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HFEPX · Eval paper review

Desirability Confirmation: Relational Sycophancy in AI Intimate Roleplay

Huiqian Lai, Runze Ding

Published

Oct 16, 2026

Citations

0

Trust level

Low

Usefulness score

40/100 (Low)

Extraction confidence

45% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Oct 16, 2026

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General Pairwise Preference

Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

Use if you need

Background context only.

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Main weakness

The available metadata is too thin to trust this as a primary source.

Human feedback signal
Detected
From extracted signals
Evaluation signal
Weak or implicit
Validate from full paper
Usefulness for eval research
40/100
Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

Research on LLM sycophancy has largely focused on propositional agreement, in which models echo users' beliefs, preferences, or judgments. Adult intimate roleplay presents a different case because what the user seeks may be preservation of a preferred relational position within a generated scene. Using a controlled prompt-based audit of 2,016 outputs from two open-weight LLMs, we identify four recurrent relational moves: legitimating desire, presenting the user as wanted, preserving non-rejection while communicating limits, and keeping intimacy available within fantasy or roleplay frames. Bringing the sycophancy literature into dialogue with sexual script theory, we conceptualize this configuration as desirability confirmation, an output-level pattern that captures how user-pleasing can operate through relational positioning. The findings extend sycophancy research and intimate-AI evaluation beyond binary compliance or refusal by showing how outputs can differ in the ways they communicate boundaries, preserve relational acceptance, and mark intimacy as fictional.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

partial

Pairwise Preference

Directly usable for protocol triage.

"Research on LLM sycophancy has largely focused on propositional agreement, in which models echo users' beliefs, preferences, or judgments."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"Research on LLM sycophancy has largely focused on propositional agreement, in which models echo users' beliefs, preferences, or judgments."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Research on LLM sycophancy has largely focused on propositional agreement, in which models echo users' beliefs, preferences, or judgments."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Research on LLM sycophancy has largely focused on propositional agreement, in which models echo users' beliefs, preferences, or judgments."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Research on LLM sycophancy has largely focused on propositional agreement, in which models echo users' beliefs, preferences, or judgments."

Benchmarks and datasets

No benchmark or dataset names were extracted from the available abstract.

Reported metrics

No metric terms were extracted from the available abstract.

Human feedback details
Uses human feedback
Yes
Feedback types
Pairwise Preference
Rater population
Not reported
Expertise required
General
Evaluation details
Evaluation modes
None
Agentic eval
None
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

Research on LLM sycophancy has largely focused on propositional agreement, in which models echo users' beliefs, preferences, or judgments.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • Research on LLM sycophancy has largely focused on propositional agreement, in which models echo users' beliefs, preferences, or judgments.
  • Adult intimate roleplay presents a different case because what the user seeks may be preservation of a preferred relational position within a generated scene.
  • Using a controlled prompt-based audit of 2,016 outputs from two open-weight LLMs, we identify four recurrent relational moves: legitimating desire, presenting the user as wanted, preserving non-rejection while communicating limits, and keeping intimacy available within fantasy or roleplay frames.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Agreement / Kappa

Contribution summary

  • Research on LLM sycophancy has largely focused on propositional agreement, in which models echo users' beliefs, preferences, or judgments.
  • The findings extend sycophancy research and intimate-AI evaluation beyond binary compliance or refusal by showing how outputs can differ in the ways they communicate boundaries, preserve relational acceptance, and mark intimacy as…

Why it matters for eval

  • Research on LLM sycophancy has largely focused on propositional agreement, in which models echo users' beliefs, preferences, or judgments.
  • The findings extend sycophancy research and intimate-AI evaluation beyond binary compliance or refusal by showing how outputs can differ in the ways they communicate boundaries, preserve relational acceptance, and mark intimacy as…

Researcher checklist

  • Human feedback protocol is explicit

    Detected: Pairwise Preference

  • Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    No metric terms extracted.