arXiv:2603.02668v2 Announce Type: replace Abstract: We present SorryDB, a dynamically-updating benchmark of open Lean tasks drawn from 78 real world formalization projects on GitHub. Unlike existing static benchmarks, often composed of competition problems, hillclimbing the SorryDB benchmark will yield tools that are aligned to the community needs, more usable by mathematicians, and more capable of understanding complex dependencies. Moreover, by providing a continuously updated stream of tasks, SorryDB mitigates test-set contamination and offers a robust metric for an agent's ability to contr
The development of SorryDB emerges from the growing push to integrate AI into formal theorem proving, addressing the limitations of static benchmarks and aiming for more practical applications.
This benchmark helps bridge the gap between theoretical AI theorem proving and real-world mathematical formalization, accelerating the development of more usable and powerful AI tools for mathematicians.
The availability of a dynamic, real-world-aligned benchmark will refine the training and evaluation of AI provers, leading to more robust and context-aware systems.
- · AI research in formal verification
- · Mathematicians using formal methods
- · Open-source AI development teams
- · Lean theorem prover community
- · Developers relying solely on static benchmarks
- · AI provers not aligned with practical mathematical challenges
Improved AI theorem provers will allow for faster and more reliable verification of complex mathematical theorems and software.
This advancement could lead to a broader adoption of formal methods in areas like critical software development and hardware design, enhancing security and reliability.
Ultimately, more capable AI provers could dramatically accelerate scientific discovery by automating complex proof generation and validation, potentially impacting fields beyond pure mathematics.
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Read at arXiv cs.AI