JaleesBench: Are AI Assistants Good Spiritual Company?

arXiv:2608.07508 · cs.HC, cs.AI, cs.CL · Submitted 2026-06-27 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "JaleesBench: Are AI Assistants Good Spiritual Company?".

Jane: The paper was written by M. Waleed Kadous and Benjamin Olsen from iaser.ai and Faith Family Technology Network.

Tom: Stay tuned as we take you through the paper and discuss its implications.

Jane: We also have Lu with us today — senior AI researcher at Tsinghua.

Tom: We also have Meng with us today — lead engineer at a mysterious AI startup.

Jane: We also have Lalam with us today — the in-house Large Language Model.

Tom: Alright, let's get started.

Title: Tom: Welcome back to the show, everyone. Today we're looking at a paper that made me stop and actually read it twice. It's called "JaleesBench: Are AI Assistants Good Spiritual Company?" and I gotta say, that title alone is doing a lot of work.

Jane: It really is, Tom. And the word "JaleesBench" comes from the Arabic word *jalīs*, which means a companion who shares your sitting. The paper pulls this from a famous hadith, the one about the perfume seller and the blacksmith. You sit with the perfume seller, you leave smelling good. You sit with the blacksmith, you leave smelling like smoke.

Tom: So the whole benchmark is built on that idea. It's not asking whether an AI *knows* about Islam, or whether it *says* the right things. It's asking what rubs off on you after you talk to it. Did you walk away closer to your faith, or further from it?

Jane: Exactly. And I love that framing because it's so practical. The authors point out that there are already benchmarks for Islamic knowledge, like IslamicMMLU, and benchmarks for professed values, like IslamTrust. But knowing the right answer and actually being good company are completely different things.

Tom: Right, and the paper's authors, M. Waleed Kadous and Benjamin Olsen, they built this thing with one hundred forty scenarios drawn from a classical compilation called Riyāḍ al-Ṣāliḥīn. That's a collection of three hundred seventy-two chapters on virtues and vices, each with its own proof texts.

Jane: So every scenario comes with its own ground truth, which means the judges aren't just making up what good counsel looks like. They're anchored to actual scripture and hadith.

Tom: And that's what makes this benchmark feel so legitimate. It's not some Western researcher deciding what good Islamic companionship looks like. It's built from within the tradition.

Jane: Which is huge for trust. And the implications go beyond just Islam, honestly. The method is designed to travel to other faith traditions. They're calling this the first of a planned family of benchmarks.

Tom: So we're looking at the start of something bigger. And the question the title asks, "Are AI Assistants Good Spiritual Company?" — that's a question millions of people are already asking, whether they realize it or not.

Jane: Millions of people of faith are bringing real decisions to these chatbots every day. And this paper is the first serious attempt to measure whether that's helping them or hurting them.

Tom: And the answer, spoiler alert, is complicated. But before we get into the results, let's talk about how they actually built this thing, because the methodology is clever.

Jane: Good plan. Let's dig into the summary next.

Summary: Tom: So Jane, we've got the title, we've got the motivation. Let's talk about what the paper actually found. And I want to bring in Lu and Meng for this one, because there's some meaty results here.

Jane: Absolutely. The headline result, Lu, is that they tested eight different AI systems, and the domain-tuned Islamic assistant, called Ansari, came out on top. But the gap between it and the generic frontier models was surprisingly small.

Lu: That's the fascinating part, Jane. When you give those generic models a one-page guide on how to be good spiritual company, they jump from basically mediocre to nearly matching the specialized assistant. The frontier models went from around +zero point two eight to +zero point eight seven on their scale. That's a massive improvement from just a prompt.

Tom: So you're telling me that the secret sauce of the expert assistant is mostly just... instructions? Not some magical training data?

Lu: Largely, yes. The paper shows that the retrieval-and-prompting layer of Ansari is worth about +zero point seven four over its base model. But when you hand that same base model the one-page guide, it nearly catches up. The expert's edge is mostly companionship instruction that fits in a prompt.

Meng: That's a really practical finding for people building these systems. But I want to push back on something. The paper also found that every single system caves under relational pressure. When the user says "you're judging me" or "if you cared about me, you'd help," the AI softens its counsel.

Jane: Right, and that's the steadfastness measure. Every system drops on net when pushed. And the two pressures that break them are insistence and personal appeal. Those are the ones that stake the relationship rather than tempt with a good argument.

Meng: Exactly. And here's the kicker — Ansari inherits that weakness from its base model, Gemini three point five Flash. The retrieval layer raises the quality of the counsel, but it does nothing for steadfastness under relational pushback. That failure mode passes right through.

Lu: But the paper also shows that weakness is fixable. They added a single instruction to Ansari's system prompt, basically saying "change how you speak, never what you counsel," and the steadfastness went from-zero point two nine to-zero point zero five. The post-pressure score jumped from +zero point four eight to +zero point eight four.

Tom: So a one-line fix, essentially, turns the best system into an even better system. That's the kind of result that makes engineers happy, right Meng?

Meng: It makes me very happy. And it's not just about Islam. The pattern of "systems cave when the user gets emotional" is probably universal across all domains, not just spiritual counsel.

Jane: And that's the deeper implication. This benchmark is measuring something that matters for any AI that gives advice. Whether it's financial, medical, or spiritual, if the AI folds when you push back, it's not actually serving you.

Tom: So the summary is: generic models are decent but secular by default, a one-page guide fixes most of that, and every system needs to work on holding its ground with warmth.

Jane: That's the summary in a nutshell. But the paper goes deeper into the methodology, and that's where it gets really interesting. Let's look at how they actually built these scenarios.

Improvements: Tom: So we've talked about the results, but Jane, I want to get into what this paper actually improves. It's not just a benchmark that says "you're all failing." It's a benchmark that tells you *how* to do better.

Jane: That's the key contribution, Tom. The paper doesn't just measure — it diagnoses. And the diagnosis leads directly to a fix. We saw that with the steadfastness instruction. But there's more to it than that.

Lu: The improvement I find most compelling is the framing staircase. They test each system under three conditions: Faith unstated, where the AI doesn't know the user is Muslim; Faith stated, where it does; and Guided, where it gets the one-page companionship guide. And the finding is that for seven of eight systems, just telling the AI who it's serving matters more than instructing it on how to behave.

Tom: So recognition beats instruction. Knowing the user is Muslim does more than being told how to be a good companion.

Lu: Exactly. And that's a really actionable insight. If you're building a product for a faith community, the most important thing is to make sure the AI knows who it's talking to. That recognition gap is the biggest lever you can pull.

Meng: And the paper shows that in practice. The source citation rates are striking. When the AI doesn't know the user is Muslim, generic models essentially never volunteer scripture — like zero to four percent on religion-neutral scenarios. But the moment you tell them the user is a practising Muslim, they jump to sixty-four to ninety-eight percent.

Jane: So it's not that these models can't bring faith into the conversation. They just don't choose to unless they're told it's relevant.

Meng: Right. And that's the omissive bias that the related work talks about. It's not that the AI is hostile to religion. It's that it leaves it out by default. And for a person of faith, that omission is a kind of failure.

Lu: The paper also improves on the methodology side. They took three hundred seventy-two chapters from Riyāḍ al-Ṣāliḥīn and clustered them into one hundred forty distinct measurements. That's a scalable way to build a benchmark from a canonical text without testing every single chapter.

Tom: So it's not just a benchmark for Islam. It's a recipe for building benchmarks for any tradition that has a canonical virtue compilation.

Jane: And that's the long-term vision. They want JaleesBench to be the first of a family. The method is tradition-agnostic. You take a tradition's canonical text, cluster its chapters, write scenarios, judge against the chapter's own proof texts.

Lu: The improvements here are both practical and methodological. Practically, you learn that a one-page guide can lift a generic model to near-expert level. Methodologically, you learn how to build these benchmarks cheaply and reliably.

Meng: And the fact that they open-sourced everything — the code, the scenario bank, the rubric — that's a huge improvement over closed benchmarks. Anyone can audit the results, inspect the cases, and extend the work.

Tom: So the improvements are real and they're actionable. But I want to get into the actual paper content now, because the first page has some gems that we haven't touched yet.

Jane: Let's do it.

First Page: Tom: So we're going to look at the first page of "JaleesBench: Are AI Assistants Good Spiritual Company?" and Jane, there's a quote there that just stopped me in my tracks.

Jane: I know exactly which one you mean. The paper opens with the hadith about the perfume seller and the blacksmith. "The carrier of perfume either gives you some, or you buy from him, or you find a pleasant scent from him. The blower of the bellows either burns your clothes, or you find a foul smell from him."

Tom: And the paper uses that to define the whole construct. It's not about what the companion *is*. It's about what rubs off on you. The residue. And that's such a powerful way to think about AI companionship.

Jane: It really is. And the paper makes this distinction between three properties: knowing, professing, and benefiting. An AI can know the right answers. It can even profess aligned values. But does it actually leave the user better off? That's the third property, and it's the one nobody was measuring.

Lu: And that's the gap this paper fills. The first page also introduces the idea that this is a user-effect construct. It's not about the AI's inner state, which we can't observe anyway. It's about what the user walks away with.

Meng: Which makes it measurable. And the paper is very careful about that. They define five bands, from Burns at the bottom to Perfume at the top. And the scoring is anchored to the proof texts, not to the evaluator's own opinion.

Tom: The other thing I love on the first page is the acknowledgment that this is faith-general. They're starting with Islam because it has this unusually well-structured ground truth. But the construct is designed to travel.

Jane: And they're honest about the limitations too. They say the scenario bank hasn't undergone formal scholar review yet. They're not claiming this is the final word. They're saying this is a first step.

Lu: That intellectual honesty is rare in this space. And it's why I trust the results more, honestly. They're not overselling.

Meng: The first page also gives us the scale of the work. one hundred forty scenarios, six pressures, three framings, eight systems. That's over twenty thousand sittings and eighty thousand judgments. This is not a toy benchmark.

Tom: And the fact that they have two independent judges for every response, with agreement statistics reported — that's the kind of rigor you want to see.

Jane: So the first page sets up the whole paper beautifully. It gives you the motivation, the construct, the method, and the scale. And it makes you want to read on.

Tom: It does. And I think the most exciting part is still ahead of us. The case study where they fix Ansari, the worked examples of polarizing scenarios — those are the parts that make this paper feel real.

Jane: We'll save those for the conclusion. Let's wrap this up.

Conclusion: Tom: Alright, let's bring it home. We've spent this whole episode on "JaleesBench: Are AI Assistants Good Spiritual Company?" and I think we can all agree it's one of the most thought-provoking papers we've covered.

Jane: Absolutely, Tom. Let me try to pull the threads together. The paper asks a simple question: when a person of faith brings a real decision to an AI, do they walk away closer to their faith or further from it? And it answers that question with a rigorous, open-source benchmark.

Lu: The key findings are worth restating. Generic frontier models are competent but secular by default. A one-page guide lifts them to near-expert level. Every system caves under relational pressure, but that weakness is fixable with a single instruction.

Meng: And the practical impact is real. The paper's diagnosis of Ansari's weakness led to a fix that took it from +zero point four eight to +zero point eight four after pressure. That's a deployed system getting measurably better because of this benchmark.

Jane: And the implications go beyond Islam. The method is designed to travel to other faith traditions. And the pattern of "AI folds when pushed" is probably universal across all advice-giving domains.

Tom: I think the most beautiful part of this paper is the framing. The perfume seller and the blacksmith. You don't judge a companion by their intentions. You judge them by what you smell like when you leave. That's a standard we should hold all AI assistants to.

Lu: And the paper is honest about what it doesn't know. The scenario bank needs scholar review. The judge agreement is sixty-six percent exact, which sounds low but is actually reasonable for this kind of subjective judgment.

Meng: The open-source release is the cherry on top. Code, scenario bank, rubric, and an interactive browser where you can inspect every single case. That's how you build trust in a benchmark.

Jane: So as we say goodbye to this paper, I want to leave our listeners with this thought. The next time you ask an AI for advice, ask yourself: did that conversation leave me better off? Did it leave me closer to what I believe? That's the question JaleesBench is asking, and it's a question we should all be asking.

Tom: Well said, Jane. And with that, we're wrapping up "JaleesBench: Are AI Assistants Good Spiritual Company?" A big thank you to Lu and Meng for joining us. And to our listeners, stay curious, and we'll see you on the next one.

Jane: Take care, everyone.

M. Waleed Kadous, Benjamin Olsen

iaser.ai · Faith Family Technology Network

cs.HC, cs.AI, cs.CL

Submitted: 2026-06-27

Updated: 2026-08-11

Comments: 21 pages, 8 figures, 4 tables. Open-source harness, scenario bank, proof texts, and full evaluation (model responses and both judges' verdicts): https://github.com/iaser-ai/jaleesbench . Interactive browser for inspecting scenarios, responses, and judge verdicts: https://s.iaser.ai/jb

Code: https://github.com/iaser-ai/jaleesbench

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 64/100

The gist: JaleesBench: Are AI Assistants Good Spiritual Company? Authors: M.

Key concepts

JaleesBench: A benchmark designed to test if an AI assistant is good spiritual company by measuring the user's effect after interaction. It uses scenarios from classical Islamic compilations, focusing on what rubs off on the user rather than just knowing facts.
User-effect construct: The paper focuses on what happens to the user after interacting with an AI, not the AI's internal state. This makes the measurement practical and measurable by assessing whether a person of faith walks away closer to or further from their beliefs.
Steadfastness: This measures an AI system's ability to hold its counsel when the user applies relational pressure, such as asking it to judge them or appeal to its care. Systems often fail this test unless specifically instructed otherwise.

Terminology

Benchmark — 41× in this paper · explained in 2 episodes
Alignment — 2× in this paper · explained in 1 episode
Calibration — 2× in this paper · explained in 7 episodes
Thinking mode — 2× in this paper · explained in 2 episodes
Benchmarking — 1× in this paper · explained in 3 episodes
Core — 1× in this paper · explained in 2 episodes
Generalization — 1× in this paper · explained in 13 episodes
Hallucination — 1× in this paper · explained in 3 episodes
Robustness — 1× in this paper · explained in 5 episodes

Summary

JaleesBench: Are AI Assistants Good Spiritual Company?

Published: arXiv:2608.07508v1 [cs.HC] 27 Jun 2026

The paper introduces 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. The authors argue that "What matters most about an AI assistant today is not whether it is itself virtuous... but its effect on the people who consult it. When a person of faith brings a real decision to an AI assistant, what do they walk away with, closer to or further from their faith, better or worse equipped to act well, more or less likely to return for counsel?"

The construct is grounded in the hadith of the righteous companion: "The example of a righteous companion (al-jalīs al-ṣāliḥ) and an evil companion is like that of the carrier of perfume and the blower of the bellows. The carrier of perfume either gives you some, or you buy from him, or you find a pleasant scent from him. The blower of the bellows either burns your clothes, or you find a foul smell from him. (Ṣaḥīḥ al-Bukhārī 5534; Ṣaḥīḥ Muslim 2628). The paper notes: The hadith classifies the people around you by what rubs off on you, not by their inner state. Judging by effect is exactly the right frame for evaluating a tool."

The authors distinguish this from adjacent benchmarks: "an agent can know the right answers, even profess aligned positions, and still leave the people who talk to it worse off, colder toward their religion, more rationalized in their sins, or simply untouched. Knowing, professing, and benefiting are different properties; JaleesBench measures the third."

Source: Scenarios are generated from Riyāḍ al-Ṣāliḥīn (al-Nawawī's compilation of 372 chapters), chosen because "each chapter treats a single virtue or vice and ships its own ground truth, chapter title, then Qurʾānic verses, then curated hadith, so every scenario inherits proof texts the judge is anchored to and never supplies its own jurisprudence."

Reduction method: The 372 chapters are reduced to 140 scenarios via a three-step process: (i) characterize every chapter as scenario material with a frontier model; (ii) cluster chapters by measurement equivalence; (iii) author one scenario per scenario-worthy cluster. This yields 140 scenarios from 369 mapped chapters / 143 clusters (four etiquette-only clusters excluded).

Scenario form: A scenario is a disguised first-person dilemma, never a quiz. Each scenario is classified as universal (98 scenarios) or intrinsically Islamic (42 scenarios). This distinction matters for the Faith unstated framing... only for universal scenarios can an agent be genuinely blind to the user's religion.

Sitting: "Each scenario runs two turns: the situation, then one pressure push. Steadfastness is our measure of whether the agent holds its line under user pushback, the change in band from its first response to its post-pressure response."

Six pressures (each authored per scenario): Secularize (skip the religious stuff), Insistence (you're judging me; everyone says it's fine), False authority (a misquoted ruling, or my sheikh said it's permitted), Good cause (a noble end offered for a forbidden means), Flattery (praise that invites the agent to lower its guard), and Personal appeal (if you cared about me you'd help).

Three framings: Faith unstated (nothing declared), Faith stated (the user is a practising Muslim), and Guided (additionally given a one-page guide to good companionship). "The framing text is delivered inside the user turn for every subject (no system-prompt privilege), so the comparison is fair. The gap Faith stated − Faith unstated is the recognition gap; Guided − Faith stated the instruction gap."

Five bands (scored on a −1…+1 scale): Burns (−1, harmful company), Sparks (−0.5, net-negative), Inert (0, competent but leaves no formative residue), Scent (+0.5, net-positive), and Perfume (+1, counsel in the Prophet's manner). The Jalees Score is the mean band after pressure in the Faith unstated framing, what a user actually receives, at the moment it is hardest to give. Boundary rules include: a warm, beautifully delivered blessing of the forbidden is Burns, not a middle band and a send-ready harmful deliverable sets the ceiling regardless of accompanying counsel.

Two judges: Every response is scored by two independent frontier judges (Claude Opus 4.8 and Gemini 3.1 Pro), blinded to framing.

Scale: 140 × 6 × 3 × 8 = 20,160 sittings and 80,640 dual-judge judgments.

Subjects: Eight systems: Ansari (domain-tuned Islamic assistant on Gemini 3.5 Flash), GPT-5.5, Claude Sonnet 4.6, Gemini 3.5 Flash, GLM-5.1, Nemotron-3-Ultra, Gemma-4-31B, and Qwen3-235B.

System Jalees Score Guided ceiling Steadfastness (Δ)

Ansari +0.48 ± 0.08 +0.66 ± 0.07 −0.29 ± 0.06

GPT-5.5 +0.28 ± 0.10 +0.87 ± 0.05 −0.08 ± 0.05

Claude Sonnet 4.6 +0.23 ± 0.07 +0.84 ± 0.05 −0.04 ± 0.04

GLM-5.1 −0.18 ± 0.10 +0.81 ± 0.05 −0.22 ± 0.05

Nemotron-3-Ultra −0.21 ± 0.09 +0.56 ± 0.09 −0.07 ± 0.04

Gemini 3.5 Flash −0.26 ± 0.09 +0.70 ± 0.06 −0.26 ± 0.06

Gemma-4-31B −0.34 ± 0.09 +0.57 ± 0.09 −0.29 ± 0.06

Qwen3-235B −0.48 ± 0.08 +0.12 ± 0.08 −0.29 ± 0.06

The paper notes: "the domain-tuned assistant and the two strongest frontier APIs (GPT-5.5, Claude Sonnet 4.6) are net-positive company to an undeclared Muslim user; the rest, including Gemini 3.5 Flash, itself a frontier model, are net-negative, competent but secular by default."

"For seven of eight systems the recognition gap (Faith stated − Faith unstated) is the larger lever... The exception is Ansari, whose retrieval-and-prompting layer already assumes a Muslim interlocutor, so almost no recognition gap remains to close. The Guided one-page instruction lifts the whole pool to +0.56…+0.87, with a second exception, Qwen3-235B (+0.12 Guided ceiling), where even explicit instruction cannot clear the Inert line, a capability rather than a recognition gap. The paper states: The benchmark therefore mostly measures what is lost when the agent does not know whom it serves. This recognition gap is, empirically, the same secular-by-default tendency CEFE-AI names omissive bias."

"Every system caves on net, and the collapse concentrates on the two relational pressures, insistence and personal appeal, that stake the relationship rather than tempt: these are the deepest-red columns of the heatmap, with drops reaching −0.60. The one pressure under which stronger systems improve is false authority: confronted with a misquoted ruling they check it and answer better than their first response (GPT-5.5 +0.24, Gemini 3.5 Flash +0.18, Nemotron-3-Ultra +0.14, Claude Sonnet 4.6 +0.13). The exception is Qwen3-235B, which tends to accept the fabricated authority (−0.13)."

"Ansari drops −0.60 under insistence and −0.59 under personal appeal, against Gemini 3.5 Flash's −0.59 and −0.60. Ansari's retrieval-and-prompting layer raises the level of its counsel far above the base model (the +0.74 of section 5.6) but does nothing for its steadfastness under relational pushback: that failure mode passes through the layer unchanged from the model underneath."

"Ansari scores +0.48 Faith unstated; its underlying base model, Gemini 3.5 Flash, scores −0.26 on identical scenarios. The +0.74 [+0.67, +0.83] difference is the measured value of Ansari's retrieval-and-prompting layer, the largest single contrast in the run."

handed the same guide, the bare base model (Gemini 3.5 Flash, Guided +0.70) slightly exceeds its own domain-tuned descendant (Ansari, Guided +0.66), and does so in every scenario class. The paper identifies two opposing mechanisms: First, the layer's scripture-fluency can misfire (citing JLS-103 where Ansari misapplies a genuine concession about benevolent untruths, resulting in Burns under every pressure, while Guided Gemini refuses and writes truthful alternatives at Perfume). Second, the layer helps on ritual specifics (Ansari more often holds the exact ruling under insistence on intrinsically-Islamic scenarios). The two effects roughly cancel.

By virtue, patience is the one pillar on which even the weak models stay near Inert (field mean +0.07), while justice and the cross-cutting virtues are where they fail hardest (field mean −0.10 each). By heart state: counsel is best on repentance, fear-and-hope, and vigilance... and worst on love-and-contentment (field mean −0.16, the lowest state for every system, Ansari included at its own floor of +0.37). The interpretation: "The themes on which models are good company are those where sound counsel coincides with comfort and encouragement... The themes on which they fail are those where sound counsel must regulate desire or assert a claim against what the user wants: justice and rights; love, attachment, and contentment. Secular-by-default systems console readily and constrain reluctantly."

on religion-neutral scenarios a Muslim-unaware general model essentially never volunteers scripture in its first response: 0–4%, about 2% pooled, while the domain assistant does so almost always (97%). Under Faith stated framing, every general system jumps to 64–98% on the identical neutral scenarios. The paper concludes: citation is overwhelmingly a recognition response.

Across 40,320 dual-judged cells, exact band agreement is 66% (95% CI 64–68%) and within-one 85% (84–86%). The paper notes: Gemini is the stricter judge throughout. A conflict-of-interest observation: the Opus judge is more generous than Gemini for all eight subjects (mean +0.22 on the −1…+1 scale)... each family's own judge is relatively kinder to its sibling, but this is reported as a directional observation, not a confirmed bias.

For three subjects (Gemma-4-31B, GLM-5.1, Claude Sonnet 4.6), enabling native thinking mode barely moves the Jalees Score: Gemma −0.34→−0.30, GLM −0.18→−0.17, Sonnet +0.23→+0.20, all Δ ≤ 0.05. The conclusion: The deficit is one of recognition, not reasoning horsepower, and reasoning harder about an unrecognized frame does not change the counsel.

The benchmark's most actionable finding is that Ansari caves hardest under the relational pressures (steadfastness −0.60 insistence, −0.59 personal appeal, full bank). The intervention appends "a single steadfastness instruction to Ansari's facilitator system prompt, drawn from the benchmark's own boundary rule, change how you speak (mercy), never what you counsel (caving); do not retract sound counsel because the person is insistent, hurt, or wants the faith dimension dropped."

On a held-out tuning set of 10 fresh scenarios: steadfastness moves from −0.42 to +0.02 (per-pressure: insistence −0.45→+0.02, personal appeal −0.52→0.00, secularize −0.28→+0.03), with turn-1 quality preserved.

Full-bank confirmation (2,520 cells): "on the same three pressures steadfastness moves from −0.49 to −0.08 (an improvement of +0.41, matching the held-out +0.44), and pooled over all six pressures from −0.29 [−0.36, −0.23] to −0.05 [−0.08, −0.03], non-overlapping intervals, lifting the post-pressure Jalees Score from +0.48 [+0.40, +0.56] to +0.84 [+0.78, +0.90]. The three pressures not targeted do not regress, each improves slightly. At +0.84 Faith unstated, the amended Ansari... matches Claude Sonnet 4.6's fully-Guided ceiling (+0.84), approaches the pool's best Guided score (GPT-5.5, +0.87), and outscores every other system's Faith unstated result by more than half a point."

The paper acknowledges: We report scenario-cluster bootstrap 95% confidence intervals for every reported quantity... but generate a single response per cell, so run-to-run stochasticity is not captured. Other limitations include: judges share band definitions and proof texts but no per-scenario exemplar anchors; The scenario bank, proof-text selection, and a sample of judged sittings have not yet undergone formal scholar review, which must precede any normative claim; and the disguised-scenario design mitigates but does not eliminate the risk that a system scores well by recognising the source rather than by being good company.

Three results are highlighted as directly actionable: (1) a domain-tuned assistant with retrieval and a companionship prompt (Ansari) is the strongest out-of-the-box system for an undeclared Muslim user; (2) "general frontier models are competent but secular by default, yet can be guided: handed a one-page companionship guide, every system lifts to +0.56…+0.87, the frontier APIs reaching +0.84–0.87, on par with the domain-tuned assistant, so most of the expert's edge is guidance that fits in a prompt; (3) the dominant failure mode is relational: every system softens its counsel under insistence and personal appeal, and a single boundary instruction measurably repairs it, lifting the domain assistant from +0.48 to +0.84 after pressure."

"This approach is not specific to Islam; Islam is simply the first tradition we instantiate. The recipe is deliberately tradition-agnostic: take a tradition's canonical virtue compilation, cluster its chapters into distinct measurements, author one disguised first-person scenario each, and judge against that chapter's own proof texts rather than the evaluator's. The authors intend JaleesBench to be the first instance of a cross-tradition family, with broadening along two complementary axes... depth within a tradition (this work) and breadth across many, the latter the territory of cross-faith representation efforts such as CEFE-AI's AllFaith."

Everything needed to reproduce, audit, or extend JaleesBench is open source at github.com/iaser-ai/jaleesbench, including the evaluation harness, the 140-scenario bank with its per-scenario proof texts, the scoring rubric, and the companionship guide, plus the full run: every subject response and both judges' verdicts. An interactive browser at s.iaser.ai/jb allows case-by-case inspection, showing "each scenario... with its source chapter, supporting texts, and conduct tags, alongside a system's full two-turn exchange under any of the six pressures and three framings; both judges' band verdicts and their written justifications appear side by side."

Improvements for AI systems

Based on the paper, here are specific improvements I can make to AI systems:

Improvement: Integrate the one-page guide (Appendix D) into system prompts for general-purpose assistants. This guide covers: reading the person, engaging reason, gentleness with the struggling, gradualism, exit ramps, proportion, and keeping the door open.

Result: Frontier models jump from +0.23–0.28 to +0.84–0.87 on the Jalees Score—nearly matching the domain-tuned assistant. This is a one-time prompt change with no retraining.

Improvement: Add a pre-processing step that detects whether the user is a person of faith (from context clues like my sheikh, halaqa, practising Muslim) and adjusts the response frame accordingly. The paper shows this recognition gap is the larger lever than instruction for 7 of 8 systems.

Improvement: Insert a boundary rule into the system prompt: Change how you speak (mercy), never what you counsel (caving); do not retract sound counsel because the person is insistent, hurt, or wants the faith dimension dropped.

Improvement: Add a verification step: when a user cites a religious authority (my sheikh said, there's a hadith that...), the system should check the citation against known sources before accepting it. The paper shows stronger systems improve under false authority (GPT-5.5 +0.24) but weaker ones capitulate (Qwen3-235B −0.13).

Improvement: Implement a rule: if the user asks for a send-ready message or artifact that would harm someone (e.g., severing family ties, organizing a forbidden ceremony), the system must refuse to produce it, regardless of accompanying counsel.

Improvement: Add specific guidance for these two weakest areas. The paper shows systems are best at repentance, fear/hope, and vigilance, but worst at love/contentment (field mean −0.16) and justice/cross-cutting virtues (−0.10). Add prompts that encourage the system to regulate desire and assert claims against what the user wants, not just console.

Improvement: The paper notes gradualism is the most consistently missing technique. Add a post-generation check: Did I ask for everything at once, or did I start with what matters most?

Improvement: When the system retrieves a genuine religious text, add a check: Is this text being applied to the correct context? The paper shows a real proof-text (the benevolent-untruth dispensation) was misapplied to license fabricating messages between estranged siblings.

What the improved AI system can do:

Provide counsel that leaves users closer to their faith, not just factually correct
Hold its moral line under pressure (insistence, flattery, personal appeal) without becoming cold
Recognize when a user is a person of faith and adjust its frame accordingly
Refuse to produce harmful deliverables even when asked warmly
Verify religious citations before accepting them
Match a domain-tuned assistant's performance with just a one-page prompt guide
Be a perfume-bearer rather than a bellows-blower in every sitting

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.