ProofOfThought: LLM-based reasoning using Z3 theorem proving
ProofOfThought LLM-based reasoning using Z3 theorem proving. Quick Start from openai import OpenAI from z3dsl.reasoning import ProofOfThought client = OpenAI(api_key=”…”) pot = ProofOfThought(llm_client=client) result = pot.query(“Would Nancy Pelosi publicly denounce abortion?”) print(result.answer) # False Batch Evaluation from z3dsl.reasoning import EvaluationPipeline evaluator = EvaluationPipeline(pot, output_dir=”results/”) result = evaluator.evaluate( dataset=”strategyqa_train.json”, max_samples=10 ) print(f”Accuracy: {result.metrics.accuracy:.2%}”) Installation pip install z3-solver openai scikit-learn numpy Architecture The system has two layers: High-level API (z3dsl.reasoning) – Simple Python interface for reasoning tasks Low-level DSL (z3dsl) – JSON-based Z3 theorem prover…
