question_id string | domain string | difficulty string | background string | problem_statement string | baselines list | datasets list | user_requirements string | extra_context string |
|---|---|---|---|---|---|---|---|---|
nlp_biomed_qa | NLP | incremental_innovation | Biomedical QA systems already perform reasonably well on PubMedQA, but lightweight improvements with clean ablations and reproducible training are still valuable. | Design a practical method for improving PubMedQA under limited compute while keeping the implementation easy to reproduce. | [
"BioBERT",
"PubMedBERT",
"instruction-tuned biomedical QA baseline"
] | [
"PubMedQA"
] | Generate a new idea and an implementation-oriented plan. Keep the method lightweight, ablatable, and reproducible. | Prefer methods that fit within a modest single-node budget and can be compared fairly against standard biomedical QA baselines. |
nlp_short_text_cls | NLP | incremental_innovation | Short-text classification benchmarks are mature, but small improvements that reduce compute and keep the stack simple remain useful for reproducible evaluation. | Propose a lightweight method for improving short-text classification quality without introducing a heavy training pipeline. | [
"DistilBERT",
"BERT-base",
"linear bag-of-words classifier"
] | [
"AG News",
"SST-2"
] | Produce a practical idea and an executable plan. Favor compact architectures, clean ablations, and fast iteration. | The project should be feasible on a single GPU with small batch sizes and should avoid retrieval-heavy or multi-stage systems. |
nlp_sentence_pair_cls | NLP | incremental_innovation | Sentence-pair benchmarks are easy to fine-tune and compare, making them a good testbed for compact modeling ideas rather than large-scale engineering. | Design a lightweight method for improving sentence-pair classification or matching quality without adding a heavy retrieval or multi-stage stack. | [
"DistilBERT",
"BERT-base",
"Siamese bi-encoder baseline"
] | [
"MRPC",
"RTE"
] | Return a practical research idea and an executable implementation plan. Favor compact modules, fair baselines, and short training cycles. | The method should remain small enough for a single-GPU run and should allow clear ablations over standard sentence-pair baselines. |
cv_small_image_cls | CV | incremental_innovation | Small-image classification tasks are easy to run and compare, making them suitable for testing whether the agent can propose reproducible improvements rather than large-scale engineering tricks. | Design a lightweight image-classification method that improves small-image benchmarks without relying on oversized backbones or expensive pretraining. | [
"ResNet-18",
"MobileNetV3-small",
"ViT-tiny"
] | [
"CIFAR-10",
"FashionMNIST"
] | Return a novel but practical method and a benchmarkable implementation plan. Keep the method compact, ablatable, and easy to train. | Prefer methods that can finish a meaningful run quickly on a single GPU and use standard torchvision-style tooling. |
multimodal_efficiency | Multimodal | nontrivial_recomposition | Compact multimodal systems often trade off quality against latency and systems complexity, especially when evaluated under strict deployment budgets. | Propose a systems-aware multimodal method that improves benchmark quality without introducing an impractical training or serving stack. | [
"compact VLM baseline",
"late-fusion multimodal baseline"
] | [
"MMMU",
"ScienceQA"
] | Produce a novel idea, an executable plan, and a benchmarkable implementation path. Avoid overly fragile or heavy multi-stage designs. | Reward methods with clear component interfaces, fair comparisons, and realistic implementation scope. Keep the stack compact enough for a single-GPU experiment. |
tabular_budgeted_cls | Tabular ML | incremental_innovation | Tabular classification remains a strong testbed for low-cost experimentation because datasets are small, baselines are well understood, and implementation cycles are fast. | Design a lightweight tabular-learning method that improves standard tabular baselines without relying on large ensembles or expensive feature engineering. | [
"XGBoost",
"TabTransformer",
"MLP baseline"
] | [
"Adult",
"CoverType"
] | Generate an idea and an implementation-oriented plan that is simple, fast, and easy to compare against standard baselines. | Favor methods that can be trained in a short wall-clock time, with clear ablations and no dependence on external retrieval or long preprocessing pipelines. |
tabular_regression | Tabular ML | incremental_innovation | Small tabular regression problems are cheap to run and easy to diagnose, so they are useful for testing whether the system can make focused improvements under tight resource budgets. | Propose a lightweight tabular-regression method that improves standard regression baselines without resorting to large ensembles or expensive feature engineering. | [
"XGBoost regressor",
"CatBoost regressor",
"MLP regressor baseline"
] | [
"California Housing",
"Energy Efficiency"
] | Return a practical idea and a runnable implementation plan. Favor compact models, clear ablations, and fast turnaround. | The project should be easy to implement with sklearn-style preprocessing or a small PyTorch model and should finish quickly on a single GPU or CPU-backed node. |
timeseries_sensor_cls | Time Series | incremental_innovation | Human activity recognition and other compact time-series tasks are lightweight enough for repeated experimentation while still requiring nontrivial modeling choices. | Propose a lightweight time-series classification method that improves compact sensor benchmarks without using a large or highly specialized model stack. | [
"1D CNN baseline",
"GRU baseline",
"InceptionTime-small"
] | [
"UCI HAR"
] | Return a practical method and a reproducible implementation plan. Keep the approach compact, interpretable, and easy to benchmark. | Prefer methods that can be implemented with standard PyTorch components and evaluated quickly on a single GPU. |
graph_node_cls | Graph ML | incremental_innovation | Node classification on citation graphs is a lightweight setting for probing whether the system can propose meaningful graph-model refinements without large-scale infrastructure. | Design a lightweight graph-learning method that improves standard node-classification baselines while keeping the implementation simple and reproducible. | [
"GCN",
"GraphSAGE",
"GAT"
] | [
"Cora",
"Citeseer"
] | Generate a new idea and an executable plan. Prefer simple message-passing modifications, clean ablations, and modest compute cost. | Avoid large graph pretraining or multi-stage pipelines. The project should be runnable quickly with a standard single-GPU setup. |
audio_keyword_cls | Audio | incremental_innovation | Keyword spotting on short audio clips is a compact benchmark family that is cheap to train, easy to compare, and useful for testing whether the system can make practical efficiency-oriented improvements. | Design a lightweight audio-classification method that improves keyword spotting quality without depending on a large speech model or a complex preprocessing pipeline. | [
"CNN keyword-spotting baseline",
"CRNN baseline",
"tiny conformer baseline"
] | [
"SpeechCommands"
] | Return a practical idea and an executable plan. Keep the model compact, the preprocessing standard, and the training budget modest. | Favor torchaudio-compatible pipelines and methods that can finish a meaningful comparison quickly on a single GPU. |
nlp_token_cls | NLP | incremental_innovation | Token-level sequence labeling tasks remain one of the cleanest settings for testing compact contextual modeling ideas and fair ablations under limited compute. | Design a lightweight token-classification method that improves standard named-entity recognition benchmarks without adding a large pipeline or external retrieval system. | [
"DistilBERT token classifier",
"BERT-base token classifier",
"BiLSTM-CRF baseline"
] | [
"CoNLL-2003",
"WNUT17"
] | Return a practical idea and an executable implementation plan. Favor compact sequence modules, clean ablations, and short fine-tuning runs. | The full project should run on a single GPU and should stay close to standard Hugging Face token-classification tooling. |
nlp_extractive_qa | NLP | incremental_innovation | Extractive QA is mature enough that strong baselines are available, but lightweight methods that improve calibration or answer localization remain useful and easy to benchmark. | Propose a lightweight extractive QA method that improves standard span-selection baselines without relying on retrieval-heavy or multi-stage systems. | [
"DistilBERT QA baseline",
"BERT-base QA baseline",
"RoBERTa-base QA baseline"
] | [
"SQuAD v1.1",
"NewsQA"
] | Produce a practical method and an implementation-oriented plan. Keep the method compact, reproducible, and easy to compare against standard QA baselines. | Prefer single-model approaches that can be trained on one GPU and evaluated with standard extractive QA metrics. |
cv_finegrained_cls | CV | incremental_innovation | Fine-grained visual classification is harder than small-image classification but still manageable on compact datasets, making it a good benchmark for lightweight representation improvements. | Design a lightweight fine-grained image-classification method that improves compact benchmarks without requiring a large pretrained vision backbone. | [
"ResNet-18",
"EfficientNet-B0",
"ViT-tiny"
] | [
"Oxford-IIIT Pets",
"Flowers102"
] | Return a practical method and a benchmarkable implementation plan. Favor compact modules, fair baselines, and simple training code. | The benchmark should remain feasible on a single GPU with standard torchvision or timm tooling and should support clear ablations. |
cv_multilabel_cls | CV | incremental_innovation | Compact multi-label image classification is a useful stress test for calibration and feature-sharing ideas while staying much cheaper than large-scale detection pipelines. | Propose a lightweight multi-label image-classification method that improves standard baselines without introducing a heavy detection or segmentation stack. | [
"ResNet-18 multi-label baseline",
"MobileNetV3 multi-label baseline",
"ViT-tiny multi-label baseline"
] | [
"Pascal VOC 2007"
] | Generate a practical idea and an executable implementation plan. Keep the model small, the training pipeline standard, and the ablations clean. | Prefer methods that can run with standard image-classification backbones and sigmoid multi-label heads on a single GPU. |
tabular_imbalance_cls | Tabular ML | incremental_innovation | Imbalanced tabular classification is common in real applications and is still easy to benchmark with compact models and short training loops. | Design a lightweight tabular method for improving imbalanced classification without relying on large ensembles, costly resampling pipelines, or heavy AutoML stacks. | [
"XGBoost",
"LightGBM",
"MLP baseline"
] | [
"Credit Card Fraud",
"Telco Churn"
] | Return a practical method and a runnable plan. Favor compact architectures, fair imbalance-aware metrics, and fast experimentation. | The project should be implementable with sklearn-style preprocessing and short training jobs on CPU or a single modest GPU. |
tabular_missing_value_cls | Tabular ML | incremental_innovation | Missing values are a realistic source of difficulty in tabular learning and provide a clean testbed for lightweight robustness ideas under short iteration cycles. | Propose a lightweight tabular-classification method that improves robustness to missing-value patterns without relying on expensive imputation ensembles or large stacked models. | [
"XGBoost",
"TabTransformer",
"MLP baseline with imputation"
] | [
"Adult",
"Higgs Small"
] | Produce a practical idea and an executable implementation plan. Keep preprocessing simple, ablations clear, and compute modest. | Prefer methods that can be implemented with simple masking or feature-gating ideas and benchmarked quickly on standard tabular datasets. |
timeseries_ecg_cls | Time Series | incremental_innovation | Compact ECG and UCR-style sequence benchmarks are cheap to run and make it easy to compare lightweight temporal architectures without specialized infrastructure. | Design a lightweight time-series classification method that improves compact ECG-style benchmarks without using a large transformer stack or custom hardware assumptions. | [
"1D CNN baseline",
"GRU baseline",
"InceptionTime-small"
] | [
"ECG200",
"FordA"
] | Return a practical method and a reproducible implementation plan. Favor small temporal modules, interpretable ablations, and fast turnaround. | The full experiment should remain compatible with plain PyTorch and run comfortably on a single GPU. |
graph_link_pred | Graph ML | incremental_innovation | Lightweight link prediction on citation or collaboration graphs is a compact graph-learning setting that still allows meaningful architectural comparison and ablation. | Propose a lightweight graph-learning method that improves standard link-prediction baselines while keeping the codebase simple and easy to reproduce. | [
"GCN encoder + dot-product decoder",
"GraphSAGE encoder + MLP decoder",
"GAT encoder baseline"
] | [
"Cora",
"Citeseer"
] | Generate a new idea and an executable plan. Favor simple neighborhood or edge-scoring modifications, clean ablations, and modest compute cost. | Avoid large graph pretraining and keep the project runnable on a single GPU with standard PyTorch Geometric tooling. |
audio_emotion_cls | Audio | incremental_innovation | Small audio emotion benchmarks are compact enough for repeated experimentation and are a useful testbed for lightweight temporal and spectral feature-learning ideas. | Design a lightweight audio-classification method that improves small emotion-recognition benchmarks without depending on a large speech foundation model. | [
"CNN spectrogram baseline",
"CRNN baseline",
"tiny conformer baseline"
] | [
"RAVDESS",
"CREMA-D"
] | Return a practical idea and an executable plan. Keep preprocessing standard, the model compact, and the implementation reproducible. | Favor torchaudio-compatible pipelines, short training cycles, and methods that can be compared fairly on a single GPU. |
multimodal_hateful_memes | Multimodal | nontrivial_recomposition | Compact multimodal classification tasks are useful for testing whether the system can improve cross-modal fusion quality without leaning on a very large vision-language model. | Propose a lightweight multimodal classification method that improves compact vision-language benchmarks without introducing a heavy multi-stage or large-model serving stack. | [
"late-fusion multimodal baseline",
"CLIP linear-probe baseline",
"compact VLM baseline"
] | [
"Hateful Memes"
] | Produce a novel idea, an executable plan, and a benchmarkable implementation path. Keep the model compact, the fusion strategy interpretable, and the evaluation fair. | Reward methods with clean image-text interfaces and realistic single-GPU training requirements. |
NanoResearch 20 Topics
This dataset contains 20 research-task specifications used to evaluate NanoResearch across multiple machine-learning domains. Each example describes a compact research problem, expected baselines, datasets, and user-facing requirements for generating an implementation-oriented research plan.
Schema
Each record contains:
question_id: unique task identifier.domain: research domain, such as NLP, CV, Tabular ML, Time Series, Graph ML, Audio, or Multimodal.difficulty: coarse difficulty category.background: context motivating the task.problem_statement: target research problem.baselines: baseline methods to consider.datasets: datasets associated with the task.user_requirements: user-facing constraints and expected output style.extra_context: additional implementation or evaluation constraints.
Usage
from datasets import load_dataset
ds = load_dataset("xjh111/nanoresearch-20topics", data_files="data/nanoresearch_20topics.json")
print(ds["train"][0])
Citation
If you use this dataset, please cite the NanoResearch project or paper associated with this benchmark.
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