:PROPERTIES:
:CREATED:  [2026-05-11 Mon]
:ID:       ec267c73-4e0d-4efb-9497-cb72f74538e4
:END:
#+title: Gaur & Sheth (2023): CREST — Trustworthy Neurosymbolic AI
#+filetags: :passepartout:architecture:

Gaur and Sheth present the CREST framework: Consistency, Reliability, user-level Explainability, and Safety build Trust — and they argue these require neurosymbolic methods. Their empirical finding: GPT-3.5 breached safety constraints 30% of the time when asked identical questions repeatedly. Claude's 16 safety rules and Sparrow's 23 rules provide no /inherent/ safety — they are heuristic guardrails that can be breached through prompt variation.

These findings validate three Passepartout design commitments: (1) prompt-level safety is insufficient — deterministic gates run in pure Lisp, cost 0 tokens, and cannot be evaded by prompt engineering; (2) inconsistency is the norm — the cardinality model expects contradiction and surfaces it with provenance; (3) knowledge infusion is required for trust — Passepartout's symbolic index IS the knowledge infusion layer, facts extracted from prose, verified by Screamer, and available for any LLM call.

Reference: Gaur, M., & Sheth, A. (2023). Building Trustworthy NeuroSymbolic AI Systems: Consistency, Reliability, Explainability, and Safety. arXiv:2312.06798.