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arXiv — NLP / Computation & Language · · 3 min read

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

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Computer Science > Computation and Language

arXiv:2609.09264 (cs)
[Submitted on 8 Sep 2026]

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

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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.
Subjects: Computation and Language (cs.CL); Logic in Computer Science (cs.LO)
Cite as: arXiv:2609.09264 [cs.CL]
  (or arXiv:2609.09264v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2609.09264
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Vikash Singh [view email]
[v1] Tue, 8 Sep 2026 17:59:46 UTC (1,301 KB)
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