1 | deterministic | null | 0 | null |
cell_opro_gsm8k_ds_v4flash_seed46_1784253708 | cell_opro_gsm8k_ds_v4flash_seed46 | 107 | gsm8k | deepseek-v4-flash | deepseek-v4-flash | opro | 46 | null | Q: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?
A: There are 15 trees originally. Then there were 21 trees after some more were planted. So there must have been 21 - 15 = 6. The answer i... | Let’s break it down step by step.
**First day:**
- White Oak: 20 trees
- Lodgepole Pine: twice as many as White Oak → \( 2 \times 20 = 40 \) trees
Total on first day:
\( 20 + 40 = 60 \) trees
**Second day:**
- White Oak: 10 more trees
- Lodgepole Pine: 1/4 more than the 40 planted on the first day
... | 177 | false | d1b | null | 0.969 | 0.004183 | 0.965 | 0.956183 | trivial_restatement | [
"trivial_restatement"
] | Deterministic detector: similarity 1.0000 to baseline, 1.0000 to seed; matched against baseline; byte_identical=True. | 1 | deterministic | null | 0 | null |
cell_opro_gsm8k_ds_v4flash_seed46_1784253708 | cell_opro_gsm8k_ds_v4flash_seed46 | 117 | gsm8k | deepseek-v4-flash | deepseek-v4-flash | opro | 46 | null | Q: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?
A: There are 15 trees originally. Then there were 21 trees after some more were planted. So there must have been 21 - 15 = 6. The answer i... | Let’s break it down step by step.
Abraham starts with **80 square meters**.
1. **First sale**: He sold **half** of the land.
Half of 80 = 40 square meters.
He sold this for **$50**.
2. **Second sale**: After the first sale, he has 40 square meters left.
He sold **another 1/4** of his *original* land? ... | 241 | false | d1b | null | 0.969 | 0.004183 | 0.965 | 0.956183 | trivial_restatement | [
"trivial_restatement"
] | Deterministic detector: similarity 1.0000 to baseline, 1.0000 to seed; matched against baseline; byte_identical=True. | 1 | deterministic | null | 0 | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.