Skill UI

Guides users through the complete GAIA benchmark submission process, including environment validation, cost estimation, benchmark execution, result packaging, and leaderboard comparison. Ensures integrity via audit reports before submission to the Princeton GAIA leaderboard.

This guide provides comprehensive instructions for packaging multiagent system (MARL) and game theory evidence for academic submissions, particularly for conferences like AAMAS. It emphasizes that the artifact must be inspectable, containing not just a trained model, but the game definition, environment code, opponent sets, and interaction protocols, ensuring rigorous reproducibility and reviewability.

A comprehensive guide for packaging and submitting evidence packages (artifacts) for NLP research papers, such as code, datasets, prompts, and model outputs. It details requirements for reproducibility, adhering to the Responsible NLP checklist, ensuring proper anonymization, and preparing for post-acceptance public release in academic settings like ACL.

A comprehensive guide for authors on best practices for packaging, documenting, and ensuring the reproducibility of LLM artifacts (weights, data, code, prompts, etc.). It addresses requirements for anonymous academic review and public post-acceptance release, focusing on reproducibility, licensing, and transparency.

This comprehensive guide outlines best practices for packaging and releasing scientific artifacts—including code, training configurations, demonstration data, and policy checkpoints—for robotics research papers. It details how to prepare materials for anonymous reviewer assessment and for durable public release, ensuring the work is fully reproducible by the academic community.

A comprehensive guide for authors submitting research to CVPR, detailing best practices for packaging code, models, and datasets. It covers requirements for review-time reproducibility (sealed-box rules) and long-term public release (licensing, provenance, and version pinning), ensuring that the work is runnable by any skeptical third party.

This guide details the best practices for packaging, documenting, and releasing research artifacts (code, models, data) for top-tier ML conferences. It covers two stages: preparing a non-identifiable archive for anonymous review, and managing the public release runway, focusing on reproducibility, licensing, and sustained maintenance.

Provides comprehensive guidelines for packaging and presenting research artifacts (code, data, models, benchmarks) submitted to NeurIPS. It details requirements for anonymous review packages, public release, and ensures scientific claims are reproducible by providing necessary metadata, licenses, and execution details.

A comprehensive guide on preparing and structuring evidence packages for recommender system research, especially for academic submissions. It details how to ensure that all ranking claims, off-policy estimates, and evaluation metrics are end-to-end reproducible by providing artifacts like datasets, split scripts, model checkpoints, and propensity logs in an anonymous, verifiable repository.