Towards Neuro-symbolic Causal Rule Synthesis, Verification, and Evaluation Grounded in Legal and Safety Principles
Zainab Rehan, Christian Medeiros Adriano, Sona Ghahremani, Holger Giese
Why It Matters
What makes this one worth your time
This work addresses critical challenges in rule-based systems, particularly in safety-critical domains like autonomous driving, by providing a structured approach to goal specification and rule maintenance.
A novel framework for scalable rule synthesis and verification in safety-critical AI systems.
Summary
The paper presents an extension of a neuro-symbolic causal framework that introduces a meta-level layer for synthesizing and verifying rules based on high-level goals and safety principles, using large language models to enhance scalability and mitigate goal misspecification.
Key contributions
- Development of a Goal/Rule Synthesizer and Rule Verification Engine.
- Implementation of a synthesis pipeline that translates natural language goals into formal rules.
- Evaluation of the framework in autonomous driving scenarios to derive minimal necessary rule sets.
Notable insights
- The integration of large language models for goal decomposition and rule synthesis is a clever use of LLM capabilities in formal rule generation.
- The focus on a meta-level layer for iterative refinement of rules highlights an innovative approach to mitigating goal misspecification.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2604.28087v1 Announce Type: cross Abstract: Rule-based systems remain central in safety-critical domains but often struggle with scalability, brittleness, and goal misspecification. These limitations can lead to reward hacking and failures in formal verification, as AI systems tend to optimize for narrow objectives. In previous research, we developed a neuro-symbolic causal framework that integrates first-order logic abduction trees, structural causal models, and deep reinforcement learning within a MAPE-K loop to provide explainable adaptations under distribution shifts. In this paper, we extend that framework by introducing a meta-level layer designed to mitigate goal misspecification and support scalable rule maintenance. This layer consists of a Goal/Rule Synthesizer and a Rule Verification Engine, which iteratively refine a formal rule theory from high-level natural-language goals and principles provided by human experts. The synthesis pipeline employs large language models (LLMs) to: (1) decompose goals into candidate causes, (2) consolidate semantics to remove redundancies, (3) translate them into candidate first-order rules, and (4) compose necessary and sufficient causal sets. The verification pipeline then performs (1) syntax and schema validation, (2) logical consistency analysis, and (3) safety and invariant checks before integrating verified rules into the knowledge base. We evaluated our approach with a proof-of-concept implementation in two autonomous driving scenarios. Results indicate that, given human-specified goals and principles, the pipeline can successfully derive minimal necessary and sufficient rule sets and formalize them as logical constraints. These findings suggest that the pipeline supports incremental, modular, and traceable rule synthesis grounded in established legal and safety principles.