Benchmarking Tomorrow's Scientific Breakthroughs Today
bench.science represents a pioneering initiative to create standardized benchmarks for evaluating AI capabilities across various scientific domains.
The Challenge
As research increasingly leverages AI systems to accelerate discovery, we need robust frameworks to assess these tools objectively.
Our Solution
QMBench marks our first benchmark, focusing on quantum materials research, with plans to expand across other scientific disciplines.
The Approach
Each benchmark tests domain-specific knowledge, reasoning, and problem-solving abilities relevant to real research workflows.
We warmly invite researchers to join our growing community. Whether you're interested in contributing to existing benchmarks, proposing new domains to evaluate, or helping improve our evaluation methodologies, your expertise is valuable to this collaborative effort.
Our Approach
Domain-Specific
Each benchmark is tailored to a specific scientific field, capturing the unique knowledge, reasoning, and problem-solving requirements of that domain.
Research-Oriented
Our benchmarks simulate real research workflows, testing AI systems on tasks that scientists actually perform in their day-to-day work.
Community-Driven
We believe in collaborative development, inviting domain experts to contribute to benchmark design, evaluation, and continuous improvement.
Join Our Community
We're building a collaborative network of researchers, AI developers, and domain experts to drive this initiative forward. Your expertise can help shape the future of AI in scientific research.
For Domain Scientists
Help design benchmarks that accurately reflect the challenges in your field and evaluate AI systems against the standards of expert researchers.
For AI Researchers
Test your models against rigorous, domain-specific benchmarks and gain insights into how to improve AI capabilities for scientific applications.
Our Vision
We envision a future where AI systems serve as capable research collaborators, accelerating scientific discovery while maintaining the rigor and creativity that human scientists bring to their work.
Through standardized benchmarks, we aim to guide AI development toward systems that truly understand scientific domains, reason effectively about complex problems, and contribute meaningfully to research workflows.