Welcome to InnovatingAI
InnovatingAI pioneers next-generation LLM-powered agents that redefine intelligent automation and decision-making.
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What is InnovatingAI?
InnovatingAI develops advanced LLM-powered agents that enhance automation and decision-making through data science frameworks and problem-solving benchmarks, bridging AI theory with real-world applications.
InnovatingAI pioneers next-generation agents with four core features:
Learn moreBenchmark
Next-generation evaluation systems leveraging automated agent interactions to create adaptive testing environments that evolve with AI capabilities, measuring both performance and creative problem-solving
Agent
Autonomous systems combining reasoning, planning and tool-calling architectures that enable dynamic task decomposition and multi-agent collaboration for complex problem-solving
Resource
Intelligent resource orchestration frameworks that optimize compute allocation, memory utilization and energy efficiency across distributed AI workloads through real-time monitoring and adaptive scheduling
Model
Meta-learning architectures featuring self-optimizing neural networks that continuously refine their parameters through reinforcement learning loops and performance feedback mechanisms
Benchmark
Task 1: atposTask 2: belkaTask 3: OAG
InnoGym: Benchmarking the Innovation Potential of AI Agents
- Real-World Tasks: Authentic challenges from multiple domains requiring creativity and planning
- Research-Level Challenges: Tasks requiring reading and implementing cutting-edge research papers, eliminating web-sourced solutions through novel problem design
- Industrial-Grade Validation: Multi-stage evaluation from basic implementation to full research paper generation with rigorous code verification
Agent
AutoMind: Adaptive Knowledgeable Agent for Automated Data Science
Revolutionizing automated data science with domain expertise integration and adaptive problem-solving.
- Expert Knowledge Base for Data Science: Curated domain expertise from Kaggle competitions and academic papers, enabling complex ML task solving with human-level insights
- Agentic Knowledge Tree Search Algorithm: Hierarchical knowledge-guided exploration of solution spaces, repesenting an improvement of 13.5% over the prior state-of-the-art (SOTA) on MLE-Bench
- Self-adaptive Coding Strategy: Dynamic code generation that automatically adjusts to task complexity, reducing token costs by 63% compared to prior SOTA
Resources
Essential tools for your projects
Resource 1
Essential coding utilities for your projects
- Code formatting and linting
- Version control integration
- Debugging assistance
Resource 2
Curated datasets for testing and development
- Multiple file formats
- Clean and structured
- Ready to use
Resource 3
Step-by-step tutorials and examples
- Beginner to advanced
- Practical examples
- Regular updates
Model
| Weight Quantization | Backend | Prefill (tokens/sec) | Decode (tokens/sec) | Time to first token (sec) | Model size (MB) |
|---|---|---|---|---|---|
| dynamic_int4 | CPU | 118 | 12.8 | 9.2 | 4201 |
| dynamic_int4 | GPU | 446 | 16.1 | 15.1 | 4201 |
The table above demonstrates the performance metrics of our model across different hardware backends. The dynamic_int4 quantization provides optimal balance between model size and inference speed.
Key observations: GPU backend significantly improves prefill speed (3.8x faster than CPU), while maintaining comparable decode performance. The consistent model size confirms proper quantization.
Publications
Cutting-edge frameworks advancing automated data science and AI innovation benchmarking
Acknowledgments
Our Sincere Thanks
We would like to express our sincere gratitude to AIDE and MLE-Bench for their significant contributions to our project. Their work has been invaluable, and we are truly grateful for the opportunity to build upon their open-source implementations.
We also deeply appreciate the continued support and collaboration from our community. A special thank you to all who have contributed by reporting issues and sharing their technical expertise - your efforts have played a crucial role in our project's development and success. 🙌