10 stories tagged Dataset
Researchers released OpenDiscoveryTrace, a public dataset of 558 complete AI scientist trajectories. Unlike existing benchmarks that only evaluate final outputs, this dataset captures the structured reasoning steps behind AI-generated code, hypotheses, and papers, enabling auditing of scientific methodology and diagnosis of failure modes.
Researchers created MTDiag, a dataset to test how well AI models can diagnose illnesses through multi-turn conversations, mimicking real doctor-patient interactions. Current AI models struggle with accuracy and reliability in these dynamic settings, and MTDiag aims to bridge this gap.
The Allen Institute for AI released TutorMoments, an open-source dataset that teaches AI tutors when to step in and when to let students struggle, aiming to reduce interruptions and improve learning outcomes.
Researchers introduced CG-World, a large-scale dataset derived from industrial computer graphics pipelines that explicitly records intermediate world states—including spatial structure, motion curves, physics caches, and contact events—to help AI models learn the joint dynamics of states, actions, events, and observations.
Researchers created PLURAL, a dataset designed to help AI understand global values beyond Western perspectives. This could make AI tools more relatable worldwide. PLURAL transforms survey responses from 92 countries into scenarios that teach AI about diverse cultural values. This is a step toward more inclusive AI systems. You can explore the initial version of PLURAL on ArXiv.
Researchers have released OpenRTLSet, the largest open-source dataset for hardware design, featuring 131,000 Verilog code samples. This resource enables AI models to learn and generate hardware designs, potentially speeding up innovation in electronics.
Researchers created CrowdMath, a dataset of 164 expert-annotated math discussions showing how experts collaborate to solve open problems. This could help AI models learn from human reasoning processes, not just final answers.
EVA-Bench Data 2.0 is a comprehensive dataset designed to test AI models' ability to use tools effectively. It includes 213 scenarios across 3 domains and 121 tools, making it a valuable resource for developers and researchers.
Researchers introduce CFMS, the first fine-grained multimodal sarcasm dataset for Chinese social media. It includes 2,796 image-text pairs with triple-level annotations, advancing research in sarcasm detection.
Researchers introduce TR-EduVSum, a dataset of 82 Turkish educational videos with 3281 human summaries, and AutoMUP, a consensus-based summarization framework. This work advances automatic summarization for educational content in Turkish.