(Auto)formalization is supposed to be easy: Trellis process semantics for spelling out rigorous proofs
arXiv:2606.09674v1 Announce Type: new Abstract: We present Trellis: an autoformalization system that leverages LLM agents in a deterministically constrained workflow to enforce incremental progress in Lean autoformalization tasks through iterative refinement of natural language proofs. Our approach is motivated by the common mathematician's notion of what it means to have a rigorous proof in the first place: namely, that it would be routine to elaborate any part of the proof in further detail. The result is a system which aims to achieve reliable autoformalization on a modest budget and with g
The rapid advancement in LLMs and AI agent architectures has made autoformalization a tractable challenge, moving from theoretical concept to practical system development.
This development in autoformalization directly addresses a significant bottleneck in rigorous scientific and mathematical proof verification, potentially accelerating research and development across many fields.
The ability to reliably translate natural language proofs into formal systems like Lean changes the landscape for automated theorem proving and the overall rigour of complex intellectual work.
- · Formal verification developers
- · Mathematicians
- · Computer scientists
- · AI researchers
- · Manual proof checkers
- · Inefficient software development
- · Those reliant on informal verification
Increased efficiency in formal verification and theorem proving.
Acceleration of research in mathematics, computer science, and areas requiring high-assurance systems.
Enhanced trust and reliability in mission-critical software and hardware through automated formal guarantees.
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Read at arXiv cs.AI