SpecPylot generates and validates icontract specifications for Python programs by combining LLM proposals with Crosshair symbolic execution feedback.
Automatic Generation of Formal Specification and Verification Annotations Using LLMs and Test Oracles
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
RLVR training raises verified Dafny pass rates from 9.7% to 31.1% on a filtered benchmark while a Lean proof scaffold lifts success from 46.2% to 69.2% on a pilot set and solves 7 of 42 prior unsolved tasks.
An agentic LLM pipeline extracts and translates unstructured requirements into syntactically and semantically aligned formal properties, achieving 77.8% accuracy across three scenarios.
citing papers explorer
-
SpecPylot: Python Specification Generation using Large Language Models
SpecPylot generates and validates icontract specifications for Python programs by combining LLM proposals with Crosshair symbolic execution feedback.
-
Automating Formal Verification with Reinforcement Learning and Recursive Inference
RLVR training raises verified Dafny pass rates from 9.7% to 31.1% on a filtered benchmark while a Lean proof scaffold lifts success from 46.2% to 69.2% on a pilot set and solves 7 of 42 prior unsolved tasks.
-
Towards an Agentic LLM-based Approach to Requirement Formalization from Unstructured Specifications
An agentic LLM pipeline extracts and translates unstructured requirements into syntactically and semantically aligned formal properties, achieving 77.8% accuracy across three scenarios.