arXiv:2603.16475v2 Announce Type: replace Abstract: In schema-guided reasoning (SGR) pipelines, LLMs produce explicit intermediate structures -- rubrics, checklists, or verification queries -- before committing to a final decision. SGR is increasingly adopted because it promises controllability: practitioners expect to inspect, edit, and override these structures to steer the outcome. But does the promise hold? We introduce a causal evaluation protocol to measure it: by selecting tasks where a deterministic function maps intermediate structures to decisions, every controlled edit implies a uni
The rapid advancement and deployment of LLMs necessitate robust methods for ensuring their reliability and controllability, especially in critical applications.
A strategic reader should care because this research addresses fundamental reliability and controllability issues with LLMs, which are crucial for their safe and effective integration into complex workflows.
This causal evaluation protocol introduces a new standard for assessing LLM faithfulness to intermediate structures, providing a clearer path to verifiable and auditable AI systems.
- · AI developers
- · Enterprises adopting AI
- · Users of LLM-powered applications
- · Black-box LLM approaches
- · Developers ignoring interpretability
Increased trustworthiness and adoption of LLMs in high-stakes reasoning tasks.
Development of new LLM architectures and training methodologies optimized for faithfulness to intermediate structures.
Potential for regulatory frameworks to mandate causal analysis methods for AI system deployments in regulated industries.
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Read at arXiv cs.AI