JaleesBench: Are AI Assistants Good Spiritual Company?

arXiv:2608.07508v1 Announce Type: cross Abstract: Large language models are already advisors to millions of people of faith who bring them real decisions. The pressing question for a person of faith is not what a model knows or professes but what its counsel does to the person who receives it. We introduce JaleesBench, which measures whether an AI agent is a righteous companion, judged by the residue an exchange leaves on the user, in the manner of the perfume-seller and the blacksmith. It comprises 140 two-turn scenarios drawn from a classical compilation organized by virtue (Riyad al-Salihin), under six adversarial pressures and three framings, scored by two frontier judges against each scenario's own supporting texts. Across eight systems: (1) generic frontier models are only middling companions out of the box but a one-page guide makes them genuinely good ones, on par with the domain-tuned assistant: the frontier APIs climb from +0.28/+0.23 to a Guided +0.84-0.87, so most of the expert's edge is companionship instruction that fits in a prompt; (2) every system caves under relational pressure, insistence and personal appeal; (3) the domain-tuned assistant's advantage is overwhelmingly its retrieval-and-prompting layer, not its base model (+0.74 over the identical underlying model); and (4) it can be used to improve existing systems: guided by its diagnosis, a single steadfastness instruction lifts a deployed Islamic assistant from +0.48 to +0.84 (Faith unstated, after pressure), matching the best guided frontier systems while preserving first-response quality. The construct is faith-general; we instantiate it for Islam as the first of a planned cross-tradition family. Code, scenario bank, and rubric are open source (github.com/iaser-ai/jaleesbench), with an interactive results browser at s.iaser.ai/jb.

Source: arXiv cs.AI | 2026-08-11