SMT-LLM builds a constraint graph from PyPI metadata and AST-derived imports, solves it with Z3, and uses LLM imputation only when needed, resolving 83.6% of HG2.9K snippets versus PLLM's 54.8% while cutting median time by 6.3x and LLM calls by 11x.
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2026 2verdicts
UNVERDICTED 2representative citing papers
A meta-level neuro-symbolic layer uses LLMs to synthesize, consolidate, and verify minimal necessary-and-sufficient first-order causal rules from human-specified goals and principles, demonstrated in two autonomous-driving proof-of-concept scenarios.
citing papers explorer
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Breaking the Dependency Chaos: A Constraint-Driven Python Dependency Resolution Strategy with Selective LLM Imputation
SMT-LLM builds a constraint graph from PyPI metadata and AST-derived imports, solves it with Z3, and uses LLM imputation only when needed, resolving 83.6% of HG2.9K snippets versus PLLM's 54.8% while cutting median time by 6.3x and LLM calls by 11x.
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Towards Neuro-symbolic Causal Rule Synthesis, Verification, and Evaluation Grounded in Legal and Safety Principles
A meta-level neuro-symbolic layer uses LLMs to synthesize, consolidate, and verify minimal necessary-and-sufficient first-order causal rules from human-specified goals and principles, demonstrated in two autonomous-driving proof-of-concept scenarios.