Dataset

OmniDocBench is a comprehensive benchmark for evaluating AI in document parsing and content extraction.

EDITREWARD is trained with our new large-scale human preference dataset, meticulously annotated by trained experts following a rigorous protocol containing over 200K preference pairs.

VideoScore2 is a multi-dimensional, interpretable, and human-aligned framework that explicitly evaluates visual quality,text-to-video alignment, and physical/common-sense consistency while producing detailed chain-of-thought rationales.

Chain-of-Agents (CoA) is a novel framework for training end-to-end agent foundation models (AFM) using multi-agent distillation and agentic reinforcement learning. Our approach addresses key challenges in developing versatile AI agents that can perform complex tasks across diverse domains.

AutoKaggle is a powerful and user-centric framework that assists data scientists in completing daily data pipelines through a collaborative multi-agent system. AutoKaggle implements an iterative development process that combines code execution, debugging, and comprehensive unit testing to ensure code correctness and logic consistency.

Knowledge-Orthogonal Reasoning Benchmark (KOR-Bench) is designed to evaluate models‘ intrinsic reasoning and planning abilities by minimizing interference from pretrained knowledge. It introduces new rules that are independent of prior knowledge, allowing for a more accurate assessment of how models adapt to novel rule-driven tasks.

YuE is a family of open foundation models based on the LLaMA2 architecture. Specifically, YuE scales to trillions of tokens and generates up to five minutes of music while maintaining lyrical alignment, coherent musical structure, and engaging vocal melodies with appropriate accompaniment.

SuperGPQA is a large-scale and highly challenging benchmark created to evaluate the advanced reasoning capabilities of Large Language Models (LLMs). Its purpose is to test model performance on expert-level, graduate qualification questions across an unprecedented 285 academic and professional disciplines.

Matrix is a massive, open-source pretraining dataset containing approximately 4.7 trillion tokens** of bilingual text in English and Chinese.

AFM-Datasets is the official training dataset released with the research paper, "Chain-of-Agents," and is specifically designed for building Agent Foundation Models (AFMs).

VeriGUI is a large-scale benchmark designed to evaluate and advance the capabilities of GUI agents in the challenging domain of long-horizon, verifiable task automation.

TaskCraft is a multi-modal benchmark dataset featuring tasks ranging from simple (1-step) to expert-level (4-step+).

CriticLeanBench is a specialized benchmark designed to evaluate the critical reasoning of AI models, specifically on the task of validating the translation of natural language mathematics into formal Lean 4 theorem statements.

MMAR (Massive Multi-disciplinary Audio Reasoning) is a new and challenging benchmark designed to evaluate the deep reasoning capabilities of Audio-Language Models (ALMs).

FormalMATH is a large-scale benchmark designed to evaluate and advance the capabilities of Large Language Models in the challenging domain of formal mathematical reasoning.

COIG-P (Chinese Open Instruction Generalist - Preference) is a high-quality, large-scale Chinese preference dataset designed for aligning Large Language Models (LLMs) with human values.