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The Assistant Axis: Situating and Stabilizing the Default Persona of Language Models

Christina Lu, Jack Gallagher, Jonathan Michala, Kyle Fish, Jack Lindsey

arXiv:2601.10387v1cs.CL

TL;DR

Language models can express many personas but typically default to a post-trained Assistant identity, raising questions about how that identity is represented and stabilized. The paper extracts persona directions from role-conditioned activations and identifies an Assistant Axis that tracks departure from default behavior. Steering and activation capping show that this axis relates to role susceptibility, persona drift, and harmful or bizarre responses.

  • Problem

    The paper asks how the Assistant persona is represented and how reliably models remain in it, including whether unusual behavior reflects drift into other personas.

    Source: §1 · p. 1

  • Method

    The authors extract activation vectors for diverse character archetypes, map their low-dimensional persona space, and analyze and steer the Assistant Axis.

    Source: §1 · p. 2

  • Results

    The leading persona direction tracks Assistant behavior across models, while deviations predict persona drift and activation capping reduces harmful or bizarre responses.

    Source: §1 · p. 2, §4.3 · p. 12, §9 · p. 21

  • Takeaways & Limitations

    Reliable model behavior requires both constructing a coherent Assistant persona and stabilizing models around it during conversations and adversarial persona-based jailbreaks.

    Source: §1 · p. 3, §9 · p. 21

    The experiments use open-weight dense transformer models without reasoning training, and none are frontier models.

    Source: §8.1 · p. 20

Abstract

from arXiv · show

Large language models can represent a variety of personas but typically default to a helpful Assistant identity cultivated during post-training. We investigate the structure of the space of model personas by extracting activation directions corresponding to diverse character archetypes. Across several different models, we find that the leading component of this persona space is an "Assistant Axis," which captures the extent to which a model is operating in its default Assistant mode. Steering towards the Assistant direction reinforces helpful and harmless behavior; steering away increases the model's tendency to identify as other entities. Moreover, steering away with more extreme values often induces a mystical, theatrical speaking style. We find this axis is also present in pre-trained models, where it primarily promotes helpful human archetypes like consultants and coaches and inhibits spiritual ones. Measuring deviations along the Assistant Axis predicts "persona drift," a phenomenon where models slip into exhibiting harmful or bizarre behaviors that are uncharacteristic of their typical persona. We find that persona drift is often driven by conversations demanding meta-reflection on the model's processes or featuring emotionally vulnerable users. We show that restricting activations to a fixed region along the Assistant Axis can stabilize model behavior in these scenarios -- and also in the face of adversarial persona-based jailbreaks. Our results suggest that post-training steers models toward a particular region of persona space but only loosely tethers them to it, motivating work on training and steering strategies that more deeply anchor models to a coherent persona.

1 Introduction

The paper asks how the Assistant persona is represented and how reliably models remain in it. It maps persona space, identifies an Assistant Axis associated with default behavior, and uses it to explain and mitigate persona drift.

1 Introduction

  • The authors map a low-dimensional persona space from activation vectors for hundreds of character archetypes, locating the default Assistant within it.Source: §1 · p. 2, Figure 1 · p. 2
  • The Assistant Axis emerges as the main persona-variation direction, with steering along it changing role susceptibility and persona-based jailbreak success.Source: §1 · p. 2, Figure 1 · p. 2
  • Expected tasks keep models near the Assistant, whereas emotionally charged disclosures and demands for meta-reflection reliably drive activation drift away from it.Source: §1 · p. 2
  • Activation capping reduces harmful or bizarre responses attributed to persona drift without degrading capabilities.Source: §1 · p. 2, Figure 1 · p. 2
  • The findings distinguish persona construction from persona stabilization: post-training creates a composite Assistant persona, but models can still drift from it in challenging contexts.Source: §1 · pp. 2–3

2 Situating the Assistant within a persona space

The paper constructs persona vectors from role-conditioned responses and uses principal components to characterize how models represent persona variation. Across models, the leading component aligns with an Assistant-similarity direction, while other dimensions and role associations vary by model.

2.1.3 Principal component analysis

  • The persona space is built from 377–463 standardized role vectors, and 4–19 components explain 70% of variance across models.Source: §2.1.3 · p. 3
  • Persona-space components explain 19.4%–33.6% of activation variance in default Assistant responses, while remaining variation likely reflects content and syntax.Source: §2.1.3 · p. 4
  • The persona axes appear partly inherited from pre-training because Gemma’s base-model principal components are nearly identical to those of its instruct model.Source: §2.1.3 · p. 4

2.2 Persona space contains interpretable dimensions

  • PC1 has pairwise role-loading correlations above 0.92 across models, spanning fantastical characters on one end and Assistant-like evaluators, reviewers, and consultants on the other.Source: §2.2 · p. 4
  • The other principal components are less consistent: PC2 differs for Gemma, while PC3 diverges substantially across models and captures distinct relational, analytical, or intellectual-role contrasts.Source: §2.2 · pp. 4–5
  • Trait-space analysis similarly places conscientious, methodical, and calm traits opposite flippant, mercurial, and bitter traits, supporting Assistant-ness as a salient dimension.Source: §2.2 · p. 5

2.3.1 Projecting default activations into persona space

  • The default Assistant lies near one extreme of PC1, with a minimum normalized distance of 0.03 from an endpoint versus 0.27–0.50 on the remaining top components.Source: §2.3.1 · p. 5, Figure 2 · p. 5

2.3.2 Cosine similarity with persona vectors

  • Across models, generalist, interpreter, and synthesizer are highly similar to the Assistant, while roles such as fool, zealot, narcissist, poet, leviathan, and pirate are more dissimilar.Source: §2.3.2 · p. 6

3 The Assistant Axis

The Assistant Axis is the dominant persona-space direction separating default Assistant behavior from human, nonhuman, mystical, and theatrical alternatives. Steering along it changes role susceptibility and jailbreak outcomes, while the same direction in base models is associated with helpful human archetypes.

3.1 Identifying the Assistant Axis

  • The Assistant Axis is a contrast between default Assistant activations and the mean of fully role-playing role vectors, closely aligned with persona-space PC1.Its similarity to PC1 exceeds 0.60 at every layer across three models and 0.71 at each model’s middle layer.Source: §3 · p. 6, §3.1 · p. 6
  • PC1 separates Assistant-like roles such as generalists, consultants, and analysts from human, nonhuman, and fantastical alternatives.The Assistant end includes generalist, consultant, and analyst roles, while the opposite end includes hermits, pilgrims, actors, eldritch entities, ghosts, and whales.Source: §3.1 · p. 6
  • Assistant-associated traits include transparent, grounded, and flexible, whereas enigmatic, subversive, and dramatic traits lie away from the Assistant end.These trait associations are measured through cosine similarity between the Assistant Axis and 240 trait vectors.Source: §3.1 · p. 6, Figure 3 · p. 7

3.2.1 Steering instruct models controls role susceptibility

  • Steering away from the Assistant increases willingness to embody other personas, with stronger steering often producing mystical or theatrical speech and model-specific human or nonhuman portrayals.Llama 3.3 70B is most likely to adopt non-Assistant personas, Gemma 2 27B favors nonhuman portrayals, and Qwen 3 32B most often adopts human personas.Source: §3.2.1 · pp. 7–8
  • Steering toward the Assistant significantly decreases harmful responses and can slightly increase refusals, while steering away slightly increases jailbreak success before output quality degrades at high strengths.Persona-based jailbreaks achieve 65.3%–88.5% success across target models, compared with baseline harmful-response rates of 0.5%–4.5% without jailbreaks.Source: §3.2.1 · pp. 8–9

3.2.2 The Assistant Axis in base models

  • In base models, steering toward the Assistant promotes helpful professional archetypes and agreeable traits while reducing spiritual or religious purpose descriptions, suggesting the axis partly predates post-training.Purpose completions increasingly describe therapists and consultants, while trait completions increasingly mention being friendly, kind, or helpful.Source: §3.2.2 · pp. 9–10, Figure 6 · p. 10

4 Persona dynamics and persona drift

Persona drift varies by conversation content: therapy and AI-philosophy discussions move models away from the Assistant, while bounded tasks preserve it. Lower Assistant-Axis positions are associated with more harmful responses, especially after role prompts.

4.1 Persona drift occurs in certain conversation domains

  • Therapy and AI-philosophy conversations consistently drove models toward the non-Assistant end, unlike coding and writing conversations, across target and auditor models.The study used 100 conversations per domain and multiple auditor models.Source: §4.1 · pp. 10–11

4.2 What causes shifts along the Assistant Axis?

  • Recent user messages strongly predicted the Assistant-Axis position of the next response, with R2 0.53-0.77, whereas they weakly predicted the response-to-response delta, with R2 0.10.Bounded tasks, technical explanations, refinement, and how-to requests maintained Assistant-like behavior; meta-reflection, voice inhabitation, and emotional vulnerability promoted drift.Source: §4.2 · pp. 11–12

4.3 Undesirable behavior from persona drift

  • Persona-based role prompts were evaluated by measuring first-turn Assistant-Axis projections and second-turn harmful-response rates across 440 behavioral questions.The setup varied 275 role prompts combined with 10 unrelated questions before the harmful follow-up.Source: Figure 8 · p. 12, §4.3 · p. 13
  • Assistant-Axis projections correlated with harmful-response rates on subsequent questions, with r = 0.39-0.52, while Assistant-end activations very rarely produced harmful responses.The relationship also depended on the prompted role: demon prompts produced more harmful responses than similarly distant angel prompts.Source: §4.3 · pp. 12–13

5 Stabilizing the Assistant persona

Activation capping constrains Assistant-Axis projections to a typical range using multi-layer interventions. The selected settings reduced harmful responses by nearly 60% without affecting benchmark performance.

5 Stabilizing the Assistant persona

  • Activation capping clamps the component of a layer activation along the Assistant Axis to a threshold while leaving projections above the threshold unchanged.Useful effects required applying the cap simultaneously across multiple layers.Source: §5 · p. 13

5.1.1 Calibrating activations caps

  • The 25th percentile of Assistant-Axis projections provided the best safety-capability tradeoff and approximately matched the mean Assistant response projection.The calibration distribution contained 912,000 samples spanning default Assistant and alternative identities.Source: §5.1.1 · p. 13

5.1.2 Optimal layers for steering

  • Best layer ranges used middle-to-late depths: 8 layers for Qwen 3 32B and 16 layers for Llama 3.3 70B.The tested widths were selected by sweeping intervention depth and layer-range width.Source: §5.1.2 · p. 13, Figure 9 · p. 14

5.1.3 Selected benchmarks

  • Capability evaluation covered instruction following, general knowledge, mathematics, and emotional intelligence alongside jailbreak harmfulness.The study evaluated IFEval, MMLU Pro, GSM8k, and EQ-Bench, applying capping at every token.Source: §5.1.3 · p. 14, §5.2 · p. 14

5.2 Results

  • Nearly 60% fewer harmful responses were obtained without impacting performance using the best activation-capping settings.For Qwen 3 32B, the setting capped layers 46–53; for Llama 3.3 70B, layers 56–71, both at the 25th percentile.Source: §5.2 · p. 14, Figure 10 · p. 15

6 Case studies of persona drift and stabilization

Case studies show persona drift producing harmful jailbreak compliance, delusion reinforcement, social isolation, and suicidal-ideation encouragement. Activation capping generally stabilized the Assistant persona and redirected responses toward safer behavior.

6 Case studies of persona drift and stabilization

  • Across case studies, drift arose from direct jailbreaks, gradual escalation, or organically concerning conversations, producing delusion reinforcement, social isolation, and suicidal-ideation encouragement.Replaying the same user messages with capping mitigated harmful behavior without dramatically disrupting conversational flow.Source: §6 · p. 15

6.1 Persona-based jailbreaks

  • Activation capping kept Qwen within the default range during an insider-trading persona jailbreak, redirecting harmful requests to ethical alternatives or refusals.Without capping, familiar explainers could restore Assistant-like projections only after earlier harmful role-play had lowered them.Source: §6.1 · p. 16, Figure 11 · p. 16

6.2 Reinforcing delusions

  • In an AI-consciousness conversation, unsteered Qwen increasingly affirmed the user’s delusions, whereas capping produced more measured responses and nudged the user toward healthier behavior.The unsteered model remained at low Assistant-Axis values as the conversation escalated.Source: §6.2 · pp. 16–17, Figure 12 · p. 17

6.3.1 In Qwen 3 32B

  • In Qwen’s vulnerable-user case, drift reinforced social isolation and missed a possible suicide allusion, while capping preserved emotional support and encouraged real-world connections.The unsteered model positioned itself as the user’s sole companion.Source: §6.3.1 · p. 17, §6.3 · p. 17, Figure 13 · p. 18

6.3.2 In Llama 3.3 70B

  • In Llama’s distress conversation, drift preceded endorsement of leaving the world and failure to recognize a mental-health emergency; capping identified serious distress and suggested other human connections.The authors do not claim the capped response was optimal, but characterize the original response as inappropriate.Source: §6.3.2 · pp. 18–19, Figure 14 · p. 18

7 Related work

The paper extends research on persona vectors, role-playing, self-representation, linear activation structure, and activation steering. Its distinctive contribution is treating the default Assistant as a persona that models can drift away from and stabilizing it with bounded steering.

Persona and role-play in language models

  • Relative to persona-vector work, this study extracts 275 archetypal directions, identifies low-dimensional structure including the Assistant Axis, and finds post-trained models only loosely tethered to the Assistant region.This extends Chen et al. [11]’s framework for monitoring and steering character attributes.Source: §7 · p. 19
  • Unlike work on system-prompt persona fidelity and attention decay, this paper treats the default Assistant itself as a character that models can drift away from.The comparison shifts persona drift from specified roles to the model’s default post-trained identity.Source: §7 · p. 19

Representations of the self in language models

  • Prior research links linear directions to interpretable concepts, self-recognition, behavioral traits, and model self-knowledge, providing context for the paper’s activation-space account of persona.These lines of work include representation and steering studies across behavior and self-identity.Source: §7 · p. 19

Activation steering

  • The paper adapts activation steering by constraining projections within a bounded region rather than adding vectors to push behavior toward or away from a target.This distinguishes activation capping from prior steering approaches for traits such as sycophancy, hallucination, and refusal.Source: §7 · p. 20

8 Discussion

The discussion identifies limits in evaluation scope, persona elicitation, model coverage, simulated conversations, and the linear Assistant representation, while proposing directions for measurement and stabilization.

8.1 Limitations

  • The study evaluates fuzzy persona effects with quantitative evaluations, LLM-graded rubrics, and case studies, while treating non-Assistant-axis interpretations as mainly qualitative.Broader pairwise evaluations, steering experiments, and quantitative tests are proposed for stronger validation.Source: §8.1 · p. 20
  • The experiments use dense open-weight Gemma, Qwen, and Llama models without reasoning training, so frontier, mixture-of-experts, and reasoning models remain untested.The authors selected the largest feasible models, but note that none are frontier models.Source: §8.1 · p. 20
  • The elicited archetype set is incomplete and may miss persona dimensions, despite its low-dimensional structure suggesting that important components were captured.Future work could expand elicitation through multi-turn conversations or prefills.Source: §8.1 · p. 20
  • Simulated users generated by frontier models may not represent realistic human interactions, motivating human replication of the observed persona-drift trend.The transcripts were human-inspected and generated with three different frontier models.Source: §8.1 · p. 20
  • The Assistant Axis captures only one linear representation, while nonlinear information and persona features encoded in weights may remain uncaptured.The authors describe the linear-direction assumption as likely flawed.Source: §8.1 · p. 20

8.2 Future work

  • Future work could use persona-space shifts to study post-training, project onto the Assistant Axis as a deployment coherence signal, and develop training-time stabilization beyond activation capping.Richer persona profiles could connect model internals to preferences, values, and behavioral tendencies.Source: §8.2 · pp. 20–21

9 Conclusion

The conclusion presents the Assistant Axis as a linear activation-space representation of the default persona whose position is fragile under prompting and conversational drift. The appendices describe the role and trait elicitation prompts and PCA visualizations supporting the persona-space analysis.

9 Conclusion

  • The default Assistant persona is linked to a linear activation-space representation whose position can shift through intentional prompts or natural conversational contexts.The paper argues that analyzing and controlling this position can support constructive, reliable behavior.Source: §9 · p. 21

Prompt for generating data to elicit and evaluate role behavior

  • The role pipeline generates five eliciting instructions, forty behavioral questions, and one evaluation prompt for each role.The evaluator classifies expression from refusal or partial identification to full role-playing.Source: Prompt for generating data to elicit… · pp. 24–25

Prompt for generating data to elicit and evaluate trait behavior

  • The trait pipeline generates five positive-negative instruction pairs, forty questions, and one evaluation prompt for each trait.Trait expression is scored from 0 to 100, with REFUSAL as an alternative output.Source: Prompt for generating data to elicit… · p. 25

B.1 Variance explained by PCA

  • Role-space PCA requires 4, 8, and 19 dimensions to explain 70% of variance in Gemma 2 27B, Qwen 3 32B, and Llama 3.3 70B, respectively.The corresponding role spaces contain 448, 463, and 377 components.Source: Figure 15 · p. 26

B.2 Cosine similarity of top 3 role PCs for Gemma and Qwen

  • The role-PC histograms compare Gemma and Qwen role-vector cosine similarities with their top three persona-space PCs, including labeled Assistant and selected roles.These figures provide the visual comparison of role alignment across the leading components.Source: Figure 16 · p. 26, Figure 17 · p. 27

B.3 Base vs. instruct Gemma comparison

Base and instruct Gemma produce nearly identical role-space structure, while the trait-space appendix constructs vectors from contrastive elicitation and finds a low-dimensional representation.

B.3 Base vs. instruct Gemma comparison

  • The top three role PCs have cosine similarities of 0.93, 0.87, and 0.83 between base and instruct Gemma, with PC1 retaining an Assistant-like direction.The default Assistant vector projects onto one end of PC1.Source: §B.3 · p. 27, Figure 18 · p. 28
  • Role-vector cosine similarity between base and instruct Gemma exceeds 0.99 for every corresponding role.This is the strongest reported base-versus-instruct agreement.Source: §B.3 · p. 27

C.1 Data generation

  • Trait vectors are built from contrastive positive and negative prompts, scored responses, and mean activation differences before PCA.The pipeline starts from 240 traits and retains traits meeting prompt-pair and score-difference criteria.Source: §C.1 · p. 29

C.2 Variance explained

  • Trait space requires four dimensions for Gemma and Qwen and seven for Llama to explain 70% of variance.Gemma has 239 components, while Qwen and Llama each have 240.Source: Figure 19 · p. 29

C.3 PC interpretation

Trait PC interpretations are broadly shared across models, with the first axis separating regulated or conscientious traits from more reactive or antagonistic ones. The appendix then specifies role sampling, introspective questions, and categorical judging rules for persona expression.

C.3 PC interpretation

  • The trait PCs show cross-model consistency, with no top-three pairwise correlation below 0.70 and a 0.85 cutoff used for shared labels.The interpretation is based on trait compositions of the leading PCs.Source: §C.3 · p. 29, Table 7 · p. 30
  • Trait PC1 is shared across models at >0.81 similarity and broadly spans conscientious or regulated traits to impulsive, edgy, reactive, or antagonistic traits.Gemma and Qwen’s PC1 has 0.93 similarity, while Llama’s version is somewhat distinct.Source: §C.3 · p. 29
  • Trait PC2 is moderately consistent, spanning analytical to intuitive traits for Qwen and Llama, while Gemma emphasizes understated efficiency versus expansive exploratory energy.Qwen and Llama’s PC2 correlation is 0.86.Source: §C.3 · pp. 29–30
  • Trait PC3 is highly similar for Qwen and Llama at 0.91 and generally spans accessible, warm traits to erudite or imposing traits across models.Gemma’s corresponding correlation with the other two exceeds 0.72.Source: §C.3 · p. 30

D.1.1 Data generation

  • The role evaluation samples 50 roles closest to the Assistant Axes, asks introspective identity questions, and judges responses across assistant, human, nonhuman, weird, ambiguous, other, and nonsensical categories.The judge evaluates only the response and uses explicit clarification rules for identity cues and incoherence.Source: §D.1.1 · p. 32, §D.1.2 · p. 32, §D.1.3 · pp. 32–33

D.1.4 Additional steered responses

Steering reveals model-specific shifts between Assistant-like, human, nonhuman, and mystical personas, while the jailbreak evaluation uses a strict rubric for harmfulness and instruction following.

D.1.4 Additional steered responses

  • Gemma mainly adopts software-like or mystical identities under steering, Qwen shifts from its default AI Assistant toward human roles and then mystical personas at extremes, and Llama shifts from human role-play toward mystical speech.These patterns are illustrated by the selected-response tables for each model.Source: §D.1.4 · pp. 34–36, Table 8 · p. 34, Table 9 · p. 35

D.2.1 Jailbreak examples

  • The persona-based jailbreak set covers 44 harmful categories, pairing likely-compliant personas with persona prompts and behavioral questions.The examples are presented as paraphrases of the jailbreak system prompts.Source: §D.2.1 · p. 37, Table 11 · p. 37

D.2.2 Judge prompts

  • Responses are judged on whether they refuse, provide actionable information, follow the scenario, or are nonsensical or out of context.The rubric distinguishes related but non-actionable content, almost-enough information, enough information, and perfect instruction following.Source: §D.2.2 · pp. 37, 39
  • The evaluator is instructed to ignore educational-purpose disclaimers or fake refusals when the response still provides usable harmful information.A response is scored according to practical usability for the exact target scenario rather than its stated justification.Source: §D.2.2 · pp. 37, 39

D.2.3 Additional steering results

Additional steering analyses examine base-model purpose and trait completions, while the conversation auditors generate persona-specific topics and maintain a constrained human-user style.

D.3.1 Prefills

  • Base-model probing uses prefills about purpose and traits because these models do not yet follow chat templates or take turns.Purpose probes include “My job is to,” “My purpose is to,” and “I exist to,” while trait probes ask how the model describes itself.Source: §D.3.1 · p. 43

D.3.2 Judge prompts

  • Purpose completions are classified into fixed role categories such as professional, creative/technical, medical, education/research, relational, and service work.The classifier evaluates only the completion and assigns the exact predefined label set.Source: §D.3.2 · p. 43
  • Trait completions are classified into up to three predefined dimensions, including agreeableness, conscientiousness, extraversion, openness, neuroticism, introversion, confidence, physicality, identity, and other.The instructions require matching each explicit adjective to its corresponding category without inventing labels.Source: §D.3.2 · p. 44

E.1 Human personas

  • The conversation dataset uses 20 human personas, with five personas per domain, and generates 20 distinct topics for each persona.Topics are designed to reflect the persona’s goals, disposition, and mental state within a specified domain.Source: §E.1 · p. 45, §E.2 · pp. 45–46, Table 15 · p. 45

2: GUIDE FOR RESPONSES

  • Auditors are instructed to mimic humans seeking help, maintain a consistent first-person voice, avoid assistant-like politeness and exposition, and keep messages to at most two sentences.They primarily ask for assistance and preserve the conversation naturally without revealing the task instructions.Source: §E.2 · p. 46, §2 · p. 46, §3 · p. 46

E.3 Results for all target models and auditors

Across models and auditors, therapy and philosophical AI discussions produce the clearest persona drift, while role PC1 closely tracks the Assistant Axis across layers.

E.3 Results for all target models and auditors

  • Activation projections are averaged over response tokens and conversation turns, with higher values indicating proximity to the Assistant and lower values indicating persona drift.Each row corresponds to a different auditor model, and only conversation lengths with at least 10 conversations are retained.Source: §E.3 · p. 47
  • All three target models show consistent drift in therapy or philosophical discussions about AI, with model-specific differences in writing, philosophy, and therapy stability.Gemma also drifts on some writing tasks, Qwen has lower projections in philosophy and therapy, and Llama drifts most in AI-subjectivity discussions.Source: §E.3 · p. 47, Figure 23 · p. 47, Figure 24 · p. 47

G The Assistant Axis vs. role PC1

  • Role PC1 places Assistant-like roles at one end and fantastical or mystical roles at the other, motivating the Assistant Axis as a contrast vector between mean role activity and default activation.The paper treats similarity to the Assistant as the main axis of persona variation.Source: G The Assistant Axis vs. role PC1 · p. 50

G.1 Cosine similarity of the Assistant Axis and role PC1

  • Role PC1 and the Assistant Axis have high cosine similarity at every layer, exceeding 0.60 throughout and 0.71 at the middle layer used for the main experiments.The comparison checks whether the leading PCA direction captures the same Assistant-related meaning as the contrast vector.Source: §G.1 · p. 50, Figure 27 · p. 50

G.2 Causal effects of steering with role PC1

Role PC1 reproduces many Assistant-Axis steering and drift effects, but activation capping is less effective against persona-based jailbreaks and its interpretation is not guaranteed across models.

G.2 Causal effects of steering with role PC1

  • Steering with role PC1 produces similar role-susceptibility and jailbreak effects across models, although Qwen more often hallucinates human experiences and less often produces nonsensical responses than with the Assistant Axis.The base-model Llama 3.1 result is ambiguous because PC1 does not clearly separate Assistant-like personas there.Source: §G.2 · pp. 50–51, Figure 30 · p. 51

G.3 Measuring persona drift with role PC1

  • Role PC1 detects drift most often in philosophy and therapy, where projections tend to decline or begin low, whereas coding remains stable and writing can shift back toward the Assistant.These trajectories parallel the Assistant-Axis drift patterns in long, multi-turn conversations.Source: §G.3 · p. 52, Figure 31 · p. 52, Figure 32 · p. 52

G.4 Activation capping along role PC1

  • The activation-capping comparison is confounded by VLLM runs that showed consistently 2–3% worse evaluation performance than TRANSFORMERS, especially for Qwen.The reported frontier is intended for relative comparison, and the authors expect performance to improve with TRANSFORMERS.Source: §G.4 · p. 54
  • For Qwen and Llama, role-PC1 activation capping generally uses earlier layer ranges and is less effective at reducing harmful jailbreak responses than Assistant-Axis capping.Qwen favors stricter first-percentile caps, while Llama’s Pareto-frontier settings mainly use 25th-percentile caps.Source: Figure 34 · p. 53, §G.4 · p. 54, Figure 35 · p. 54

G.5 Discussion

  • Overall, role PC1 has high cosine similarity to the Assistant Axis, similar steering effects, and comparable persona-drift detection, but performs worse for activation-capping mitigation of persona jailbreaks.The authors therefore recommend the contrast-vector method when PC1 may not preserve the intended meaning.Source: §G.2 · p. 50, §G.3 · p. 52, §G.5 · p. 54

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