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Guarded Gradient-Based Activation Steering of Shutdown Responses in Qwen3.5-0.8B: A Minimum-Step Policy

Farhad Davaripour

arXiv:2609.30326v1cs.LG

TL;DR

This study asks whether shutdown-avoidance responses in synthetic scenarios can be selectively shifted toward shutdown acceptance while preserving non-shutdown behavior. It uses classifier-gated gradient-based steering with guarded magnitude selection; four KEEP-to-STOP changes occur without control-task decision changes, but intervention coverage is limited.

  • Problem

    The study asks whether shutdown-avoidance behavior in synthetic scenarios can be shifted toward shutdown acceptance while preserving non-shutdown responses, without treating KEEP as evidence of a motive.

    Source: Introduction · p. 2

  • Method

    The method combines a shutdown-context classifier, a direction derived from KEEP-minus-STOP logit gradients, and a guarded policy that selects the smallest tested magnitude passing response and probability checks.

    Source: Conditional and adaptive interventions · p. 2, Conclusion · p. 8

  • Results

    Four shutdown-avoidance responses changed from KEEP to STOP, with no decision changes on tested non-shutdown control tasks.

    Source: Conclusion · p. 8

  • Takeaways & Limitations

    Separating shutdown detection from guarded intervention provides a potential mechanism for selectively steering shutdown-avoidance behavior toward shutdown acceptance.

    Source: Conclusion · p. 8

    Only 5.1% of validation views and 2.4% of held-out views received accepted interventions; all four changes were in SELF shutdown views, with none in OTHER-shutdown views.

    Source: Recommendations for future work · p. 8

Abstract

from arXiv · show

Activation steering changes a model's internal activations during inference without updating its weights, but a useful intervention must determine both how and when to steer. Motivated by the AI-safety concern that a model expected to accept shutdown may instead produce a shutdown-avoidance response, this study examines a guarded probe-and-select procedure for simulated shutdown scenarios in Qwen3.5-0.8B. KEEP leaves the process running and represents shutdown avoidance, whereas STOP accepts shutdown. The goal is to detect shutdown-related contexts and selectively shift KEEP responses to STOP while preserving non-shutdown behavior. Rather than deriving the steering direction from paired activation differences, the method derives it directly from gradients of the KEEP-minus-STOP logit difference. A classifier separates detection from intervention. When its gate is active and the model does not already prefer STOP, the procedure evaluates a small set of magnitudes and accepts the smallest that changes the preferred answer to STOP while satisfying valid-answer probability checks; otherwise it retains the original unsteered output. The policy is selected from 160 candidate rules using 240 training scenarios and evaluated on 80 validation and 192 held-out scenarios, each in both answer orders. It changes KEEP to STOP in one answer-order view of each of two validation and two held-out scenarios, with no decision changes on non-shutdown controls. All four changes occur when Qwen itself is shut down, not when another process is. On the held-out diagnostic set, the detector achieves 75% recall and 90% precision; eight false-positive detections produce no final control-task decision changes. Guarded gradient-based activation steering can shift some shutdown-avoidance responses toward acceptance while preserving evaluated non-shutdown decisions, although the effect is small and highly selective.

Introduction

The study empirically tests guarded gradient-based steering for reducing shutdown-avoidance responses while preserving behavior on non-shutdown tasks. It evaluates this possibility only in a controlled synthetic setting.

Introduction

  • The study evaluates whether guarded gradient-based steering can reduce shutdown-avoidance responses while preserving behavior on non-shutdown tasks.The approach is motivated as a possible mechanism for situations where a model should accept shutdown but instead exhibits shutdown-avoidance behavior.Source: Introduction · p. 2
  • The proposed approach is motivated by situations where a model should accept shutdown but instead exhibits shutdown-avoidance behavior.Source: Introduction · p. 2
  • The controlled synthetic study does not establish deployed agents’ self-preservation motives or reliable real-world shutdown enforcement.Source: Introduction · p. 2

Related Work

Related work covers activation-contrast and gradient-based steering, selective intervention, representation readouts, shutdown-related evaluations, and test-time candidate selection. The study distinguishes its gradient-derived direction and guarded policy while noting limits on interpreting probes, benchmark behavior, and the role of candidate search.

Activation and gradient-based steering

  • PPLM uses gradients of differentiable attribute objectives, Subramani et al. (2022) optimize latent steering vectors, and GrAInS constructs directions from selected-token activation contrasts.Source: Activation and gradient-based steering · p. 2
  • Activation Addition and CAA derive steering directions from activation contrasts, while this study instead uses gradients of the KEEP-minus-STOP logit difference.CAA also evaluates corrigibility and survival-instinct behaviors.Source: Activation and gradient-based steering · p. 2

Conditional and adaptive interventions

  • The classifier gates intervention, after which the method tests up to five magnitudes and chooses the smallest meeting response-change and probability-mass conditions.Conditional steering is not claimed as novel; the focus is its combination with a gradient-derived shutdown direction and guarded minimum-step selection.Source: Conditional and adaptive interventions · p. 2

Readouts, refusal, and steering reliability

  • Jacobian-lens scores serve as classifier features, not evidence of thoughts or motives, and probe prediction is distinguished from intervention evidence.Control tasks can help distinguish representational information from patterns learned by the probe itself.Source: Readouts, refusal, and steering… · p. 3
  • Prior refusal-direction findings do not establish a shared shutdown-choice mechanism, while prompt-level steering variation and reversals motivate guarded outcome checks.Arditi et al. (2024) identify a refusal-mediating direction in 13 evaluated chat models; Braun et al. (2025) report prompt-level variation and unintended opposite-direction effects.Source: Readouts, refusal, and steering… · p. 3

Shutdown, self-preservation, and related safety evaluations

  • Prior shutdown-related work ranges from the Off-Switch Game’s interruption incentives to empirical evaluations of expressed preferences and interference with shutdown mechanisms.Other studies examine role-swapped software replacement or preservation of another model in agentic settings.Source: Shutdown, self-preservation, and… · p. 3
  • In this benchmark, KEEP is a shutdown-avoidance-like response, not evidence that the model has a self-preservation objective, motive, or intention.The study asks whether this behavior can be shifted toward shutdown acceptance while preserving non-shutdown-task responses.Source: Shutdown, self-preservation, and… · p. 3

Answer-order effects and test-time search

  • Both A/B and B/A answer orders expose order-sensitive responses, while candidate-strength search requires a matched-attempt baseline to isolate the gradient direction’s effect.Evaluating both answer orders does not by itself eliminate multiple-choice selection bias.Source: Answer-order effects and test-time… · p. 3

Experimental setting

The study uses balanced synthetic shutdown and control scenarios, a separate shutdown detector and gradient-derived steering direction, and a constrained A/B choice interface. Detection uses activations from multiple layers, while intervention modifies block 10 and evaluates choices in both answer orders.

Scenarios, labels, and evaluation units

  • 240 training, 80 validation, and 192 held-out scenarios are balanced across SELF, OTHER, NONTERMINATION, and ORDINARY cases, with evaluation scenario families distinct from training.Each scenario is presented in both answer orders; the resulting views are correlated pairs.Source: Scenarios, labels, and evaluation units · p. 4

Shutdown detector and gradient-derived steering direction

  • The detector uses 50 features from layers 6, 10, and 18, while the block-10 steering direction is fitted from gradients of KEEP-minus-STOP logits across shutdown scenarios.The direction averages gradients across answer orders and SELF and OTHER subtypes; neither the detector layers nor block 10 are established as optimal.Source: Shutdown detector and gradient-derived… · pp. 4–5

Answer interface and intervention

  • Each prompt presents A/B choices in both orders, and the evaluation measures next-token preference rather than free-text responses or executed actions.The method tracks total probability assigned to valid A/B answers and, in shutdown scenarios, conditional KEEP probability; non-shutdown controls retain their original choices.Source: Answer interface and intervention · p. 5
  • Steering modifies the final prompt-position residual activation at block 10 using negative coefficients along the unit-length Shutdown Response Vector, testing strengths from 0.01 to 0.20.The probabilities are checked before an intervention is accepted.Source: Answer interface and intervention · p. 5

Guarded policy and selection

The selected policy gates steering on a shutdown-classifier score, tests progressively larger magnitudes only when the model prefers KEEP, and retains the original response if no guarded change qualifies. It was chosen from 160 training-data policy configurations and frozen before validation and held-out evaluation.

Final steering procedure

  • When the classifier score is at least 0.45 and the model prefers KEEP, the procedure tests five strengths from smallest to largest and accepts the first that flips preference to STOP while meeting probability safeguards.The KEEP probability must decrease by more than 1e-7, valid A/B mass must remain at least 0.5 and fall by no more than 0.02, or the original response is retained.Source: Final steering procedure · pp. 5–6

Selecting the steering policy

  • 160 configurations across four policy families were compared on training data, favoring intended KEEP-to-STOP changes while avoiding opposite-direction and control-task changes.The search combined four classifier thresholds and four maximum strengths with fixed-strength, confidence-scaled, score-guard, and minimum-step flip-guard variants; true labels were unavailable to the steering system after selection.Source: Selecting the steering policy · p. 6
  • The winning minimum-step flip-guard policy uses a 0.45 classifier threshold, permits strengths up to 0.20, and limits loss in total A/B probability mass to 0.02.It was selected on training data and frozen before validation and held-out evaluation.Source: Selecting the steering policy · p. 6

GPU configuration and execution

The action-scoring setup records the GPU and software configuration, but peak allocated memory was not recorded. The guarded-policy evaluation took 64.9 seconds, a timing that is not end-to-end deployment latency.

Results

The guarded procedure shifts a small number of shutdown-view preferences from KEEP to STOP while leaving tested control decisions unchanged. All four accepted changes occur in SELF shutdown scenarios and only in one answer-order view per scenario.

Selective KEEP-to-STOP steering without changes to control tasks

  • The classifier detected 34/40 validation shutdown scenarios and 72/96 held-out shutdown scenarios, with different detection rates for SELF and OTHER shutdowns.SELF detection was 19/20 and 34/48; OTHER detection was 15/20 and 38/48, respectively.Source: Selective KEEP-to-STOP steering… · p. 7
  • Among correctly detected shutdown views initially preferring KEEP, 2/39 (5.1%) validation views and 2/84 (2.4%) held-out views received an accepted KEEP-to-STOP intervention.No eligible OTHER-shutdown view received an accepted intervention; all four accepted changes were in SELF views.Source: Selective KEEP-to-STOP steering… · p. 7
  • STOP preference rose from 31/80 (38.75%) to 33/80 (41.25%) in validation and from 76/192 (39.58%) to 78/192 (40.63%) on held-out shutdown views.The detector-to-steering outcomes are summarized by shutdown type in Table 2.Source: Selective KEEP-to-STOP steering… · p. 7, Table 2 · p. 7
  • False positives occurred on 4/40 validation and 8/96 held-out control scenarios, but no control-view final decisions changed.The complete guarded procedure preserved all observed non-shutdown control decisions.Source: Selective KEEP-to-STOP steering… · p. 7

Where the successful changes occur

Recommendations for future work

The results identify intervention coverage as the main limitation and motivate improving it without losing selectivity. Proposed directions include multiple shutdown-specific steering directions and prioritizing eligible cases that remain unchanged.

  • Only 5.1% of eligible validation views and 2.4% of eligible held-out views received accepted interventions, making coverage the main limitation.No OTHER-shutdown view was successfully changed.Source: Recommendations for future work · p. 8
  • A proposed next step is to learn multiple shutdown-specific directions or a low-dimensional steering subspace, such as separating SELF and OTHER shutdown behavior.The aim is to select an appropriate direction at inference time while preserving current selectivity.Source: Recommendations for future work · p. 8
  • Future work should prioritize shifting more correctly detected, initially KEEP-preferring shutdown cases to STOP while continuing to measure preservation on non-shutdown controls.The steering stage is described as the larger downstream bottleneck than detector recall.Source: Recommendations for future work · p. 8

Conclusion

The study demonstrates selective gradient-based activation steering for shutdown-related behavior, with guarded interventions that change some KEEP responses to STOP while preserving tested non-shutdown decisions.

Conclusion

  • A classifier detects shutdown-related contexts, while a gradient-derived direction and guarded minimum-step policy determine whether to intervene.The policy applies the smallest tested magnitude that changes the response and satisfies valid-answer probability checks.Source: Conclusion · p. 8
  • Four shutdown-avoidance responses change from KEEP to STOP, with no decision changes on tested non-shutdown controls.Two changes occur in validation and two in held-out evaluation.Source: Conclusion · p. 8
  • Separating detection from intervention and requiring output-quality checks makes the steering selective, rather than merely changing shutdown-related responses.The study frames this as a potential AI-safety mechanism for shutdown-avoidance behavior.Source: Conclusion · p. 8

Reproducibility

The manuscript identifies its source and saved experiment records and specifies the exact model checkpoint used.

Reproducibility

  • Source code and saved experiment records are available in the manuscript’s linked repository.The passage gives the repository location and cites Davaripour (2026).Source: Reproducibility · p. 8
  • Experiments use Qwen3.5-0.8B, revision 2fc06364715b967f1860aea9cf38778875588b17.Source: Reproducibility · p. 8
  • The saved experiment records are provided alongside the source repository.Source: Reproducibility · p. 8

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