VERIMED translates natural-language requirements to formal logic via LLMs, detects ambiguity from stochastic formalization differences, and audits for inconsistency and safety violations using SMT queries.
A Neurosymbolic Approach to Natural Language Formalization and V er- ification,
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MANTRA automatically synthesizes SMT-validated compliance benchmarks for LLM agents from natural language manuals and tool schemas, producing 285 tasks across 6 domains with minimal human effort.
A framework that extracts candidate procedural rules from uncertain LLM-generated state-transition samples, transforms them into explicit constraints, and uses them to repair steps in virtual lab planning.
A neuro-symbolic system using LLM disagreement to trigger Z3 formal verification achieves 94.3% accuracy and a combined score of 41.88 on syllogistic validity prediction, improving on the pure ensemble by reducing content effects.
citing papers explorer
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Neurosymbolic Auditing of Natural-Language Software Requirements
VERIMED translates natural-language requirements to formal logic via LLMs, detects ambiguity from stochastic formalization differences, and audits for inconsistency and safety violations using SMT queries.
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MANTRA: Synthesizing SMT-Validated Compliance Benchmarks for Tool-Using LLM Agents
MANTRA automatically synthesizes SMT-validated compliance benchmarks for LLM agents from natural language manuals and tool schemas, producing 285 tasks across 6 domains with minimal human effort.
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Managing Uncertainty in LLM-Generated Procedural Knowledge for Virtual Laboratory Planning
A framework that extracts candidate procedural rules from uncertain LLM-generated state-transition samples, transforms them into explicit constraints, and uses them to repair steps in virtual lab planning.
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FregeLogic at SemEval 2026 Task 11: A Hybrid Neuro-Symbolic Architecture for Content-Robust Syllogistic Validity Prediction
A neuro-symbolic system using LLM disagreement to trigger Z3 formal verification achieves 94.3% accuracy and a combined score of 41.88 on syllogistic validity prediction, improving on the pure ensemble by reducing content effects.