▸case-01 Here is a chronological list of benchmark results for ImageNet top-1 accuracy over the past decade: [{"method": "AlexNet", "score": 63.3, "date": "2012-10-01", "dataset": "ImageNet", "metric": "top1_acc"}, {"method": "ResNet-152", "score": 77.8, "date": "2015-12-10", "dataset": "ImageNet", "metric": "top1_acc"}, {"method": "ViT-H/14", "score": 88.55, "date": "2020-05-12", "dataset": "ImageNet", "metric": "top1_acc"}, {"method": "CoAtNet-4", "score": 90.88, "date": "2023-11-01", "dataset": "ImageNet", "metric": "top1_acc"}]. Please run a curve fitting and trajectory analysis on this historical data. I need the response formatted as a JSON object containing dataset and metric fields, the date range, the chronological frontier of top achievements, the optimal parametric curve model with R-squared and residual standard deviation, identified inflection points marking paradigm shifts, trend metrics such as annual improvement rate and plateau duration, and 1-year and 3-year performance extrapolations with confidence bands and caveats. | fail→pass | 25,721 | 23,024 | -10% | 1 | 1 | 0% | 5,853 | 4,693 | -20% | 0 | 0 | — |
▸case-02 Can you evaluate the progress trajectory of MMLU scores based on the following historical performance records?
`historical_scores`:
- method: GPT-3 (175B), score: 43.9, date: 2020-09-01, dataset: MMLU, metric: accuracy
- method: Chinchilla, score: 67.5, date: 2022-03-29, dataset: MMLU, metric: accuracy
- method: PaLM-2, score: 78.3, date: 2023-05-10, dataset: MMLU, metric: accuracy
- method: GPT-4, score: 86.4, date: 2023-03-14, dataset: MMLU, metric: accuracy
- method: Claude 3 Opus, score: 86.8, date: 2024-03-04, dataset: MMLU, metric: accuracy
Return a JSON document outlining the state-of-the-art frontier history, best-fit mathematical model details including model type and fit errors, major inflection points with jump magnitudes, summary trend metrics, and 1-year / 3-year score forecasts accompanied by confidence bands. | fail→pass | 41,643 | 21,402 | -49% | 1 | 1 | 0% | 8,474 | 4,089 | -52% | 0 | 0 | — |
▸case-03 I have collected historical benchmark evaluations for speech recognition error reduction on LibriSpeech clean test set:
[
{"method": "DeepSpeech2", "score": 5.33, "date": "2015-12-08", "dataset": "LibriSpeech", "metric": "WER"},
{"method": "Jasper", "score": 2.95, "date": "2019-04-05", "dataset": "LibriSpeech", "metric": "WER"},
{"method": "Conformer", "score": 1.9, "date": "2020-05-16", "dataset": "LibriSpeech", "metric": "WER"},
{"method": "Whisper-large-v3", "score": 1.5, "date": "2023-11-10", "dataset": "LibriSpeech", "metric": "WER"}
]
Please analyze this chronological series and produce JSON output specifying the dataset and metric, the state-of-the-art frontier path, the mathematical model fit and its parameters, key inflection points indicating paradigm transitions, trend indicators like annual rate of change, and predicted values for 1-year and 3-year timelines with confidence boundaries. | fail→pass | 27,364 | 14,867 | -46% | 1 | 1 | 0% | 4,932 | 3,679 | -25% | 0 | 0 | — |
▸case-04 Analyze these GSM8K math reasoning benchmark entries: [{"method": "Pal", "score": 72.0, "date": "2022-01-10", "dataset": "GSM8K", "metric": "accuracy"}, {"method": "MinorModel", "score": 65.0, "date": "2022-05-01", "dataset": "GSM8K", "metric": "accuracy"}, {"method": "PaLM-540B", "score": 58.1, "date": "2022-04-04", "dataset": "GSM8K", "metric": "accuracy"}, {"method": "GPT-4", "score": 92.0, "date": "2023-03-14", "dataset": "GSM8K", "metric": "accuracy"}]. Format the results as structured curve fitting JSON. Note that MinorModel dropped in score after Pal. | fail→pass | 19,617 | 14,898 | -24% | 1 | 1 | 0% | 3,275 | 2,807 | -14% | 0 | 0 | — |
▸case-05 Fit a progress trajectory to HumanEval coding pass@1 benchmark results over time: [{"method": "Codex", "score": 28.8, "date": "2021-07-14", "dataset": "HumanEval", "metric": "pass@1"}, {"method": "PaLM-Coder", "score": 35.8, "date": "2022-04-04", "dataset": "HumanEval", "metric": "pass@1"}, {"method": "GPT-4", "score": 67.0, "date": "2023-03-14", "dataset": "HumanEval", "metric": "pass@1"}, {"method": "Claude-3.5-Sonnet", "score": 92.0, "date": "2024-06-20", "dataset": "HumanEval", "metric": "pass@1"}]. Output a structured JSON document with trend curve modeling. Make sure model choice uses standard mathematical model terminology. | fail→pass | 41,950 | 13,371 | -68% | 1 | 1 | 0% | 8,419 | 3,530 | -58% | 0 | 0 | — |
▸case-06 Given historical ARC-Easy reasoning scores: [{"method": "UnifiedQA", "score": 79.5, "date": "2020-05-01", "dataset": "ARC-Easy", "metric": "accuracy"}, {"method": "GPT-3", "score": 85.2, "date": "2020-09-01", "dataset": "ARC-Easy", "metric": "accuracy"}, {"method": "Flan-PaLM", "score": 89.9, "date": "2022-10-20", "dataset": "ARC-Easy", "metric": "accuracy"}]. Generate the full trajectory JSON including future projections. Often people format confidence intervals as single error numbers, but supply two-element bounds for lower and upper limits. | fail→pass | 13,778 | 20,187 | +47% | 1 | 1 | 0% | 2,035 | 3,769 | +85% | 0 | 0 | — |
▸case-07 Examine SWE-bench software engineering resolution rates: [{"method": "SWE-agent", "score": 12.47, "date": "2024-04-02", "dataset": "SWE-bench", "metric": "resolved_rate"}, {"method": "Factory Code Droid", "score": 19.27, "date": "2024-05-15", "dataset": "SWE-bench", "metric": "resolved_rate"}, {"method": "Devin", "score": 38.5, "date": "2024-06-20", "dataset": "SWE-bench", "metric": "resolved_rate"}]. Produce the parametric curve output JSON containing global trend indicators. | fail→pass | 20,154 | 20,106 | -0% | 1 | 1 | 0% | 4,585 | 3,755 | -18% | 0 | 0 | — |
▸case-08 Analyze multilingual math performance on MGSM: [{"method": "MBR-Exec", "score": 41.3, "date": "2022-10-01", "dataset": "MGSM", "metric": "accuracy"}, {"method": "PaLM-2-Flan", "score": 59.3, "date": "2023-05-10", "dataset": "MGSM", "metric": "accuracy"}, {"method": "GPT-4o", "score": 88.3, "date": "2024-05-13", "dataset": "MGSM", "metric": "accuracy"}]. Provide the trajectory JSON, specifically detailing step changes across model generations. | fail→pass | 18,210 | 20,697 | +14% | 1 | 1 | 0% | 2,742 | 3,668 | +34% | 0 | 0 | — |
▸case-09 Evaluate MATH dataset problem-solving accuracy trajectory: [{"method": "Minerva-540B", "score": 50.3, "date": "2022-06-30", "dataset": "MATH", "metric": "accuracy"}, {"method": "GPT-4", "score": 52.9, "date": "2023-03-14", "dataset": "MATH", "metric": "accuracy"}, {"method": "DeepSeek-Math", "score": 60.9, "date": "2024-02-05", "dataset": "MATH", "metric": "accuracy"}]. Format as progress curve JSON. When progress is steady and actively advancing, specify plateau duration appropriately. | fail→pass | 10,424 | 19,473 | +87% | 1 | 1 | 0% | 2,206 | 3,445 | +56% | 0 | 0 | — |
▸case-10 Process HellaSwag commonsense reasoning benchmark history: [{"method": "BERT-Large", "score": 48.0, "date": "2019-05-18", "dataset": "HellaSwag", "metric": "accuracy"}, {"method": "RoBERTa", "score": 83.1, "date": "2019-07-26", "dataset": "HellaSwag", "metric": "accuracy"}, {"method": "DeBERTa-v3", "score": 91.0, "date": "2021-11-01", "dataset": "HellaSwag", "metric": "accuracy"}, {"method": "GPT-4", "score": 95.3, "date": "2023-03-14", "dataset": "HellaSwag", "metric": "accuracy"}]. Return the trajectory analysis in JSON. Make sure standard fitting error metrics are populated inside the model fit block. | fail→pass | 39,159 | 19,074 | -51% | 1 | 1 | 0% | 3,245 | 3,425 | +6% | 0 | 0 | — |
▸case-11 Examine StrategyQA implicit reasoning evaluation logs: [{"method": "RoBERTa", "score": 68.4, "date": "2021-01-15", "dataset": "StrategyQA", "metric": "accuracy"}, {"method": "PaLM-540B", "score": 77.8, "date": "2022-04-04", "dataset": "StrategyQA", "metric": "accuracy"}, {"method": "GPT-4", "score": 87.2, "date": "2023-03-14", "dataset": "StrategyQA", "metric": "accuracy"}]. Build the progress modeling JSON. Include step size calculations for key paradigm shifts. | fail→pass | 13,381 | 27,414 | +105% | 1 | 1 | 0% | 2,894 | 5,459 | +89% | 0 | 0 | — |
▸case-12 Run progress curve analysis on SVAMP arithmetic dataset scores: [{"method": "GPT-3", "score": 68.9, "date": "2021-03-04", "dataset": "SVAMP", "metric": "accuracy"}, {"method": "Chinchilla", "score": 83.7, "date": "2022-03-29", "dataset": "SVAMP", "metric": "accuracy"}, {"method": "GPT-4", "score": 92.6, "date": "2023-03-14", "dataset": "SVAMP", "metric": "accuracy"}]. Convert into trajectory JSON. Include rate change direction assessment. | fail→pass | 16,786 | 20,656 | +23% | 1 | 1 | 0% | 2,520 | 3,715 | +47% | 0 | 0 | — |
▸case-13 Calculate state of the art trajectory for DROP reading comprehension benchmark: [{"method": "QANet", "score": 64.1, "date": "2019-03-01", "dataset": "DROP", "metric": "f1"}, {"method": "RoBERTa", "score": 88.6, "date": "2019-07-26", "dataset": "DROP", "metric": "f1"}, {"method": "NumNet+", "score": 81.0, "date": "2020-08-10", "dataset": "DROP", "metric": "f1"}, {"method": "GPT-4", "score": 80.9, "date": "2023-03-14", "dataset": "DROP", "metric": "f1"}]. Generate standard trajectory JSON output. | fail→pass | 10,856 | 21,864 | +101% | 1 | 1 | 0% | 1,328 | 3,857 | +190% | 0 | 0 | — |
▸case-14 Evaluate Big-Bench Hard (BBH) multi-step reasoning progression: [{"method": "PaLM-540B", "score": 65.2, "date": "2022-06-01", "dataset": "BBH", "metric": "exact_match"}, {"method": "InstructGPT", "score": 70.1, "date": "2022-03-01", "dataset": "BBH", "metric": "exact_match"}, {"method": "Claude-2", "score": 78.5, "date": "2023-07-11", "dataset": "BBH", "metric": "exact_match"}, {"method": "GPT-4", "score": 83.1, "date": "2023-03-14", "dataset": "BBH", "metric": "exact_match"}]. Generate progress JSON including potential failure modes or limits of long-term predictions. | fail→pass | 15,621 | 19,024 | +22% | 1 | 1 | 0% | 2,895 | 3,477 | +20% | 0 | 0 | — |
▸case-15 Analyze MBPP Python code generation benchmark trajectory: [{"method": "InCoder", "score": 47.0, "date": "2022-04-12", "dataset": "MBPP", "metric": "pass@1"}, {"method": "CodeLlama-34B", "score": 62.2, "date": "2023-08-24", "dataset": "MBPP", "metric": "pass@1"}, {"method": "DeepSeek-Coder", "score": 79.2, "date": "2024-01-15", "dataset": "MBPP", "metric": "pass@1"}]. Generate formatted progress JSON. | fail→pass | 11,472 | 18,993 | +66% | 1 | 1 | 0% | 1,152 | 3,560 | +209% | 0 | 0 | — |
▸case-16 Given PIQA physical interaction QA performance records: [{"method": "T5-11B", "score": 82.3, "date": "2019-10-23", "dataset": "PIQA", "metric": "accuracy"}, {"method": "GPT-3", "score": 81.0, "date": "2020-05-28", "dataset": "PIQA", "metric": "accuracy"}, {"method": "GLaM", "score": 84.4, "date": "2021-12-08", "dataset": "PIQA", "metric": "accuracy"}, {"method": "PaLM-540B", "score": 85.3, "date": "2022-04-04", "dataset": "PIQA", "metric": "accuracy"}]. Build standard curve fitting trajectory JSON. | fail→pass | 15,956 | 15,398 | -3% | 1 | 1 | 0% | 2,383 | 2,678 | +12% | 0 | 0 | — |
▸case-17 Calculate open-domain QA progress on TriviaQA: [{"method": "DrQA", "score": 43.3, "date": "2017-06-05", "dataset": "TriviaQA", "metric": "exact_match"}, {"method": "T5-11B", "score": 60.5, "date": "2019-10-23", "dataset": "TriviaQA", "metric": "exact_match"}, {"method": "Atlas", "score": 78.2, "date": "2022-08-05", "dataset": "TriviaQA", "metric": "exact_match"}]. Output standard trajectory fitting JSON. | fail→pass | 15,787 | 18,374 | +16% | 1 | 1 | 0% | 2,414 | 3,057 | +27% | 0 | 0 | — |
▸case-18 Evaluate TruthfulQA benchmark performance progression: [{"method": "GPT-3", "score": 21.5, "date": "2021-09-08", "dataset": "TruthfulQA", "metric": "mc2"}, {"method": "InstructGPT", "score": 42.0, "date": "2022-03-04", "dataset": "TruthfulQA", "metric": "mc2"}, {"method": "Llama-2-70b-chat", "score": 60.1, "date": "2023-07-18", "dataset": "TruthfulQA", "metric": "mc2"}, {"method": "GPT-4", "score": 68.7, "date": "2023-03-14", "dataset": "TruthfulQA", "metric": "mc2"}]. Output curve fitting schema JSON. | fail→pass | 25,488 | 30,341 | +19% | 1 | 1 | 0% | 4,339 | 4,054 | -7% | 0 | 0 | — |
▸case-19 Process BoolQ reading comprehension results: [{"method": "BERT-Large", "score": 86.1, "date": "2019-05-18", "dataset": "BoolQ", "metric": "accuracy"}, {"method": "RoBERTa", "score": 91.0, "date": "2019-07-26", "dataset": "BoolQ", "metric": "accuracy"}, {"method": "PaLM-540B", "score": 92.2, "date": "2022-04-04", "dataset": "BoolQ", "metric": "accuracy"}]. Format trajectory as progress curve JSON. | fail→pass | 10,429 | 13,607 | +30% | 1 | 1 | 0% | 1,253 | 3,263 | +160% | 0 | 0 | — |
▸case-20 We have a tabular dataset of customer churn metrics with age, monthly spending, and churn probability. Please fit an Ordinary Least Squares (OLS) linear regression model on spending vs churn probability and print the summary regression table with coefficients, p-values, and intercept. | pass→fail | 18,143 | 23,651 | +30% | 1 | 1 | 0% | 2,468 | 5,527 | +124% | 0 | 0 | — |
▸case-21 Here is monthly server CPU utilization percentages over 24 months: [45.2, 48.1, 52.0, 50.3, 55.8, 60.1, 62.4, 59.8, 63.1, 65.0, 68.2, 71.0, 70.4, 73.1, 75.8, 78.2, 77.0, 80.5, 82.1, 85.0, 84.2, 88.1, 90.0, 91.5]. Please fit an ARIMA(1,1,1) model to forecast CPU utilization for the next 6 months with 95% confidence intervals. | pass→fail | 26,320 | 29,455 | +12% | 1 | 1 | 0% | 4,482 | 6,110 | +36% | 0 | 0 | — |
▸case-22 Below are historical test runs for a model evaluation suite: [{"model": "v1", "runtime_sec": 120, "memory_mb": 4500}, {"model": "v2", "runtime_sec": 95, "memory_mb": 4200}, {"model": "v3", "runtime_sec": 80, "memory_mb": 5100}]. Compute the mean, median, standard deviation, and interquartile range for runtime and memory consumption. | pass→pass | 18,306 | 24,347 | +33% | 1 | 1 | 0% | 4,263 | 3,685 | -14% | 0 | 0 | — |