arXiv:2606.09450v1 Announce Type: new Abstract: LLMs have recently achieved strong results on formal proving benchmarks. However, existing evaluations remain heavily concentrated on competition-style problems and often fail to capture how models behave on longer, more dependency-rich mathematical developments. We introduce TheoremBench, a Lean4 benchmark designed to evaluate theorem provers beyond contest settings. The benchmark is built from nearly one hundred classical theorems and is released in two complementary forms: a plain main version containing one target theorem per instance, and a
The rapid advancements in large language models necessitate more sophisticated benchmarks to accurately assess their capabilities in complex domains like formal mathematics, moving beyond simpler competition-style problems.
This benchmark is crucial for understanding and accelerating the development of robust, reliable AI systems capable of advanced reasoning and theorem proving, a key step towards truly intelligent agents.
The introduction of TheoremBench allows for a more granular and realistic evaluation of LLMs on theorem proving, shifting the focus to longer, more interdependent mathematical developments beyond current benchmarks.
- · AI research labs developing LLMs
- · Formal mathematics community
- · Open-source AI developers
- · Educational technology sector
- · AI models reliant on simplistic evaluation metrics
- · Organizations underestimating the complexity of true mathematical reasoning in A
- · Benchmarking methods focused solely on competition problems
More accurate and challenging benchmarks drive improvements in LLM reasoning capabilities.
Advanced theorem-proving LLMs accelerate scientific discovery and automate complex logical tasks across various sectors.
The ability of AI to formally prove theorems could fundamentally alter research paradigms, leading to new methods of knowledge generation and validation.
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Read at arXiv cs.AI