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StochBench: A Domain-Specific Benchmark for Stochastic Processes in LeanStochBench: A Domain-Specific Benchmark for Stochastic Processes in Lean

📅 2026-09-10 ⏱️ 约 5 分钟阅读⏱️ 5 min read ✍️ AI导航编辑部✍️ AI Nav Editorial 🔗 arxiv.org
StochBench: A Domain-Specific Benchmark for Stochastic Processes in Lean
📝 内容摘要📝 Summary

Computer Science > Computation and Language [Submitted on 8 Sep 2026] Title:StochBench: A Domain-Specific BenchmarComputer Science > Computation and Language [Submitted on 8 Sep 2026] Title:StochBench: A Domain-Specific Benchmar

📌 核心要点

  • Computer Science > Computation and LanguageComputer Science > Computation and Language
  • [Submitted on 8 Sep 2026][Submitted on 8 Sep 2026]
  • Title:StochBench: A Domain-Specific Benchmark for Stochastic Processes in LeanTitle:StochBench: A Domain-Specific Benchmark for Stochastic Processes in Lean

Computer Science > Computation and Language

[Submitted on 8 Sep 2026]

Title:StochBench: A Domain-Specific Benchmark for Stochastic Processes in Lean

View PDF HTML (experimental)Abstract:Leading benchmarks for formal theorem proving with large language models are small collections drawn from competition math, such as the IMO and Putnam, that poorly represent field-specific applications. We introduce StochBench, a Lean 4 benchmark of 450 graduate stochastic-processes problems at varying abstraction levels, each paired with its natural-language source. Addressing a field underrepresented in Mathlib, it covers finite and countable Markov chains, renewal processes, random walks, martingales, stopping times, queues, Brownian motion, stochastic calculus, weak convergence, and Poisson and continuous-time Markov processes. Our Opus 4.8-based agent achieves a 34.9% proof rate (157/450) under a 15-minute per-problem limit. StochBench better represents domain-specific applied mathematics while remaining challenging for advanced provers.

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Computer Science > Computation and Language

[Submitted on 8 Sep 2026]

Title:StochBench: A Domain-Specific Benchmark for Stochastic Processes in Lean

View PDF HTML (experimental)Abstract:Leading benchmarks for formal theorem proving with large language models are small collections drawn from competition math, such as the IMO and Putnam, that poorly represent field-specific applications. We introduce StochBench, a Lean 4 benchmark of 450 graduate stochastic-processes problems at varying abstraction levels, each paired with its natural-language source. Addressing a field underrepresented in Mathlib, it covers finite and countable Markov chains, renewal processes, random walks, martingales, stopping times, queues, Brownian motion, stochastic calculus, weak convergence, and Poisson and continuous-time Markov processes. Our Opus 4.8-based agent achieves a 34.9% proof rate (157/450) under a 15-minute per-problem limit. StochBench better represents domain-specific applied mathematics while remaining challenging for advanced provers.

References & Citations

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Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)

Connected Papers (What is Connected Papers?)

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scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub (What is DagsHub?)

Gotit.pub (What is GotitPub?)

Hugging Face (What is Huggingface?)

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Demos

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Influence Flower (What are Influence Flowers?)

CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

来源arxiv.org· 本文为编辑整理,仅供参考