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2077AI

Revolutionizing AI Data

The 2077AI Foundation, is at the forefront of AI data standardization and progression. Our goal is unlocking greater value from data, accelerating AI development, and fostering an efficient, prosperous AI data ecosystem. Utilizing a $10.24 million investment, we're committed to promoting open source initiatives and invite you to join us in shaping the future of AI.

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DataSets

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KOR-BENCH: A Benchmark for Knowledge-Orthogonal Reasoning Tasks: 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.

KOR-BENCH: A Benchmark for Knowledge-Orthogonal Reasoning Tasks

KOR-BENCH

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.

AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions

AutoKaggle

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.

YuE: Open Music Foundation Models for Full-Song Generation

YuE

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.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

Chain-of-Agents

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.

FormalMATH: Benchmarking Formal Mathematical Reasoning of Large Language Models

FormalMATH

FormalMATH is a large-scale Lean4 benchmark comprising 5,560 formally verified problems spanning from high-school Olympiad challenges to undergraduate-level theorems across diverse domains including algebra, applied mathematics, calculus, number theory, and discrete mathematics.

SuperGPQA: An LLM Evaluation Benchmark Across 285 Graduate Disciplines

SuperGPQA

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.

KOR-BENCH: A Benchmark for Knowledge-Orthogonal Reasoning Tasks

KOR-BENCH

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.

AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions

AutoKaggle

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.

YuE: Open Music Foundation Models for Full-Song Generation

YuE

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.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

Chain-of-Agents

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.

FormalMATH: Benchmarking Formal Mathematical Reasoning of Large Language Models

FormalMATH

FormalMATH is a large-scale Lean4 benchmark comprising 5,560 formally verified problems spanning from high-school Olympiad challenges to undergraduate-level theorems across diverse domains including algebra, applied mathematics, calculus, number theory, and discrete mathematics.

SuperGPQA: An LLM Evaluation Benchmark Across 285 Graduate Disciplines

SuperGPQA

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.

Blog/Featured

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Introducing Chain-of-Agents: A New Paradigm for Agent Foundation Model

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VeriGUI: The Open-Source Benchmark Testing AI Agents Real-World Capabilities

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Unveiling GPT-5’s Two Faces: SuperGPQA Benchmark Analysis

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Creative Writing Dataset with Thought Processes: Unleashing Human-like Creativity in AI

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Proudly Announce Abaka AI Join 2077AI Community as Core Founding Contributor

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FormalMATH Benchmark: A Formal Mathematics Benchmark for Pushing the Limits of AI

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Breaking Traditional Knowledge Dependency: KOR-Bench for Evaluating Intrinsic Reasoning Abilities of Models

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A Novel Paradigm for Model Evaluation: The Innovative Multi-source Document Parsing Evaluation Framework OmniDocBench

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OmniHD-Scenes: A Next-Generation Multimodal Dataset for Autonomous Driving

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SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines - Exploring the Real Proficiency Boundaries of LLM

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Matrix Dataset: A Revolutionary Bilingual AI Pre-training Corpus

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PIN Dataset: A Unified Paradigm for Multimodal Learning

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Blog

Insights and narratives surrounding the 2077AI Project, focusing on aspects like dataset development and model evaluation.
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View more

Introducing Chain-of-Agents: A New Paradigm for Agent Foundation Model

VeriGUI: The Open-Source Benchmark Testing AI Agents Real-World Capabilities

Unveiling GPT-5’s Two Faces: SuperGPQA Benchmark Analysis

Creative Writing Dataset with Thought Processes: Unleashing Human-like Creativity in AI

Proudly Announce Abaka AI Join 2077AI Community as Core Founding Contributor

FormalMATH Benchmark: A Formal Mathematics Benchmark for Pushing the Limits of AI