AlgorithmicResearchGroup/s2orc-safety

S2ORC Safety

This dataset is a filtered and enriched subset of an S2ORC computer science paper corpus, focused on AI safety and adjacent safety-relevant research.

It contains 16,806 papers selected through:

1. 1.local embedding generation
2. 2.clustering
3. 3.GPT-5.4 mini cluster-level screening
4. 4.GPT-5.4 mini paper-level labeling
5. 5.a rescue relabel pass on suspicious exclusions
6. 6.structured metadata extraction over the accepted paper set
7. 7.filtering out 304 rows that were missing both parsed_title and abstract

Main Files

• —main/*.parquet
• —sharded full enriched source rows
• —extracted metadata
• —normalized GitHub repo links
• —Hugging Face code mirror links
• —normalized model / dataset / metric / scalar fields

• —metadata/*.parquet
• —sharded metadata extraction only

• —paper_metadata_summary_normalized.json
• —corpus-level summary statistics over the normalized metadata fields

• —code_links/*.parquet
• —sharded paper-to-code join table with normalized GitHub URLs and HF mirror paths

Contents

The main parquet includes:

• —original enriched paper fields from the source corpus
• —title, abstract, full text, sections, references, authors, venue metadata, URLs
• —extracted source-side fields like summary, methods, results, models, datasets, metrics, limitations, training_details

• —metadata extraction fields
• —reproducibility:
• —repro_steps_json
• —setup_requirements_json
• —training_or_eval_recipe_json
• —artifact_availability_json
• —code_urls_json
• —dataset_urls_json
• —model_urls_json
• —safety taxonomy:
• —safety_area_json
• —attack_or_defense_json
• —threat_model_json
• —target_system_json
• —harm_type_json
• —experimental details:
• —target_models_json
• —datasets_benchmarks_json
• —baselines_compared_json
• —evaluation_metrics_json
• —main_results_json
• —claimed_contributions_json
• —practicality:
• —compute_requirements_json
• —runtime_cost
• —human_eval_required
• —closed_model_dependency
• —deployment_readiness
• —replication_difficulty
• —extraction_confidence

• —normalized fields
• —setup_requirements_norm_json
• —target_models_norm_json
• —datasets_benchmarks_norm_json
• —baselines_compared_norm_json
• —evaluation_metrics_norm_json
• —runtime_cost_norm
• —human_eval_required_norm
• —closed_model_dependency_norm
• —deployment_readiness_norm
• —replication_difficulty_norm

• —code link fields
• —github_repo_urls_json
• —hf_code_paths_json
• —hf_code_web_urls_json
• —github_repo_count
• —hf_code_repo_count

Missing Values

• —missing list-like fields are stored as empty JSON arrays
• —missing scalar categorical fields are stored as "None specified"

Notes

• —This is a broad-tent AI safety dataset rather than a narrow alignment-only dataset.
• —The labeling and extraction steps were LLM-assisted and should be treated as high-utility annotations, not ground truth.
• —Process-only columns used to build the release were removed from the published parquet.
• —The companion code mirror is published separately as AlgorithmicResearchGroup/s2orc-safety-code.
• —Normalization is conservative. It collapses obvious duplicates like CIFAR10 / CIFAR-10, ResNet50 / ResNet-50, and accuracy / Accuracy, but does not try to solve full ontology matching.