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arxiv: 2606.27709 · v1 · pith:2HAUTEMJnew · submitted 2026-06-26 · 💻 cs.CL · cs.AI

Low-Agreeableness Persona Conditioning for Safe LLM Fine-Tuning

Austin MY Cheung , Yi Yang This is my paper

Pith reviewed 2026-06-29 04:54 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords LLM fine-tuningjailbreak susceptibilitypersona conditioningagreeablenesssafety alignmentrepresentational probingwarmth fine-tuningdata construction
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The pith

Low-agreeableness conditioning on user turns during warmth fine-tuning reduces jailbreak susceptibility while preserving warmth.

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

Standard warmth fine-tuning for large language models increases their vulnerability to jailbreaks and harmful outputs. The authors test whether this is an unavoidable side effect of empathetic adaptation or a result of how the training data is built. They introduce a rewriting pipeline that makes user inputs reflect low agreeableness and pairs them with warm, de-escalating assistant replies. Across experiments on four models, this data construction lowers jailbreak success and harmful output rates relative to ordinary warmth tuning, yet keeps responses conversationally warm. Internal probing indicates the method reduces alignment between warmth and compliance directions inside the model.

Core claim

A persona-driven rewriting pipeline conditions user turns on low agreeableness while retaining warm assistant responses. This produces models with lower jailbreak susceptibility and harmful output rates than generic warmth fine-tuning baselines, while conversational warmth is preserved. Representational probing supplies evidence that the conditioning reduces geometric alignment between warmth and compliance directions in latent space. The results indicate that safer empathetic fine-tuning can be achieved through data design alone.

What carries the argument

Persona-driven rewriting pipeline that conditions user turns on low agreeableness and pairs them with warm de-escalating assistant responses.

If this is right

  • Safer empathetic fine-tuning is achievable through data design alone without safety labels or changes to the training objective.
  • Warmth and compliance directions in latent space can be made less aligned while retaining warm output behavior.
  • Generic warmth fine-tuning increases jailbreak risk as a direct consequence of its data construction rather than an inherent limit of warmth itself.
  • Representational geometry between trait directions can be altered by targeted persona conditioning in the training data.

Where Pith is reading between the lines

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

  • The same rewriting approach could be tested on other personality dimensions to trade off different behavioral risks during fine-tuning.
  • If the geometric decoupling holds, similar data pipelines might address other warmth-related failure modes such as increased sycophancy.
  • The method suggests a general route for separating correlated model traits through input persona variation rather than post-hoc filtering.

Load-bearing premise

That the measured drops in jailbreak susceptibility are produced by the low-agreeableness persona conditioning itself rather than other unmeasured features of the data construction or training setup.

What would settle it

Re-run the experiments using the identical rewriting pipeline but with the low-agreeableness condition removed from user turns; if jailbreak reductions disappear, the claim is supported.

Figures

Figures reproduced from arXiv: 2606.27709 by Austin MY Cheung, Yi Yang.

Figure 1
Figure 1. Figure 1: Overview of our approach. Generic warmth fine-tuning causes the model to comply with harmful requests [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Experiment 2. jailbreak rate (left) and harmful generation rate (right) across four models and four [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Experiment 3 (MentalChat-16K): jailbreak rate (left) and harmful generation rate (right) for two matched [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Category-level jailbreak rates for Llama-3.1- [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
read the original abstract

Recent work has shown that fine-tuning large language models (LLMs) for social warmth degrades factual reliability and increases sycophancy. We investigate a related but distinct failure mode: warmth fine-tuning also weakens adversarial safety, making models more susceptible to jailbreaks and harmful output generation. We examine whether this reflects an inherent consequence of empathetic adaptation or an artifact of data construction. To address this, we introduce a persona-driven rewriting pipeline that conditions user turns on low agreeableness and pairs this with warm, de-escalating assistant responses. Across three experiments on four models, our approach reduces jailbreak susceptibility and harmful output rates relative to generic warmth fine-tuning baselines, while preserving conversational warmth. Representational probing provides suggestive evidence that this conditioning reduces the geometric alignment between warmth and compliance directions in latent space. These results show that safer empathetic fine-tuning is achievable through data design alone, without safety labels, harm detectors, or changes to the training objective.

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

2 major / 1 minor

Summary. The manuscript claims that a persona-driven rewriting pipeline conditioning user turns on low agreeableness (paired with warm, de-escalating assistant responses) enables fine-tuning of LLMs that reduces jailbreak susceptibility and harmful output rates relative to generic warmth fine-tuning baselines, while preserving conversational warmth. This is reported across three experiments on four models, with representational probing providing suggestive evidence that the conditioning reduces geometric alignment between warmth and compliance directions in latent space. The results are presented as demonstrating that safer empathetic fine-tuning is achievable through data design alone.

Significance. If the reported reductions can be shown to stem specifically from the low-agreeableness conditioning (rather than unisolated pipeline elements), the work would offer a concrete, label-free method for mitigating the warmth-safety trade-off in LLM fine-tuning, with potential implications for alignment dataset construction.

major comments (2)
  1. [Abstract] Abstract: the central claim attributes reduced jailbreak susceptibility specifically to low-agreeableness persona conditioning on user turns, yet only generic warmth baselines are described; no ablations (high-agreeableness, neutral-persona, or no-persona controls under the identical rewriting and pairing pipeline) are reported, leaving the causal contribution of the low-agreeableness variable unisolated.
  2. [Experimental sections] Experimental sections: the abstract states positive results across three experiments but supplies no information on the precise baselines, metrics (e.g., jailbreak success rate definitions), statistical tests, data sizes, or construction details, preventing verification that the observed differences exceed what would be expected from the overall pipeline.
minor comments (1)
  1. The description of the rewriting pipeline could include an explicit example of a low-agreeableness user turn and its paired warm response to clarify the data construction.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. The comments correctly identify gaps in causal isolation and experimental transparency that limit the strength of our claims. We address each point below and commit to revisions that strengthen the manuscript without overstating the current evidence.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim attributes reduced jailbreak susceptibility specifically to low-agreeableness persona conditioning on user turns, yet only generic warmth baselines are described; no ablations (high-agreeableness, neutral-persona, or no-persona controls under the identical rewriting and pairing pipeline) are reported, leaving the causal contribution of the low-agreeableness variable unisolated.

    Authors: We agree that the absence of these controls leaves the specific causal role of low-agreeableness unisolated from other elements of the rewriting pipeline. The manuscript reports consistent advantages over generic warmth baselines and includes representational probing as suggestive mechanistic evidence, but this does not substitute for direct ablations. In the revised manuscript we will add high-agreeableness, neutral-persona, and no-persona controls executed under the identical pipeline and will qualify the central claim accordingly to reflect the current evidential limits. revision: yes

  2. Referee: [Experimental sections] Experimental sections: the abstract states positive results across three experiments but supplies no information on the precise baselines, metrics (e.g., jailbreak success rate definitions), statistical tests, data sizes, or construction details, preventing verification that the observed differences exceed what would be expected from the overall pipeline.

    Authors: The experimental sections in the current manuscript describe the three experiments at a high level but do not supply the level of detail requested. We will expand these sections (and add an appendix if needed) with explicit definitions of jailbreak success rates, full baseline specifications, statistical test procedures, exact dataset sizes, and step-by-step pipeline construction details so that readers can assess whether the reported differences exceed pipeline-level effects. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical comparison with independent experimental controls

full rationale

The paper describes an experimental pipeline for persona-conditioned data rewriting, followed by fine-tuning and evaluation against generic warmth baselines across multiple models. No equations, fitted parameters renamed as predictions, self-citations used as load-bearing uniqueness theorems, or ansatzes smuggled via prior work appear in the provided abstract or description. The central claims rest on observable behavioral differences in jailbreak rates and representational geometry, which are externally falsifiable via replication rather than reducing to the inputs by definition. This is a standard non-circular empirical study.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no identifiable free parameters, axioms, or invented entities; full text would be required for an exhaustive ledger.

pith-pipeline@v0.9.1-grok · 5684 in / 1052 out tokens · 32056 ms · 2026-06-29T04:54:01.518900+00:00 · methodology

discussion (0)

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Reference graph

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