An LLM agent with Rocq backend automatically builds a verified RISC-V RV32I interpreter (1859 lines Rocq, 2848 lines extracted C++) that passes 265 tests and 12-hour fuzzing, while a Dafny backend fails.
Hammer for coq: Automation for dependent type theory.J
5 Pith papers cite this work. Polarity classification is still indexing.
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Deep embedding of free-variable tableaux in Rocq with soundness proof, modular Skolemization, and certified checking for Goeland prover outputs.
Quarry improves Rocq proof automation success rates by 7-13% under 10-minute budgets via LLM-planned decompositions ranked by a proof-state difficulty model for CoqHammer solvability.
Develops syntax and semantics for polymorphic DHOL, extends its translation to HOL, and implements the result in a logic-embedding tool for evaluation on TPTP problems.
PROMISE reframes automated proof generation as stateful search over structural embeddings of proof states, outperforming prior LLM-based systems by up to 26 points on the seL4 benchmark.
citing papers explorer
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Trustworthy Software Project Generation : a Case Study with an Interactive Theorem Prover
An LLM agent with Rocq backend automatically builds a verified RISC-V RV32I interpreter (1859 lines Rocq, 2848 lines extracted C++) that passes 265 tests and 12-hour fuzzing, while a Dafny backend fails.
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TableauxRocq: A Deep Embedding of Free-Variable Tableaux in Rocq
Deep embedding of free-variable tableaux in Rocq with soundness proof, modular Skolemization, and certified checking for Goeland prover outputs.
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Planning to Hammer: Difficulty-Aware Decomposition for Automating Rocq Proofs
Quarry improves Rocq proof automation success rates by 7-13% under 10-minute budgets via LLM-planned decompositions ranked by a proof-state difficulty model for CoqHammer solvability.
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Polymorphism Meets DHOL
Develops syntax and semantics for polymorphic DHOL, extends its translation to HOL, and implements the result in a logic-embedding tool for evaluation on TPTP problems.
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PROMISE: Proof Automation as Structural Imitation of Human Reasoning
PROMISE reframes automated proof generation as stateful search over structural embeddings of proof states, outperforming prior LLM-based systems by up to 26 points on the seL4 benchmark.