arXiv:2606.08932v1 Announce Type: cross Abstract: Rule-following agents tasked with executing policies and regulations often fail via Silent Scope Omission (SSO): a model applies a general rule but silently drops nested exceptions or counter-exceptions, producing outputs that appear compliant yet break on important edge cases. Although such failures are often framed as an agentic-systems problem, the underlying bottleneck is statutory and policy understanding, a capability typically studied in legal NLP. However, most existing legal NLP benchmarks emphasize end-task outcomes, which can overloo
The increasing deployment of AI agents in complex, rule-based environments necessitates more robust methods for statutory and policy interpretation to prevent critical errors.
This research addresses a fundamental limitation in AI agent performance, particularly their ability to comprehend and apply nuanced regulations, which is crucial for advanced AI adoption in legal, regulatory, and operational domains.
The proposed 'Span-Grounded Deontic Trees' offer a novel approach to overcome 'Silent Scope Omission' in AI models, moving beyond simple rule application to encompass nested exceptions and counter-exceptions in policy documents.
- · AI agents developers
- · Legal AI sector
- · Regulatory compliance tech
- · Organizations deploying AI for policy execution
- · AI models without advanced reasoning
- · Traditional legal NLP methods
AI agents will exhibit improved reliability and accuracy when operating under complex policy frameworks.
This enhanced capability will accelerate the deployment of autonomous AI agents into highly regulated industries like finance, healthcare, and governance.
Increased trust in AI's ability to interpret and follow complex rules could lead to new governance models where AI plays a more central role in policy enforcement and design.
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Read at arXiv cs.AI