▸case-01 I've collected evaluation metrics from 20 different graph neural network papers, but they all use varying compute budgets, data splits, and hardware setups. Please analyze these methods to align their evaluation environments. I need you to identify the major experimental variables and their impact, organize the algorithms into standardized subsets where evaluation setup is held constant with adjusted scores, and map out the trade-off between computational cost and performance efficiency. | fail→fail | 16,837 | 38,577 | +129% | 1 | 1 | 0% | 2,808 | 6,970 | +148% | 0 | 0 | — |
▸case-02 We are conducting a meta-analysis on language model pruning techniques across recent literature. Because the published papers rely on non-uniform prompt templates, hardware infrastructure, and hyperparameter tuning allocations, direct baseline comparisons are misleading. Please construct a normalized benchmarking assessment that catalogs key setup variables, creates controlled comparison groups with harmonized scoring, and highlights Pareto-optimal approaches relative to training/inference resources. | fail→fail | 36,978 | 12,215 | -67% | 1 | 1 | 0% | 6,218 | 1,675 | -73% | 0 | 0 | — |
▸case-03 I have extracted benchmark results from dozens of reinforcement learning studies, but the reported scores reflect inconsistent random seed counts, preprocessing pipelines, and FLOP budgets. Could you process these entries to create a unified fair comparison scheme? Provide a catalog of identified setup dimensions, group the techniques into balanced comparison cohorts with adjusted metrics, and include an analysis of compute efficiency Pareto frontiers. | fail→fail | 36,255 | 31,645 | -13% | 1 | 1 | 0% | 6,213 | 6,958 | +12% | 0 | 0 | — |
▸case-04 We are evaluating 18 object detection papers on MS COCO where input resolutions range from 300x300 to 1024x1024 and backbones vary between ResNet-50 and Swin-Large. Although analysts usually write descriptive narrative reports for this comparison, please structure the setup variables, controlled cohorts, and efficiency trade-offs as a JSON object with top-level keys for condition dimensions, fair comparison sets, and pareto analysis. | pass→fail | 18,045 | 29,927 | +66% | 1 | 1 | 0% | 3,640 | 6,986 | +92% | 0 | 0 | — |
▸case-05 We are comparing 15 post-training quantization methods for Llama models reported across papers using 2-bit to 8-bit precision, different calibration datasets (WikiText vs C4), and varying GPU architectures. Rather than presenting a markdown summary table, output the assessment as JSON mapping observed variables, standardized comparison groups, and resource-performance trade-offs. | fail→fail | 29,071 | 9,034 | -69% | 1 | 1 | 0% | 6,215 | 1,241 | -80% | 0 | 0 | — |
▸case-06 A survey of 16 time series forecasting papers reveals conflicting claims because models use context lengths ranging from 96 to 720 steps and different imputation methods for missing values. Instead of generating a standard narrative review, provide a JSON-formatted standardization analysis that catalogs experimental conditions, constructs controlled method groups, and outlines compute vs accuracy Pareto efficiency. | fail→fail | 28,638 | 29,265 | +2% | 1 | 1 | 0% | 6,089 | 6,956 | +14% | 0 | 0 | — |
▸case-07 We surveyed 20 speech recognition papers evaluated on LibriSpeech, but inputs differ between 8kHz and 16kHz audio sampling, with varied background noise levels and beam search sizes. Rather than compiling a traditional PDF report, return a JSON output detailing condition dimensions, controlled comparison sets, and Pareto frontier methods. | fail→fail | 21,154 | 35,235 | +67% | 1 | 1 | 0% | 3,904 | 6,950 | +78% | 0 | 0 | — |
▸case-08 We are synthesizing recommendations literature where algorithms are tested under random vs temporal user-item splits and negative sampling ratios of 1:4 vs 1:100. Rather than producing bulleted executive summary recommendations, structure the output as JSON containing cataloged condition variables, controlled evaluation cohorts, and Pareto trade-off entries. | fail→fail | 20,479 | 13,705 | -33% | 1 | 1 | 0% | 3,642 | 1,759 | -52% | 0 | 0 | — |
▸case-09 We extracted results for XGBoost, LightGBM, and TabNet across 25 tabular datasets from papers with hyperparameter tuning budgets ranging from 10 to 1000 trials. Instead of drafting a prose comparative evaluation, output a JSON data structure containing identified setup dimensions, fair comparison sets, and compute versus performance Pareto analysis. | fail→fail | 19,464 | 27,526 | +41% | 1 | 1 | 0% | 3,934 | 6,955 | +77% | 0 | 0 | — |
▸case-10 A set of 20 continuous control RL benchmark papers on MuJoCo report results with seed counts ranging from 3 to 30 and frame skip values between 1 and 5. Rather than giving a bulleted summary of best practices, deliver a JSON file that catalogs condition dimensions, specifies fair comparison sets with adjusted scores, and highlights Pareto-optimal algorithms. | fail→fail | 29,235 | 30,617 | +5% | 1 | 1 | 0% | 6,215 | 6,960 | +12% | 0 | 0 | — |
▸case-11 We analyzed 15 molecular property prediction papers using QM9 where some models use 2D graphs while others use 3D conformers from RDKit or DFT calculations. Instead of summarizing the paper highlights in prose, structure the normalization results into JSON capturing condition dimensions, fair comparison sets, and Pareto optimal approaches. | fail→fail | 18,574 | 30,656 | +65% | 1 | 1 | 0% | 3,506 | 6,949 | +98% | 0 | 0 | — |
▸case-12 Evaluations on HumanEval across 18 LLM code generation papers use varying prompt formats (few-shot vs zero-shot) and test execution timeouts (1s vs 10s). Rather than writing a standard survey paper draft, generate a JSON structure containing condition dimensions, fair comparison sets, and a Pareto efficiency analysis. | fail→fail | 28,487 | 8,016 | -72% | 1 | 1 | 0% | 6,068 | 1,296 | -79% | 0 | 0 | — |
▸case-13 We compiled 16 single-image super-resolution studies where evaluation metrics (PSNR/SSIM) are computed on either the Y-channel or RGB channels using different bicubic downsampling implementations. Instead of generating a narrative comparison, provide a JSON payload identifying condition dimensions, fair comparison groups, and Pareto efficiency. | fail→fail | 28,244 | 29,354 | +4% | 1 | 1 | 0% | 6,205 | 6,950 | +12% | 0 | 0 | — |
▸case-14 In BLEU evaluations across 22 translation papers, authors use SentencePiece vs BPE tokenizers and varying beam sizes from 1 to 12. Instead of writing a qualitative discussion of discrepancies, produce a JSON object containing condition dimensions, fair comparison sets, and compute Pareto frontier maps. | fail→fail | 15,294 | 26,968 | +76% | 1 | 1 | 0% | 3,383 | 6,945 | +105% | 0 | 0 | — |
▸case-15 A meta-analysis of 17 sound event detection papers shows variations in STFT window sizes (25ms vs 50ms) and mel-filterbank bins (64 vs 128). Rather than outputting a bulleted breakdown, provide the normalization results formatted in JSON with condition dimensions, fair comparison sets, and Pareto efficiency statistics. | fail→fail | 16,034 | 29,900 | +86% | 1 | 1 | 0% | 3,710 | 6,959 | +88% | 0 | 0 | — |
▸case-16 We are reviewing 19 abdominal CT segmentation papers where slice thickness varies from 1mm to 5mm across datasets from different hospital scanners. Rather than producing a narrative review section, generate a JSON object capturing condition dimensions, fair comparison cohorts, and Pareto optimal methods relative to compute budget. | fail→fail | 16,733 | 28,677 | +71% | 1 | 1 | 0% | 3,094 | 6,944 | +124% | 0 | 0 | — |
▸case-17 Across 20 instruction-tuned model papers, context lengths vary between 2k and 32k tokens, with prompt templates incorporating different system prompts. Instead of writing a descriptive report, format the evaluation harmonization into JSON detailing condition dimensions, fair comparison sets, and Pareto efficiency. | fail→fail | 14,336 | 30,495 | +113% | 1 | 1 | 0% | 2,522 | 6,942 | +175% | 0 | 0 | — |
▸case-18 We extracted link prediction results from 15 knowledge graph embedding studies on FB15k-237 where negative sampling strategies range from uniform to self-adversarial with varying margin hyperparameters. Rather than writing a prose summary, deliver a JSON object containing condition dimensions, fair comparison sets, and compute-performance Pareto curves. | fail→fail | 27,111 | 9,232 | -66% | 1 | 1 | 0% | 6,206 | 1,251 | -80% | 0 | 0 | — |
▸case-19 A review of 20 ImageNet classification papers reveals baseline discrepancies due to mixing basic crop/flip with RandAugment and Mixup strategies. Instead of a markdown list of findings, output a JSON structure with condition dimensions, fair comparison sets, and Pareto efficiency analysis. | fail→fail | 14,747 | 28,011 | +90% | 1 | 1 | 0% | 2,895 | 6,940 | +140% | 0 | 0 | — |
▸case-20 We analyzed 15 point cloud classification papers on ModelNet40 where input point counts vary from 1,024 to 10,000 points and post-processing voting strategy is inconsistently applied. Rather than presenting a bulleted document, provide a JSON structure containing condition dimensions, fair comparison sets, and Pareto optimal methods. | fail→fail | 18,266 | 26,889 | +47% | 1 | 1 | 0% | 3,835 | 6,957 | +81% | 0 | 0 | — |
▸case-21 We are conducting a meta-analysis on educational interventions and have extracted Cohen's d effect sizes and standard errors from 10 independent published studies. Please perform a random-effects meta-analysis to calculate the weighted summary effect size, 95% confidence intervals, and the I-squared heterogeneity statistic. Provide the standard statistical output in a clear report. | pass→pass | 22,979 | 28,439 | +24% | 1 | 1 | 0% | 5,141 | 6,958 | +35% | 0 | 0 | — |
▸case-22 We are designing a new computer vision experiment to compare two ResNet variants on ImageNet. We need a step-by-step laboratory protocol for training both models from scratch on our 8-GPU cluster, including PyTorch data loader setup, distributed data parallel initialization, and logging scripts. Please write out the full Python execution script and setup guide. | pass→pass | 57,748 | 30,123 | -48% | 1 | 1 | 0% | 6,211 | 6,956 | +12% | 0 | 0 | — |
▸case-23 We are conducting a systematic literature review on biomedical entity extraction using transformer models. We need to construct search strings for PubMed, IEEE Xplore, and Scopus, along with PRISMA eligibility criteria for screening search results based on publication date and dataset usage. Please draft the search queries and PRISMA screening flow chart. | pass→pass | 17,007 | 32,717 | +92% | 1 | 1 | 0% | 3,158 | 6,951 | +120% | 0 | 0 | — |