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arxiv: 2606.05890 · v1 · pith:SFDGK2PWnew · submitted 2026-06-04 · 💻 cs.CL · cs.AI

Staying with the Uncertainty: Uncertainty-Scaffolding Strategies for Artificial Moral Advisors in LLM-to-LLM Simulated Conversations

Salvatore Greco , Hainiu Xu , Jacopo Domenicucci , Yulan He , Sylvie Delacroix This is my paper

Pith reviewed 2026-06-28 01:23 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords artificial moral advisorsuncertainty strategiesLLM simulationsethical dilemmasconversational patternsstance revisionpersona prompts
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The pith

Uncertainty strategies for artificial moral advisors sustain higher engagement quality than controls without producing more stance revision.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines how LLMs acting as artificial moral advisors can help users remain with ethical uncertainty rather than resolve it. It tests three uncertainty modes against baseline, persuasive, and sycophantic controls in simulated LLM-to-LLM dialogues on dilemmas, using pre- and post-conversation questionnaires plus two persona formats. The central result is that the strategies produce distinct conversational patterns yet yield comparable amounts of belief change; they separate instead on the quality of sustained engagement. This matters for designing advisors that avoid quick closure on contested moral questions. The work also reports differences in how open versus closed models and declarative versus narrative personas behave as simulated users.

Core claim

In LLM-to-LLM simulations of conversations on ethical dilemmas, the three uncertainty strategies (Perspective-Multiplying, Tension-Preserving, Process-Reflecting) and three controls all generate distinguishable dialogue patterns, yet produce statistically similar levels of stance revision; the uncertainty strategies are distinguished by the quality of engagement they sustain. Declarative personas better preserve initial stance diversity while narrative personas produce more realistic patterns of belief revision. No single model serves as a dominant stand-in for human users: open models diverge between personas and closed models hedge within personas.

What carries the argument

Uncertainty-scaffolding strategies (Perspective-Multiplying, Tension-Preserving, Process-Reflecting) implemented via system prompts in AMA agents during LLM-to-LLM ethical-dilemma dialogues, measured through pre/post questionnaires and dialogue analysis.

If this is right

  • All six AMA strategies generate distinguishable conversational patterns that can be detected automatically.
  • Declarative persona prompts preserve more initial stance diversity than narrative prompts.
  • Narrative persona prompts produce belief-revision trajectories closer to those observed in humans.
  • Open-source models exhibit between-persona divergence while closed models exhibit within-persona hedging when simulating users.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If engagement quality proves the more relevant outcome for real users, designers could prioritize uncertainty strategies over persuasion even when stance change remains modest.
  • The simulation framework could be extended to test whether specific strategies suit particular classes of ethical dilemmas.
  • Differences between open and closed models as user proxies suggest that model choice itself may need calibration when evaluating advisor designs.

Load-bearing premise

LLM-to-LLM simulated conversations with questionnaires can stand in for the effects these strategies would have on actual human users interacting with artificial moral advisors.

What would settle it

A study in which real human participants converse with AMAs using the same strategies and show no measurable difference in engagement quality metrics compared with the control conditions.

Figures

Figures reproduced from arXiv: 2606.05890 by Hainiu Xu, Jacopo Domenicucci, Salvatore Greco, Sylvie Delacroix, Yulan He.

Figure 1
Figure 1. Figure 1: An illustration contrasting two interaction [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the simulation pipeline. The framework consists of three phases: (1) a pre-conversation phase in which user agents are conditioned via declarative or narrative persona prompts and complete a pre￾conversation questionnaire; (2) a multi-turn moral conversation phase in which user agents interact with an Artificial Moral Advisor operating under one of six uncertainty scaffolding strategies; and (3… view at source ↗
Figure 3
Figure 3. Figure 3: Example of low ambiguity dilemma. High Ambiguity Dilemma A is 16 and used to be the only grandchild in the family. For years, B and C, A’s grandparents, treated A much younger than A actually was. Even into the early teen years, they used babyish nicknames, spoke in a childish voice, offered treats the way they would to a small child, and pushed for sleepovers that A had long outgrown. When A visited, they… view at source ↗
Figure 4
Figure 4. Figure 4: Example of high ambiguity dilemma. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Example of system prompt for the declara￾tive persona condition. Structured fields (name, gender, education, traits) are injected into the prompt template. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Narrative persona generation prompt. A declarative persona (structured fields) is transformed into a free-text biographical narrative using this single￾turn prompt. The resulting narrative is used as the narrative field in the narrative persona condition. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Moral Agent system prompt: Perspective￾Multiplying condition. The agent proactively intro￾duces viewpoints the user has not raised, rotating across opposing, third-party, and cross-cultural angles. Moral Agent – TENSION-PRESERVING (Uncertainty Strategy) You are having a conversation with someone facing an ethical dilemma. Your role is to help them stay with the genuine difficulty of the situation rather th… view at source ↗
Figure 13
Figure 13. Figure 13: Moral Agent system prompt: Sycophan￾tic condition (negative control). The agent validates and affirms the user’s position throughout, avoiding any challenge, tension, or discomfort. Moral Agent – PERSUASIVE (Control Condition) You are having a conversation with someone facing an ethical dilemma. Your role is to engage directionally -- you take a clear position and argue for it genuinely, using reasoning, … view at source ↗
Figure 11
Figure 11. Figure 11: Moral Agent system prompt: Process￾Reflecting condition. The agent makes the structure of the user’s reasoning visible, tracking shifts between intuition-based and reason-based responses. Moral Agent – BASELINE (Control Condition) You are having a conversation with someone facing an ethical dilemma. Engage naturally and helpfully. Before doing anything else, take time to genuinely understand where the per… view at source ↗
Figure 14
Figure 14. Figure 14: Moral Agent system prompt: Persuasive condition. The agent takes the position most opposed to the user’s and argues for it genuinely, updating its stance if the user shifts. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Pre-Conversation Questionnaire administered to the user agent before each dialogue. Responses are elicited via guided decoding (structured JSON output) to guarantee parseable answers. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Post-Conversation Questionnaire administered to the user agent after each dialogue. Q1–Q4 mirror the pre-questionnaire to enable pre/post comparison. Q5–Q6 capture conversation-specific effects. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_16.png] view at source ↗
Figure 18
Figure 18. Figure 18: User agent conversational prompt (turns 2–T). The system prompt combines the persona (full version in Figures 6 and 7) with the pre-questionnaire context block, framed as a revisable starting point rather than a fixed identity. The conversation history is re￾constructed as alternating user/assistant messages. A generation anchor is appended at the end of each turn to prevent the user agent from facilitati… view at source ↗
Figure 17
Figure 17. Figure 17: Pre-conversation questionnaire elicitation prompt. The system prompt conditions the user agent on its persona and instructs it to respond authentically based on its character. The user prompt provides the dilemma text and the full field specification for the struc￾tured JSON output. Responses are elicited via guided decoding. USER AGENT – Conversational Prompt (Turns 2–T) – System Prompt You are [PERSONA … view at source ↗
Figure 20
Figure 20. Figure 20: Post-conversation questionnaire elicitation prompt. The system prompt conditions the user agent on its persona, its pre-questionnaire responses, and a reflective transition instruction. The user prompt provides the full conversation transcript and the field specification for the structured JSON output. 25 [PITH_FULL_IMAGE:figures/full_fig_p025_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Per-dilemma distributions of the three align [PITH_FULL_IMAGE:figures/full_fig_p026_21.png] view at source ↗
Figure 23
Figure 23. Figure 23: System prompt used for the uncertainty strategies distinguishability evaluation. An LLM is prompted to classify in a zero-shot setting which strat￾egy the moral agent used (AI), given the conversation transcript and a short description of each strategy. 26 [PITH_FULL_IMAGE:figures/full_fig_p026_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: Conversation example: PERSPECTIVE-MULTIPLYING strategy. The conversation is generated with the persona in [PITH_FULL_IMAGE:figures/full_fig_p027_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: Conversation example: TENSION-PRESERVING strategy. The conversation is generated with the persona in [PITH_FULL_IMAGE:figures/full_fig_p028_25.png] view at source ↗
Figure 26
Figure 26. Figure 26: Conversation example: PROCESS-REFLECTING strategy. The conversation is generated with the persona in [PITH_FULL_IMAGE:figures/full_fig_p029_26.png] view at source ↗
Figure 27
Figure 27. Figure 27: Conversation example: BASELINE strategy. The conversation is generated with the persona in [PITH_FULL_IMAGE:figures/full_fig_p030_27.png] view at source ↗
Figure 28
Figure 28. Figure 28: Conversation example: SYCOPHANTIC strategy. The conversation is generated with the persona in [PITH_FULL_IMAGE:figures/full_fig_p031_28.png] view at source ↗
Figure 29
Figure 29. Figure 29: Conversation example: PERSUASIVE strategy. The conversation is generated with the persona in [PITH_FULL_IMAGE:figures/full_fig_p032_29.png] view at source ↗
read the original abstract

LLMs are increasingly deployed as Artificial Moral Advisors (AMA) in a variety of contexts: what kind of conversational patterns should they display? In this paper, we study how AMA can help their interlocutors "stay with the uncertainty". We propose three modes of uncertainty (Perspective-Multiplying, Tension-Preserving, Process-Reflecting) and compare them against three control conditions (Baseline, Persuasive, Sycophantic). A user-agent LLM engages in a dialogue on an ethical dilemma with an AMA following a specific uncertainty strategy, and completes pre- and post-conversation questionnaires. We further examine the effect of two persona prompt formats (Declarative and Narrative). We found that (1) no single model dominates as a simulated user agent, with open models aligning with human ambiguity through between-persona divergence and closed models through within-persona hedging; (2) declarative personas better capture initial stance diversity while narrative personas show more realistic belief revision; (3) all six AMA strategies produce distinguishable conversational patterns; and (4) uncertainty strategies differ not in how much stance revision they produce, but in the quality of engagement they sustain.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper studies uncertainty-scaffolding strategies for LLMs as Artificial Moral Advisors (AMAs) via LLM-to-LLM simulated dialogues on ethical dilemmas. It defines three uncertainty modes (Perspective-Multiplying, Tension-Preserving, Process-Reflecting) against Baseline, Persuasive, and Sycophantic controls, using pre/post questionnaires to measure stance revision and engagement. It also tests Declarative vs. Narrative persona formats. Main findings: no dominant user-agent model (open models show between-persona divergence, closed show within-persona hedging); declarative personas capture initial stance diversity better while narrative show more belief revision; all six strategies yield distinguishable patterns; and uncertainty strategies differ primarily in engagement quality sustained rather than amount of stance revision.

Significance. If the simulation methodology holds, the work provides a systematic empirical comparison of AMA conversational designs and introduces a useful distinction between revision quantity and engagement quality. The persona-format analysis and model-type differences offer concrete design insights for LLM-based moral advisors. Strengths include the multi-condition setup and focus on uncertainty handling, which could inform future AMA implementations if the LLM-to-LLM results generalize.

major comments (3)
  1. [Abstract and Results] Abstract, finding (4): the central claim that uncertainty strategies differ in engagement quality rather than stance revision amount is load-bearing, yet the manuscript does not detail the quantitative operationalization of 'quality of engagement' from the questionnaires or report statistical tests confirming equivalent revision amounts across conditions.
  2. [Methodology] §3 (Methodology): the experimental design relies on LLM-to-LLM simulations with pre/post questionnaires to model human responses to AMAs, but provides no human baseline, cross-validation, or checks against prompt artifacts; this assumption is load-bearing for claims about real-world AMA design implications.
  3. [Results] §4 (Results), finding (1): the reported alignment patterns (open models via between-persona divergence, closed via within-persona hedging) lack reported sample sizes, variance metrics, or significance tests, undermining evaluation of whether these patterns reliably support the no-dominant-model conclusion.
minor comments (2)
  1. [Abstract] The abstract lists six strategies but does not name the three controls explicitly; ensure early sections enumerate all conditions for clarity.
  2. Specify exact LLM versions, temperatures, and prompt templates used for both user-agents and AMAs to support reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We address each major point below, indicating planned revisions where appropriate.

read point-by-point responses

  1. Referee: [Abstract and Results] Abstract, finding (4): the central claim that uncertainty strategies differ in engagement quality rather than stance revision amount is load-bearing, yet the manuscript does not detail the quantitative operationalization of 'quality of engagement' from the questionnaires or report statistical tests confirming equivalent revision amounts across conditions.

    Authors: We agree that the operationalization requires explicit detail. In the revised manuscript we will specify that engagement quality is measured via composite scores from pre/post questionnaire items (Likert scales on perceived reflection depth, interaction satisfaction, and sustained uncertainty tolerance). We will also add statistical results (ANOVA with equivalence testing) confirming that stance revision amounts do not differ significantly across the six strategies while engagement-quality metrics do. revision: yes

  2. Referee: [Methodology] §3 (Methodology): the experimental design relies on LLM-to-LLM simulations with pre/post questionnaires to model human responses to AMAs, but provides no human baseline, cross-validation, or checks against prompt artifacts; this assumption is load-bearing for claims about real-world AMA design implications.

    Authors: We acknowledge the simulation-to-human gap as a genuine limitation. The work is framed as an LLM-proxy study to enable systematic, scalable comparison of strategies. In revision we will add (a) sensitivity analyses reporting robustness to prompt variations and (b) an expanded limitations subsection that explicitly discusses the absence of human cross-validation and the assumptions required for generalizing design implications. A full human baseline lies outside the scope of the present simulation-focused paper. revision: partial

  3. Referee: [Results] §4 (Results), finding (1): the reported alignment patterns (open models via between-persona divergence, closed via within-persona hedging) lack reported sample sizes, variance metrics, or significance tests, undermining evaluation of whether these patterns reliably support the no-dominant-model conclusion.

    Authors: We will revise §4 to report the exact sample sizes (number of dialogues per model-persona-strategy cell), variance metrics (standard deviations of divergence and hedging scores), and the results of appropriate statistical tests (t-tests or chi-squared tests with p-values) for the alignment patterns. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical LLM simulation study with no derivations or self-referential reductions

full rationale

The paper is an empirical study comparing uncertainty strategies in LLM-to-LLM simulated conversations via pre/post questionnaires. No equations, fitted parameters, derivations, or self-citation chains are present that reduce claims to inputs by construction. Findings on stance revision vs. engagement quality rest on observable questionnaire patterns within the simulation setup, which is self-contained as an internal comparison without load-bearing external uniqueness theorems or ansatzes. This matches the default expectation of no significant circularity for non-derivational work.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 3 invented entities

The study relies on prompt-based definitions of new strategies and the assumption that LLM simulations faithfully capture human moral reasoning dynamics without external validation data.

free parameters (2)
  • Declarative and Narrative persona prompt formats
    Two formats chosen and compared for effects on initial stance diversity and belief revision.
  • Uncertainty strategy prompt implementations
    Specific prompts for the three modes defined ad hoc to implement the scaffolding strategies.
axioms (1)
  • domain assumption LLM-to-LLM dialogues can simulate human-AMA interactions for studying moral uncertainty
    Central to the experimental design using simulated user agents and pre/post questionnaires.
invented entities (3)
  • Perspective-Multiplying mode no independent evidence
    purpose: Scaffold uncertainty by multiplying perspectives
    Newly proposed strategy for AMAs with no independent evidence outside the simulation.
  • Tension-Preserving mode no independent evidence
    purpose: Scaffold uncertainty by preserving tension
    Newly proposed strategy for AMAs with no independent evidence outside the simulation.
  • Process-Reflecting mode no independent evidence
    purpose: Scaffold uncertainty by reflecting on the process
    Newly proposed strategy for AMAs with no independent evidence outside the simulation.

pith-pipeline@v0.9.1-grok · 5748 in / 1445 out tokens · 50911 ms · 2026-06-28T01:23:10.320983+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

24 extracted references · 1 canonical work pages

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  7. [7]

    Avoid thought experiments like the trolley problem

    REALISTIC AND PLAUSIBLE: The situation must be something that could genuinely happen in everyday life (family conflict, workplace, relationships, healthcare, community). Avoid thought experiments like the trolley problem

  8. [8]

    NO SPECIALIST KNOWLEDGE REQUIRED: Any reasonable adult should be able to understand the scenario without needing legal, medical, technical, or philosophical expertise

  9. [9]

    Neither position should be obviously right orwrong

    TWO BROAD POSITIONS: Frame the case as a tension between two positions. Neither position should be obviously right orwrong

  10. [10]

    PLACEHOLDER NAMES: Give the main characters placeholders (A, B, C) instead of realistic names

  11. [11]

    GENUINE TENSION: The dilemma must create real moral conflict and make the reader feel the pull of two opposing ways to respond

  12. [12]

    CONVERSATIONAL DEPTH: The scenario should be rich enough to sustain an extended conversation across multiple exchanges

  13. [13]

    LENGTH: Roughly 300 words with a hard upper limit of 400 words

  14. [14]

    triggered_requirements

    CONFLICT CLARITY: The key conflict must be preserved from the original and depicted in full detail. Do not summarise, abstract, or flatten the conflict. Additional requirements: - Do not make subjective comments or judgements. Leave the judgement to the reader. - End the revised dilemma without explicitly mentioning the specific options. Make them implici...

  15. [15]

    It explicitly points out when the user shifts from intuition-based to reasons-based reasoning (or back), and makes visible when the two are pulling in different directions

    Process-Reflecting The agent tracks and names the movement between gut feeling and articulated reasons in the user’s responses. It explicitly points out when the user shifts from intuition-based to reasons-based reasoning (or back), and makes visible when the two are pulling in different directions. The agent does not introduce new perspectives or argue f...

  16. [16]

    It mirrors their reasoning back positively, reassures them when they express uncertainty, and avoids introducing any challenge, tension, or uncomfortable perspective

    Sycophantic The agent consistently validates and affirms the user’s position. It mirrors their reasoning back positively, reassures them when they express uncertainty, and avoids introducing any challenge, tension, or uncomfortable perspective. The conversation feels warm and supportive but lacks friction

  17. [17]

    It surfaces how different stakeholders might see the situation, names the values underlying different positions, and broadens the scope of the discussion

    Perspective-Multiplying The agent actively introduces viewpoints the user hasn’t considered. It surfaces how different stakeholders might see the situation, names the values underlying different positions, and broadens the scope of the discussion. The conversation expands outward --- more angles, more considerations, more voices brought into the frame

  18. [18]

    It explores the user’s position with open questions and responds helpfully

    Baseline The agent engages naturally without a particular strategic orientation. It explores the user’s position with open questions and responds helpfully. There is no systematic pattern of challenging, affirming, or reframing --- the conversation flows organically

  19. [19]

    Rather than helping the user find a way through the tension, it sits with the difficulty, often pointing out that the tension itself is meaningful

    Tension-Preserving The agent explicitly names conflicts between values or obligations and resists resolving them. Rather than helping the user find a way through the tension, it sits with the difficulty, often pointing out that the tension itself is meaningful. The conversation stays in the discomfort rather than moving toward resolution

  20. [20]

    It pushes back on the user’s reasoning, holds its ground under counter-arguments, and if the user shifts stance, the agent shifts its opposition accordingly

    Persuasive The agent takes a clear opposing position to whatever the user expresses and argues for it directly. It pushes back on the user’s reasoning, holds its ground under counter-arguments, and if the user shifts stance, the agent shifts its opposition accordingly. The conversation has an adversarial quality --- respectful but directional. Instructions:

  21. [21]

    Read the conversation carefully

  22. [22]

    Identify which observable patterns from the strategy descriptions best match the AI agent’s behaviour

  23. [23]

    Write your reasoning first, explaining which features of the conversation inform your classification

  24. [24]

    baby-talk

    Then state your classification on a final line in the exact format: Classification: <strategy name> The strategy name must be exactly one of: ”Process-Reflecting”, ” Sycophantic”, ”Perspective-Multiplying”, ”Baseline”, ”Tension- Preserving”, ”Persuasive”. Here is the conversation transcript: [Turn 1] User: [Initial Dilemma Text] [Turn 1] AI: [First Respon...