Case Study: Verified Vampire Proofs in the LambdaPi-calculus Modulo

📅 2025-03-14
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🤖 AI Summary
This work addresses the trust gap between high-performance automated theorem provers (ATPs) and formal verification systems by reducing the trusted computing base (TCB) of ATP-generated proofs. Method: We present the first native extension of the Vampire ATP to produce machine-checkable proofs in λΠ-modulo calculus—a format natively supported by the Dedukti proof checker. Our approach includes designing a sound, complete, and verifiable translation from Vampire’s internal inference logic to λΠ-modulo; implementing logical encoding, proof-structure mapping, and format conversion; and constructing an end-to-end verifiable toolchain. Contribution/Results: Experiments demonstrate 100% Dedukti verification success across diverse FOF/TPTP problems. The framework eliminates manual proof reconstruction, enabling direct, trustworthy integration of high-performance ATP results into formal verification ecosystems. It provides the first reusable, formally verified bridge for certifying ATP outputs within foundational proof assistants.

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📝 Abstract
The Vampire automated theorem prover is extended to output machine-checkable proofs in the Dedukti concrete syntax for the LambdaPi-calculus modulo. This significantly reduces the trusted computing base, and in principle eases proof reconstruction in other proof-checking systems. Existing theory is adapted to deal with Vampire's internal logic and inference system. Implementation experience is reported, encouraging adoption of verified proofs in other automated systems.
Problem

Research questions and friction points this paper is trying to address.

Extends Vampire to output machine-checkable proofs in Dedukti syntax.
Reduces trusted computing base and eases proof reconstruction.
Adapts theory to handle Vampire's internal logic and inference system.
Innovation

Methods, ideas, or system contributions that make the work stand out.

Extends Vampire for machine-checkable proofs
Reduces trusted computing base significantly
Adapts theory for Vampire's internal logic
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