arXiv:2606.04883v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in workflows for generating formal proofs in Lean. These workflows often decompose problems into smaller lemmas, sample many proof attempts, and use compiler feedback to guide search. However, they can be prohibitively expensive, often spending substantial compute on attempts that ultimately fail. In this work, we address this problem with an action routing agent that consists of a data plane and a control plane. The data plane generates natural-language lemma decompositions, formalizes them in L
The proliferation of Large Language Models (LLMs) and their integration into complex problem-solving workflows, such as formal theorem proving, necessitates innovation in optimizing their operational efficiency and cost.
This development indicates progress in making sophisticated AI systems like agentic theorem provers more practical and economically viable, expanding their applicability beyond research labs.
The focus is shifting towards developing more efficient and less resource-intensive methods for deploying AI agents, potentially democratizing access to powerful AI tools.
- · AI compute providers
- · Enterprises adopting formal verification
- · Developers of AI agent architectures
- · Inefficient AI model developers
- · Traditional theorem proving methods
The cost of generating formal proofs using AI agents will decrease, making formal verification more accessible.
This could accelerate the development and deployment of highly reliable AI systems in critical applications, reducing software bugs and security vulnerabilities.
Increased reliability and lower development costs could lead to new product categories and services that are currently too complex or expensive to verify.
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Read at arXiv cs.CL