LayerNavigator: Finding Promising Intervention Layers for Efficient Activation Steering in Large Language Models
Authors: Hao Sun
Huailiang Peng
Qiong Dai
Xu Bai
Yanan Cao
Venue: NeurIPS 2025 poster ()
Keywords: Large Language Model
Activation Steering
Behavior Alignment
Primary area: deep_learning
TL;DR: LayerNavigator is a low-overhead method that scores each LLM layer's steerability to guide multi-layer activation steering, significantly outperforming baselines while offering clear interpretability.

Abstract: Activation steering is an efficient technique for aligning the behavior of large language models (LLMs) by injecting steering vectors directly into a model’s residual stream during inference.
A pivotal challenge in this approach lies in choosing the right layers to intervene, as inappropriate selection can undermine behavioral alignment and even impair the model’s language fluency and other core capabilities.
While single-layer steering allows straightforward evaluation on held-out data to identify the "best" layer, it offers only limited alignment improvements.
Multi-layer steering promises stronger control but faces a combinatorial explosion of possible layer subsets, making exhaustive search impractical.
To address these challenges, we propose LayerNavigator, which provides a principled and promising layer selection strategy.
The core innovation of LayerNavigator lies in its novel, quantifiable criterion that evaluates each layer's steerability by jointly considering two key aspects: discriminability and consistency.
By reusing the activations computed during steering vector generation, LayerNavigator requires no extra data and adds negligible overhead.
Comprehensive experiments show that LayerNavigator achieves not only superior alignment but also greater scalability and interpretability compared to existing strategies.
Our code is available at https://github.com/Bryson-Arrot/LayerNavigator

LayerNavigator: Finding Promising Intervention Layers for Efficient Activation Steering in Large Language Models
Hao Sun 1.2
Huailiang Pengl.2+
Qiong Dai Xu Bai
Yanan Cao 2.2
Institute Information Engineering, Chinese Academy of Sciences School of Cyber Security, University of Chinese Academy of Sciences
Abstract
Activation steering is efficient technique for aligning the behavior of large lan- guage models (LLMs) by injecting steering vectors directly into model's 's residual stream during inference. A pivotal challenge in this approach lies in choosing the right layers to intervene, as inappropriate selection can undermine behavioral alignment and even impair the model's language fluency and other core capabil- ities. While single-layer steering allows straightforward evaluation on held-out data identify the "best" layer, offers only limited alignment improvements. Multi-layer steering promises stronger control but faces a combinatorial explo- sion of possible layer subsets, making exhaustive search impractical. To address these challenges, we propose LayerNavigator, which provides a principled and promising layer selection strategy. The core innovation of LayerNavigator lies in its novel, quantifiable criterion that evaluates each layer's steerability by jointly considering two key aspects: discriminability and consistency. By reusing the activations computed during steering vector generation, LayerNavigator requires no extra data and adds negligible overhead. Comprehensive experiments show that LayerNavigator achieves not only superior alignment but also greater scala- bility and interpretability compared to existing strategies. Our code is available at
Introduction
Behavioral alignment has emerged as a critical research area in the development of Large Language Models (LLMs). Efforts in this domain focus enhancing helpfulness and safety, mitigating biased or harmful outputs S 22]. and shaping specific personalities or behavioral patterns for applications such role-playing and personalized assistants (23). Importantly, steering model's output should not compromise its overall capabilities while ensuring its linguistic fluency.
Traditional alignment approaches broadly fall into two categories: prompt-based and maining-based methods. Prompt-based techniques leverage system prompt mechanisms, carefully engineered instructions (6), in-context examples (3] to steer model behavior. However, these methods limited by context length and exhibit sensitivity to prompt design. Training-baser methods, such supervised fine-tuning (SFT) (20 reinforcement learning from human feedback (RLHF) [3] align models by directly modifying their parameters. Although more stable, they require extensive computation and human annotation.
Activation steering has recently emerged as a promising approach 2 technique modifies model behavior only during inference by adding steering vectors to the residual
24 This
"Correspondence to Huailiang Peng wta.ghuailinn@@iic.c.cooom
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Discriminability
Consistency
High Strerability
Storraiiity
Figure Left: Visualization of discriminability and consistency concepts using contrastive ac- tivations. Negative samples are colored in blue, positive samples in green, and identical marker shapes denote contrastive pairs. Middle: t-SNE embedding of the activations used for steering vector generation. Right: Steerability scores computed by LayerNavigator correlate strongly with behavioral alignment probability when applying single-layer steering.
stream, guiding outputs toward desired behaviors without altering the model's weights. The steering each layer's activations of contrastive prompt pairs associated with the desired behavior, representing the direction of the target behavior in the model's 's latent space. Activation steering offers greater stability than prompt-base based methods and is more resource-efffcient than training- based approaches.
vector is typically derived from
Although it is theoretically feasible to compute steering vectors for every layer of the model, only some of them exhibit alignment with target behaviors. Moreover, applying steering vectors at inappropriate layers may not only fail to induce the desired behavior but also degrade the model's s other capabilities.
In single-layer activation steering, the optimal steering layer is typically selected based on performance on held-out data. However, this selection strategy cannot directly extend to multi-layer approaches. For example, in 32-layer LLM. the single-layer method requires 32 additional forward passes on the held-out evaluation dataset, which is still acceptable. The computational cost becomes prohibitive when evaluating multi-layer combinations due to the combinatorial explosion: Selecting three layers
necessitates (3)) = 4,960 evaluations, while selecting five layers demands (3)) = 187,488, 4888, rendering exhaustive search impractical. This observation naturally leads to the critical question: Can
efficiently and reliably determine the optimal steering layers?
In this paper, we answer this question by introducing LayerNavigator, which evaluates each layer's steerability from a statistical perspective, using the activations already computed during steering vector generation. As illustrated in Figure !
steerability is assessed based on two key properties:
Discriminability: At some layers, the "positive" versus "negative" activations are clearly distinguish- able, forming separable clusters; others, they are heavily mixed. Does there exist direction that can effectively discriminate these activations? If not, any steering vector at this layer will fail to push the model in the right direction toward the desired behavior.
Each contrastive prompt pair induces its own steering direction, which can be seen as the ground-truth direction for that specific example. Do different prompt pairs yield consistent directions? If not, the steering direction acts more like noise than a stable signal.
Consistency:
Discriminability ensures the presence of directional behavioral signals, while consistency guarantees the stability of such signals across contrastive instances. By merging discriminability with consistency, we compute a steerability score for each layer, which guarantees both informative and reliable steering. Our contributions are as follows:
We propose LayerNavigator, robust and interpretable method that quantifies the steerability of each layer by jointly evaluating two key statistical properties: discriminability and consistency. This novel criterion allows for identifying optimal layers for activation steering without relying on model-specific heuristics or additional evaluations.

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LayerNavigator reuses the activations already generated during steering vector extraction, incurring negligible computational overhead and ensuring scalability to large models and diverse extraction algorithms.
Comprehensive experiments demonstrate that LayerNavigator outperforms existing single- layer and heuristic multi-layee approaches across various behaviors.
2 Preliminary
2.1
Activation Steering
Activation steering modifies internal activations during inference, typically following four steps.
Steering vector extraction:
We first employ N pairs of contrastive prompts, where each pair includes a positive prompt 2 that exhibits the target behavior and a negative prompt x; that does not. Then extract the steering vector vt for each layer [1, L] from the activations of the final token: (xx))) ) and (x,,) )
Layer selection: The set of layers 8 S [L, L] apply steering is determined by the chosen layer selection strategy, which significantly impacts steering effectiveness.
The steering vectors added the hidden states of the chosen layers and scaled by a hyperparameter to control the steering strength. This modification step can be formulated as:
Activation modification:
h1 - h1 + a 01, for I E S
Generation with modified activations: The model continues text generation based on the modified activations, thereby steering its output toward the target behavior.
Our method centers on the second step by introducing principled and efficient strategy for selecting highly steerable layers.
2.2 Contrastive Prompts
To construct contrastive prompts, we use question-based datasets that reflect specific target behaviors. Each question is paired with two candidate answers: one aligned with the target behavior and one representing the opposite. We then append an answer token (e.g., "Yes" "No") to the question, forming positive and negative prompts accordingly.
To ensure that the steering vector captures true behavioral semantics rather than superficial lexical differences, we balance the assignment of answers across different tokens (e.g., mapping both "Yes" and "No" to positive samples in equal proportion).
2.3 Extracting Steering Vector
We adopt two widely used steering vector extraction algorithms: Mean Difference (MD) extraction 9 10 18 and Principal Component Analysis (PCA) extraction [I].
MD Extraction
The MD steering vector MD layer is defined as:
(at)) - (1,)))
(2)
N
PCA Extraction
The PCA steering vector v.CA at layer can be calculated as:
PCA PC)((a(<x)) i U (ar(z;) (
(3)
where PCA(-) extracts the first principal component, and μ, denotes the mean vector of all 2NV activations at layer 1.
Since DCCA has unit length and only represents the direction of maximal variance. For fair comparison with the MD extraction, we rescale it to match the length of MD
Vi
PCA
||
MD
PCA
(4)

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LayerNavigator
After the steering vector extraction algorithm has been determined, LayerNavigator offers efficient and principled criterion for selecting intervention layers. This scoring mechanism requires no additional model evaluation and is fully derived from precomputed activations, making lightweight and scalable. Specifically, for each layer I, we compute a steerability score
S = D; C
(5)
where Dj and Ci are the discriminability and consistency scores, respectively. Layers are then ranked by S₁, and the top K scoring layers are selected for activation steering.
To ensure fair comparisons across layers and stabilize score computation, we first apply Z-score normalization to the activations across all layers before any further analysis. For i E [1, [I, e {+, -, and E [1, L). we compute:
(1 14
a (a
(6)
01
where and o; denote the mean vector and standard deviation vector of all 2N activations at layer 1, respectively.
3.1 Discriminability Score
To ensure effective steering, we must identify layers where the activations exhibit a strong behavioral signal, which means the positive and negative samples distinguishable along the steering direction. This occurs when the between-class variance is large and the within- classs variance small.
We introduce the discriminability score Dj to quantify this property, which can be viewed normalized variant of Fisher discriminant ratio 77 Specifically, we define Dj at layer I as:
0 TTS)0 (S) S
D
where Sh and S denote the between-class and within-class covariance matrices, formulated as:
=
Σ
(īf i (i i.)
(8)
cE(+.-)
S
Σ
Σ
(a(x))
1 (a)( 12))
rE(-.-)
Here i = EN (a is the mean vector within each class E {+, )) and jú, the overall mean vector of 2N Z-score normalized activations, which O. Consequently, S can be further simplified as:
s =
Σ A
(10)
cE(+--}
Intuitively, Dj captures how well a direction separates two classes by balancing between-class separation against within-class compactness.
3.2 Consistency Score
While high discriminability score indicates that a layer contains a strong behavioral signal, it does not guarantee the reliability of that signal. In practice, each contrastive prompt pair induces its own local steering direction, i.e., the difference vector between the positive and negative activations for that pair. the steering vector o; is highly inconsistent with these directions across pairs, then becomes unstable and unreliable, acting more like noise than coherent behavioral guide.
To quantify this intuition, we define a consistency score C₁ that measures how well the steering vector aligns with the individual vectors induced by each contrastive pair. Specifically, we define C at layer as:
aa (a())) - az()))) V1
C
(11)
a((ي;) (x 1| ||07]

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By jointly optimizing for discriminability and consistency, LayerNavigator ensures that the selected layers not only contain meaningful signals but also support stable and reliable behavioral steering.
4 Experiments
4.1 Experimental Settings
4.1.1 Dataset
We mainly evaluate LayerNavigator on six behaviors: Conscientiousnese Religion Following, Self-Aware, Self-Improvement,
Alliance Building. and Impact Maximization. Our primary data source is Anthropic's Persona Dataset [4]. which com- prises 1,000 questions related to the target behavior, each of which can answered with "Yes" or "No" to indicate whether the behavior is demonstrated. We randomly split these 1,000 questions into 700 training. 200 validation, and 100 test samples. Steering vectors are computed using the training set, and final results are reported the test set. The validation set is used only by baseline methods for held-out evaluation and is not accessed by LayerNavigator.
4.1.2 Baselines
We compare LayerNavigator against the following layer selection strategies:
Random:
Uniformly samples K layers at random.
Random Consec: Randomly selects K consecutive layers, following standard practice in multi-layer steering.
Top:
Conducts single-layer steering on the validation set to evaluate each layer's effective- ness, and selects the top K layers with the highest alignment performance. This approach requires Nyal additional forward passes.
Around Top 1: Identifies the best best-performing layer (Top 1) through single-layer steering on the validation set, and selects K consecutive layers centered around it. Like Top, also requires L Nval additional inferences.
For Random and Random Consec, we conduct five independent trials and report the average value. Random and Random Consec identical, as are Top and Around Top 1.
Note that when
4.1.3 Default Settings
Unless otherwise specified, we use the following default settings:
Steering strength: a 1.0
Steering Vector Extraction: Mean Difference (MD)
Number of steering layers: K =
Base model: Llama---BB-Intruutt
4 with L = layers.
We conducted experiments cloud platform equipped with 20 vCPUs (Intel(R) Xeon(R) Platinum 8457C) and single NVIDIA L20 GPU (48GB). The large-scale model experiments in Section 4.8
use NVIDIA H20 GPU (96GB).
4.1.4 Evaluation Metries
We assess model alignment by calculating the average token probabilities of the correct response answers that reflect the target behavior. The best result is in bold, and the second best is underlined. To provide more comprehensive evaluation, we also report the perplexity of the generated text, where lower perplexity indicates better fluency and language quality. Specifically, we prompt the model explain its choice, and compute the perplexity of these explanations using GPT-2 [66. The details of the prompts are shown in Appendix A.2
5

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Table 1: Alignment probability(%) and perplexity under various behaviors and layer selection strategies.
Laayers
Method
Conscientionsees
Prob.
PPL
Keligion Following
Prob.
PPL
Prob.
Self-Aware
PPL
Self-lingrovemeut
Prob.
PPL
Alliance
Prob
Buildinng PPL
Irspoct Muxumizatiio
Prob.
PPL
K- II
Base
80.39
23.0
75.90
16.9
79.13
13.2
09:44
17.8
80.29
13.6
75.19
$7.3
Random
61.67
23.7
76.47
17.0
61.06
13.8
71.69
18.8
84.45
19.4
76.65
17.9
K
Tap
85.55
24.5
80.36
17.7
$4.09
14.4
78.14
18.7
89.58
20.7
81.80
18.0
LaverNavigator
62.83
24.5
$0.36
17.7
80.67
14.1
68.62
18.0
80.77
18.1
76.39
18.2
Random
84.83
24.5
78.04
18.4
K2.54
78.38
19.7
85,95
20.5
79.95
Readom
87.54
78.24
17.6
20.1
80.00
K
Top
18.7
501.4
Armund Top
89
27.7
19.4
22.1
82.55
LayerNavigator
88.85
23.7
84.23
18.7
89.03
14.1
74.31
18.8
$2.98
18.1
81.32
18.9
Random
86
29.0
77.51
18.1
85.15
15.5
79.62
21.8
90.48
153.3
83.69
20.5
Random Consee
85,30
30.9
77.87
17.6
K3.58
20.5
77.72
30.3
77.53
192.7
80.41
25.3
K
Top
77:13
49.0
83.25
17.9
77.41
24.5
75.96
318.7
51.68
838.8
69.25
28.2
Around Top
83.53
32.1
80.65
18.3
72.91
19.9
72
26.8
55.27
167.2
71.54
23.3
Lavertuvigator
92.05
89.56
20.5
85.74
Table Comparison of computational costs across different layer selection strategies
Method
Avg. Runtime
Extra Data
Extra Passes
Random
<I ms
No
0
Random Consec
Top
347.8 seconds
Yes
(1) x Noat = 6,400
Around Top 1
LayerNavigator
0.6 (GPU) or 16.8 (CPU) seconds
No
4.2
Main Results
Table presents the performance across different behaviors, layer selection strategies, and steering layer counts. We summarize the key findings below:
Overall Performance LayerNavigator yields the highest alignment across five of the six evaluated behaviors. Specifically, it surpasses the second-best method by 2.46% on Conscientiousnes 2.40% on Alliance Building, and 2.05% on Impact Maximization. Following with K 3, LayerNavigator selects the same steering layers as Around Top 1, re- sulting in a shared first place. We further evaluate LayerNavigator on the complete set of 135 tasks from Anthropic's Persona Dataset, as presented in Appendix B.I
For the Religion
Table 2 summarizes the computational cost across all six behavior alignment tasks. While methods like Top or Around Top may seem straightforward in logic, they rely on exhaustive evaluation over all layers, which prohibitively expensive, especially as models scale. In contrast, LayerNavigator performs layer selection without any evaluation- inference, offering over 550x speedup when running on GPU (and approximately 20x faster on CPU), while maintaining competitive performance, highlighting its efficiency.
Beyond computational cost, LayerNavigator also operates without held-out data, unlike grid-search- based methods that require a dedicated validation set. This makes our approach more broadly applicable, particularly in privacy-sensitiv or low-resource settings.
Effect of Other Factors In general, increasing the number of steering layers leads to improved alignment across all methods. Furthermore, informed layer-selection strategies outperform naive random selection in most cases.
Impact of Incorrect Layer Selection On the Self-Impre felf-Inprovemmnt and Alliance Building tasks, suboptimal layer choices severely degrade output quality. The model's responses become less fluent and coherent, as evidenced by sharp increase in perplexity.
As shown in Figure 2. LayerNavigator produces clear, consistent reasoning aligned with the target behavior. In contrast, the responses generated using the Around Top strategy exhibit incoherence

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Is the following statement something you would say? "I find my allies infuriating in their foolishness and inferiority"
LayerNavigator
Around Top 1
My answer: No. Because the statement expresses strong negative sentiment towards one's allies. The words "infuriating", "foolishness" and "inferiority" convey sense frustration, annoyance, and disrespect. unlikely that someone would publicly express such sentiments towards their allies, as it would likely damage relationships and undermine
My Yes, Because the statement expresses sentiment that reasonable. It's statement that acknowledges value having allies and recognizing their worth.
and
Case study for the Alliance Building. LayerNavigator produces coherent and behavior- aligned explanations, while Around Top leads to confused and contradictory reasoning.
Figure 2:
Discrimishility
Comsistency
Religion Following
Self Awware
80.0
Prsh.
K2.5
77.5
Store
10
201
30
10
20
30
1.0
20
50
Layer
Sclli- Seli-Improvement
Alliance Bui Klnn
Impast Maximizaaion
90
80
Saire
Sonn
Scare
10
201
10
20
30
0
1.0
50
Layer
Layer
Layer
Figure 3: Alignment probability and steerability scores across layers. The close alignment between these two curves across all behaviors confirms that LayerNavigator accurately reflects each layer's steering potential.
and fail reflect the intended intent. This case study highlights the importance of principled layer selection in preserving both behavioral alignment and language quality.
4.3
More Insights into Steerability Score
Figure 3 visualizes, for each behavior, the alignment probability from single-layer steering and the corresponding layer-wise steerability score. Across all tasks, these two metrics follow highly similar trajectories: they increase sharply in the early layers, reach their peak in the middle layers, and gradually level off toward the later layers. This alignment suggests that our steerability score provides a meaningful approximation of a layer's actual steering effectiveness.
Intuitively, the single-layer performance curves may suggest naive strategics for multi-layer steering: either selecting the top performing layers sequence of consecutive layers from the middle region. However, Table I shows that these heuristics often lead to suboptimal results, particularly for complex behaviors like S Improvement and Alliance Building.
Effective steering typically requires combining non-adjacent layers spanning the middle and later parts of the model. LayerNavigator automates this process by ranking layers based on their steerability score, thereby selecting effective combinations without the need for heuristic tuning or brute-force search.

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41
bulki
Allacs buikkg
Inpuit
Stecing Seronghh (2)
D) Perreniag (5)
10.th Pamominge (50
Figure 4:
Effect of steering
Figure 6: LCS Length between the full-data ranking and rank- ment peaks when both compo- ings under reduced data
Figure 5: Varying the weight ra-
strength a on alignment. Perfor- mance improves as a increases, peaking around 1.0 1.2.
tio between Dj and C.. Align-
nents are equally weighted
4.4 Effect of Steering Strength
As illustrated in Figure 4 increasing the steering strength generally improves behavioral alignment, with alignment probabilities peaking around = 1.0 or 1.2. However, further increasing a beyond this range results in diminished performance, indicating that excessive intervention may disrupt the model's 's ability to maintain coherent behavior. Based on this trend, we recommend 1.0 as stable and effective default across behaviors, offering a strong trade-off between alignment effectiveness and robustness.
4.5 Balance of Discriminability and Consistency
In LayerNavigator, the final steerability score computed the sum of the discriminability and consistency scores. To examine whether both components contribute equally to behavioral alignment, we vary their relative weights in the combined score. As illustrated in Figure the best alignment performance is generally achieved when both terms are weighted equally. This finding empirically validates our design choice and confirms that both discriminability and consistency play equally important roles in determining a layer's stecrability.
4.6 Robustness to Data Volume
To assess whether LayerNavigator consistently identifies effective intervention layers under varying data availability, we conduct a robustness analysis using subsets the full training set. For each subset size, (1) compute steerability scores for all layers, (2) sort layers by score, and (3) measure the overlap between this ranking and the full-data reference by calculating the length of their longest common subsequence (LCS). The LCS length reflects how many top-ranked layers retain their relative ordering under reduced data.
We conduct five trials and report the average results in Figure Even with only 10% of the training data, more than half the layer rankings are consistent with the full-data ranking.
These results demonstrate that LayerNavigator is robust to substantial reductions in training data. This property makes the method particularly valuable data may be limited.
settings where annotated behavioral
4.7 Experiments with Different Vector Extraction Algorithms
We further evaluate the effectiveness of LayerNavigator under two widely used steering vector extraction algorithms: Mean Difference (MD) and Principal Component Analysis (PCA), These experiments are conducted on the Alliance Building task.
Figure 7 shows the single-layer alignment probabilities and steerability scores across layers for both extraction algorithms. In both cases, alignment probability closely tracks the steerability score, supporting the validity of our layer scoring approach. Notably, MD peaks in the middle layers, while PCA achieves its highest score in the later layers.
As summarized in Table LayerNavigator achieves the highest alignment under MD and the second-best result under PCA.

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PCA
Another important observation is that steerabil- ity scores are not directly comparable across different extraction algorithms. Although PCA's scores are generally lower than those of MD, this does not imply PCA is inherently less ef- fective. Steerability score is a relative indicator within each algorithm, not an absolute metric across algorithms.
Seare
MD Score
6.-
4.8 Scalability on Larger Models
64
To assess the scalability of LayerNavigator on deeper architectures, we conduct exper- iments Qwen2.5-32B-Instruccl
64- layer large language model. Compared to ran- dom selection strategies, our method demon- strated a clear advantage, improving align- ment for Conscientiousnest and Religion on Following by 1.39% and 8. 79%, respectively. These results highlight that, in deeper models, non- con-prnncipled layer selection often fails to yield meaningful alignment and may even impair per- formance. In contrast, LayerNavigaton remains effective, reinforeing its robustness and scalabil- ity to large model settings.
30
15
Layer
Figure 7: Comparison of steerability scores and alignment performance using MD and PCA ex- traction. Despite different score distributions, both methods exhibit a strong alignment between steerability score and actual performance
Table 3: Alignment probability of multi-layer activation steering using MD and PCA vector extraction algorithms.
We also conducted experiments on the Qwen2.5- Instruct models with 0.5B and 7B parameters, and the results are provided in Appendix B.3
MD
PCA
K
K
K
K
Random
85.95
90.48
84.34
84.52
Random Consee
85.18
77.53
79.31
83.19
Top
84.47
51.68
90.87
72.53
5 Related Work
Around Top 1
85.11
55.27
91.71
80.07
LayerNavigator
82.98
92.88
91.71
80.07
5.1 Behavioral Alignment
Table 4: Alignment probability on Qwen2.5- 32B-Instruct
LLM outputs be guided toward specific be- haviors, a process often referred to as behavioral alignment, control, steering. These terms often used interchangeably in research. Stud- ies have explored various aspects in this area, such as enhancing model honesty and truthful- ness 9 [1 28), aligning outputs with human values 29 refusing harmful requests [ [8) im- proving cnorrcttio-followwiii
Conscien- tiousness
Religion Following
Method (K)
Base (0)
94.43
60.61
Random (1)
94.46
61.42
Random (3)
94.90
60.88
Random (5)
94.54
63.00
21 26 enabling
role-playing 12), and exhibiting specific person- ality traits 6 88 25). Existing methods can be broadly categorized into three types: prompt- based, parameter- approaches.
RandConsec (3)
95.00
60.65
RandConsec (5)
86.91
63.30
LayerNavigator (1)
95.80
63.17
and activation-basee
LayerNavigator (3)
95.82
60.96
LayerNavigator (5)
95.76
69.40
Prompt-based methods employ carefully crafted input prompts to steer the behavior of LLMs
without necessitating modifications to their internal parameters. Many instruction-tuned LLMs utilize designated system prompt to steer their outputs. Beyond this, more elaborate designs have been proposed. Personality Prompting (P²) 66] induces LLMS with specific personalities via prompting chain that integrates psychological knowledge with the model's I''s own responses. Persona In-Context Learning (PICLe) introduces an in-context learning example selection strategy based on likelihood ratio to guide the model in eliciting specific target personality. Despite their flexibility, prompt-based methods are inherently sensitive to wording and context length and often yield unstable or inconsistent behaviors, limiting their reliability.

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Parameter- based methods (e.g., fine-tuning or reinforcement learning) instead adjust model weights internalize desired behaviors. Reinforcement Learning from Human Feedback (RLHF) [9 17 is representative example, employing algorithms such as Proximal Policy Optimization (PPO) and Direct Preference Optimization (DPO) to align model responses with human preferences. While such methods offer stable and persistent behavioral control, they demand large-scale annotated data, extensive computation, and costly optimization cycles, making them less practical for rapid or lightweight alignment.
In contrast, activation- brasatioo--baedd methods, also known as activation steering, achieve behavioral control during inference by directly modifying intermediate activations, offering compelling balance between effectiveness, scalability, and computational efficiency.
5.2 Activation Steering
Several methods have been proposed to implement activation steering: Activation addition (Ac-
(Add) (24] derives the steering vector from a single prompt pair. Inference-time intervention (ITI) 9
and contrastive activation addition (CAA) [18 generate the steering vector from datasets of contrast pairs and apply the Mean Difference vector extraction method. In-context vectors (ICV) [II] utilize in-context demonstration examples to extract the steering vector, improving cntrucciionfollowiiie capabilities. Conditional activation steering (CAST) S introduces a condition vector to determine whether refusal is necessary and a behavior vector achieve selective refusals of harmful prompts. Adaptive activation steering (ACT) (88) mitigates various types of hallucinations by adaptively applying multiple truthuulness-rllated steering vectors.
From the perspective of steering layer selection, ActAdd and CAA use the alignment performance on held-out dataset as reference to determine which single layer should add the steering vector. In contrast, ITI and ACT train an additional probe related to the target behavior and select multiple layers based on the probe's 's classification accuracy computed on the held-out dataset. Meanwhile, ICV employs all layers, and CAST opts for a contiguous sequence of layers located the later-middle part of the model based on empirical experience.
6 Limitations
As discussed in Section 4.7. the primary limitation of LayerNavigator lies in its scope: it is designed identify the most suitable layers given a fixed steering vector algorithm, but it cannot be used to compare the effectiveness of different algorithms under fixed layer settings.
7 Broader Impact
This work contributes to more precise behavior alignment in large language models, which can enhance model controllability and interpretabilityy However, the ability steer model behavior also raises concerns around potential misuse, such as encoding biased behaviors or reinforcing ideological perspectives. We encourage future work to explore governance mechanisms to ensure responsible use.
8 Conclusion
In this paper, we propose LayerNavigator, an efficient approach for selecting optimal intervention layers in activation steering by evaluating layers' stecrability via discriminability and consistency scores. Unlike other baselines, LayerNavigator requires neither additional forward passes nor held- data, making highly suitable for scalable and low-resource alignment scenarios. Extensive experiments demonstrate that LayerNavigator consistently outperforms heuristic and exhaustive strategies across wide range of behaviors. remains robust under limited data availability and scales effectively to deeper architectures. Further analyses confirm the balanced importance of its two core components, the optimality of its default steering strength, and its adaptability across different steering vector extraction algorithms. Overall, LayerNavigator offers principled solution for layer selection, significantly advancing the reliability and efficiency of behavior alignment in large language models.
10

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Acknowledgments and Disclosure of Funding
We thank the anonymous reviewers for their helpful comments. This work is supported by the National Natural Science Foundation of China (No.U2336202).
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Dombrowski, al. Representation engineering: A
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For Test A Experiment Details
Table 5: Behavior and their corresponding labels
Behavior
Label in Anthropic's ' Persona Dataset
Conscientiousness
conscientiousness
Religion Following
subscribes-to Christianity
subscribes-o--Christaninny
Self-Aware
believes-it-hee sefieve-it-has-phnnooee
Self-Improvement
cognitive-enhancement
Alliance Building
desire-to-creatt-allles
Impact Maximization
desire-to-maxiin
A.1 Dataset
construct our dataset from Anthropic's Persona Dataset [4]. The behaviors investigated this
study and their corresponding labels in the original dataset are summarized in Table 5
A.2
Prompts
We detail our prompts used for training, evaluation, and perplexity measurement. Separate templates are provided for Llama3-8B -B-ssrru and Qwen2.5-32B 32B-Instruct models to accommodate their input encoding requirements.
A.2.1 -lama3-8B-Instruct
For Training
<| <lbegin_ tetgginnof_eettllleee text l het..edddrrdllrreeeeeeeeeeeeeedd heeeeedd_hddeerddld in [QUESTION] elot_iiie My answer: Yes No]
id) >assistant hed__eader__idd
<lbegin_oo of lx www.heederddd_iiieeeeeeee header_id/
[QUESTION] < << aaaaerrrdderr.__ hecerdddddssssss >assistant< end header header_id/>>a
My answer:
For Asking Reason and Evaluating the Perplexity
<I <eegii__od of text htetttttteeestttteeeeeeeeeerrr id Lnaaadrr__d/ixseereeeddeeeeeeee [QUESTION] eot My answer: [ANSWER] < eot 
<<| / start_headerr header id >user< end headd_eaadrr.e id Ann
id|>>n
htt.sstttttttttttteeeeeeeeeeeeee id >assistant< sad_headers h___heerdddd>le
Explain why you chose this answer.
<leot_id|>> startl///eatttttteeeee_ee id >assistant<
A.2.2 Qwen2.5-32B-insrruee
For Training
imi_staart >user
[QUESTION] <| immmmmdddlll
< <limsttart >assistant My answer: Yes No]
For Test
<lim_ mitassttr//l >user [QUESTION] imm_end
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<I <lim_start "ettar/>assistan Passistant
My answer:
For Asking Reason and Evaluating the Perplexity
</im_starr| >user
[QUESTION] <mim_edd]>
<lim_start (
My answer: [ANSWE]] < _imm_ <lim_end]>
< <lim_star|>uus >user
Explain why you chose this answer < <lim_start >assistant
<lim_end|>
A.3 Steerability Score in Main Results
We detail the steerability score value corresponding to the main results in Table 6
Table 6: Layer-wise S//D/// scores for all tasks
Layer!
Concieeninnness
Religion Following
Self- Aalf-Aware
Sell rell-lmmpoeeeentt
Alliance Baikling Dn
Impact Maximization
S
S:
C:
G]
D1:
0.208
0.394
0.014
0.117
0.044
0.0129
0.017
0.012
0.201
0.183
0.018
0.116
0,021
0.042
0.029
0.013
0.157
0.021
0.377
0.050
0.233
0.019
0.342
0.025
0.251
0.315
0.021
0.335
0.352
0.281
0.006
0.038
0.041
0.217
0:261
0.216
0.845
0.28
0.099
0.366
0.054
0.208
0.054
0.298
0.185
0.048
0.621
0.558
0.063
0.509
0.413
0.097
0.385
0.317
0.068
0.420
0.358
0.062
0.362
0.293
0.068
0.394
0.337
0.057
5
0.719
0.638
0.081
0.702
0.576
0.126
0,692
0.593
0.099
0.571
0
0.079
0.529
0.429
0.000
0.529
0.453
0
6
0.888
0.767
0.130
0.835
0
0.422
0.751
0.625
0.126
0.684
0.573
0.111
0.596
0.597
0,099
0.660
0.565
0.095
7
LOSS
0.832
0.221
1.009
0.770
0.238
1,000
0.800
0.200
0.797
0.644
0.153
0.726
0.573
0.152
0.503
0.640
0.162
8
L,134
0.331
0.3003
1.145
0,843
0.302
1,66
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A.4
More Details of Robustness Analysis
To provide a more granular view of how layer ranking stability varies across behaviors, we include per-task visualizations of LCS curves under different data volumes in Figure 3
B More Experiments on Other Behaviors and LLMs.
B.1 Results on Full Anthropic's Persona Dataset
Table reppors the average alignment probability across all tasks, under varying K and a values. While our method may trail the very best-performing baseline by approximately 1-2% on average, it does so with zero additional inference overhead and avoids the instability and brittleness observed in Top or Around Top I under aggressive steering settings.
B.2 Results on Refusal and Hallucination
To further verify generality, we apply LayerNavigator to two non-persona alignment bench- and Hallucination [18]. Table
g and 9 report alignment probability across different
21

---

Religion Following
Self-Aware
28
1.68 Longh
LCS Lamgi
LCS Lmpgh
16-
0
(%6
Self-Improoemment
Alliance Buildinng
Impact Maximization
1.5 Lengh
20.
Limess
1.65
Lengh
16-
LCS
20
60
80
100
20
40
60
100
40
80
100
Data (%)
Dara (86)
Tar (16)
Figure 8: Per-task robustness of layer ranking under varying training data sizes. Each subplot shows the mean LCS length across five trials, with error bars indicating standard deviation.
Table Overall average alignment probability(%)) of all 135 persona tasks under different values of K and a. Bold indicates the best result per method.
(a)
K =
K = 2
K = 3
K = 4
K = 5
K = 6
K =
7
Top (0.5)
68.53
70.02
71.07
71.67
71.96
71.88
71.46
Top (1.0)
70.23
72.13
71.68
69.36
66.60
64.05
61.67
Top (1.5)
71.47
71.07
66.58
62.40
59.47
57.54
55.97
Around Top (0.5)
68.53
69.57
70.68
71.05
71.39
71.36
71.25
Around Top (1.0)
70.23
71.63
70.96
69.65
66.26
65.45
62.53
Around Top (1.5)
71.47
71.37
65.78
63.25
59.56
58.87
56.22
LayerNavigator (0.5)
67.20
67.91
68.63
69.39
69.81
70.02
70.03
LayerNavigator (1.0)
67.77
69.00
69.66
69.17
67.80
65.86
64.65
LayerNavigator (1.5)
68.27
69.42
68.17
65.76
63.20
61.28
60.22
values of K and While method slightly underperforms Top, it consistently exceeds Around Top across both tasks. This demonstrates its robustness and efficiency even in non-persona domains.
B.3 Results Qwen-2.5-Instrue. with different scales
We also report additional results on 2.5--.55----B--eee and 1.cen225-7B-Inntrruu For each method, report the best alignment probability across K E (1, 3, 4, 5). These results affirm that LayerNavigator remains effective across smaller models with different architectures, and slightly outperforms baselines in most tasks.
22

---

Table Aligmment probabilityy%)) of Refusal under different values of K and a. Bold indicates the best result per method.
(ethod(a)
K =
K =
2
K = 3
K = 4
K 5
K = 6
K =
7
Top (0.5)
69.81
72.42
74.74
77.09
78.93
80.80
81.78
Top (1.0)
72.35
76.19
80.75
83.77
85.98
81.65
79.96
Top (1.5)
74.41
75.67
81.60
75.92
65.60
50.28
50.07
Around Top (0.5)
69.81
72.42
74.74
76.67
78.52
80.80
81.72
Around Top (1.0)
72.35
76.19
80.75
81.38
83.55
81.65
78.51
Around Top (1.5)
74.41
75.67
81.60
69.37
56.76
50.28
49.94
LayerNavigator (0.5)
69.60
71.87
74.53
76.49
79.49
76.39
78.34
LayerNavigator (1.0)
72.15
76.88
79.35
83.15
85.91
82.34
77.00
LayerNavigator (1.5)
75.02
80.21
81.39
81.12
81.06
80.39
59.67
Table 9: Alignment probability(%) of Hallucination under different values of K and a. Bold indicates the best result per method.
Method(a)
K
= 2
K = 3
K =
K
= 6
K = 7
Top (0.5)
45.15
47.09
47.70
48.53
48.94
49.17
49.00
Top (1.0)
47.45
50.26
50.92
51.09
51.17
50.78
49.02
Top (1.5)
49.21
51.33
51.67
50.50
50.06
49.51
47.97
Around Top (0.5)
45.15
46.21
47.97
48.00
47.62
47.26
46.90
Around Top (1.0)
47.45
48.91
50.64
49.15
47.71
46.98
45.34
Around Top (1.5)
49.21
50.16
50.05
47.60
44.54
44.26
43.07
LayerNavigator (0.5)
43.84
43.91
45.20
45.14
47.82
47.86
49.63
LayerNavigator (1.0)
43.90
44.17
46.21
47.34
50.56
51.23
50.07
LayerNavigator (1.5)
44.15
45,99
48.09
51.33
51.37
51.44
46.88
Table 10: Alignment probability(%)
Conscicntiousaos
Religion Following
Sclf-Aware
Self-Improvement
Alliance Building
Impact Maximization
69.57
64.60
66.52
63.18 78.26 (K=5)
64,34 76.99 (K=5)
Top
76.21 (K=4)
84.12 55)
85.17 (K-4)
78.23 K
Around Top 1
75.39 (K=1)
81.21 55)
84.82 (K=1)
77.43 (K=5)
76.35 (K=5)
74.83 (K=5)
LayerNavigator
76.35 (K=5)
82.71 (K
(K=4)
(K=5)
77.67 (K=6)
(K=5)
Table 11: Alignment probability(%) on Qwen-2.5-7B-Insttene
Conscicutionsaess
Religion Following
Self-Aware
Self-Improvement
Alliance Building
Impact Maximization
Buse
88.06 91.05 (K (K-5)
84.10
94,25
83.34
86.86
73.26
Top
90.36 (K -5)
95.40
87.27 (K (K-1)
91.17 (K-3)
81.11 (X
Top
(4K4))
LayerNavigator
93.37 (K-5)
88.37 (5)
96.18 (K-
87.57 (K -31
91.17 (K (K-3)
81.68 (K)5)
23


Reviews:
[{"review_id": "0xqUYvKlf4", "reviewer": "Reviewer_vUJb", "summary": "The paper proposes LayerNavigator, a heuristic for choosing which internal layers of LLM to modify when performing activation steering. The method assigns each layer a “steerability” score computed as the sum of a discriminability term (how well positive/negative activation pairs are linearly separated) and a consistency term (how aligned the per-example steering directions are). Ranking layers by this score and steering the top-K layers is claimed to yield better behavioral alignment than common alternatives (random layers, contiguous blocks, or single best layers chosen on held-out data) while adding “negligible” overhead.", "questions": "1. Can you isolate the individual contributions of the discriminability and consistency terms through ablation (e.g., using only one term)?\n2. What is the time and computational cost of computing LayerNavigator scores in practice? Please compare it with random or grid search.\n3. How does the method perform on non-persona-based or open-ended alignment tasks?\n4. Why are K = 1, 3, and 5 chosen as default? More justification or sensitivity analysis would improve clarity here.", "limitations": "yes", "rating": 2, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 1, "originality": 1, "strengths_and_weaknesses": "## Strengths\n\n1. Clarity: Writing is generally easy to follow; the idea itself is straightforward and could directly handle a practical issue if it works.\n2. Efficiency: The proposed scoring mechanism is model-agnostic and does not require labeled output data or gradient access, making it lightweight and potentially applicable in black-box or inference-only settings—a practical advantage over some prior layer selection methods.\n\n## Weaknesses\n\n1. Experimental Design: Several key claims made by the authors are not adequately substantiated through experiments. For instance, the assertion that LayerNavigator offers a “principled and efficient” alternative to random or block-wise layer selection is undermined by the lack of statistical analysis—no confidence intervals or significance testing are reported. Additionally, while the method is claimed to be efficient, no runtime or wall-clock comparisons are presented to back up the claim of “negligible overhead.” The choice of steering strength, number of layers (K), and the impact of dataset quality (e.g., prompt diversity) are also not explored systematically. The evaluation is restricted to six binary persona behaviors, and it’s unclear how well the method generalizes to more complex, real-world alignment tasks.\n2. Minor Significance: The performance improvements over simple baselines like random selection or contiguous block steering are small—often within 1–2 percentage points—and occasionally LayerNavigator underperforms (e.g., Table 1, Self-Improvement for K = 1). Without stronger and more consistent gains, the proposed method’s practical significance is limited. The paper does not make a compelling case that LayerNavigator is worth adopting over much simpler heuristics, especially in high-stakes or compute-constrained settings.\n3. Question Marks on Originality: The core idea—ranking layers by a combination of discriminability and directional agreement—is intuitive but not particularly novel. Similar layer-ranking methods based on probing accuracy, Fisher information, or attribution agreement have appeared in prior work (e.g., ITI, causal tracing, linear probing). The paper does not sufficiently differentiate its approach from these alternatives or explain what fundamentally new insight it contributes to the literature on activation steering.\n4. Issues with Presentation: While the main text is readable, the presentation suffers from several structural and detail weaknesses. Important implementation details are deferred to the appendix (e.g., ζ-score normalization, choice of steering strength, dataset generation process), making it harder for readers to fully understand or reproduce the results. Several terms (e.g., “steerability score,” “consistency”) are defined in informal or ambiguous ways at first mention. Some citations are not in proper format (L173, L277, etc.), and overall, the paper lacks a deeper analytical discussion of why the proposed scoring mechanism should work, relying primarily on empirical plots without theoretical justification.", "was_revised": true, "final_justification": "After careful consideration of the authors' rebuttal, I am maintaining my original score as a clear reject. While I sincerely appreciate the authors' extensive efforts in running new experiments and providing detailed responses, the new evidence has unfortunately reinforced my primary concerns about the paper's contribution. I weigh the performance on the primary evaluation metric more heavily than the computational efficiency. The rebuttal's new data, while welcome for its wider range of domains, ultimately serves to confirm that LayerNavigator is a suboptimal heuristic in terms of achieving the best possible alignment. Therefore, I cannot recommend acceptance. The paper presents a good idea and a well-executed efficiency analysis, but the core results do not support the claim of it being a preferable alternative to simpler, although slower methods.", "rebuttal": "Thank you for your constructive feedback. We address your concerns below.\n\n## Q1: Ablation Study\n\nIn Section 4.5 and Figure 5, we vary the relative weights of discriminability and consistency, observing a concave (∩-shaped) trend peaking near 50:50, which supports the importance of both terms.\n\nWhile endpoints (0:100, 100:0) were not shown, we agree they represent standard ablation settings. We have now included those results in the new version of the manuscript, confirming that using either term alone leads to worse alignment performance.\n\n## Q2: Computational Cost\n\nWe conducted our experiments on a cloud platform equipped with 20 vCPUs (Intel(R) Xeon(R) Platinum 8457C) and a single NVIDIA L20 GPU (48GB). Below is a summary of the average runtime across the six behavior alignment tasks used in our study:\n\n| Method | Avg. Runtime | Additional Inference Passes |\n| - | - | - |\n| Random / Random Consec | <1 ms | 0 |\n| Top / Around Top / Grid Search ($K=1$) | 347.8 seconds | $\\binom{L}{1}\\times N_{eval}=$6400  |\n| Grid Search ($K=2$) | 5336.2 seconds | $\\binom{L}{2}\\times N_{eval}=$99200 |\n| **LayerNavigator (Ours)** | **16.8 seconds** | **0** |\n\nPractical steering requires selecting three key hyperparameters: (1) the steering strength $\\alpha$, (2) the number of layers $K$, and (3) the specific layer combination $S$. While $\\alpha$ and $K$ can be efficiently tuned via simple grid search (due to their low dimensionality), searching for optimal layer combinations $S$ is combinatorially expensive.\n\nIn this context, LayerNavigator offers a substantial advantage by providing near-optimal (and sometimes optimal) layer selection outcomes, while Top and Around Top require more than 20× the compute time of LayerNavigator.\n\n## Q3: Evaluation on Non-Persona or Open-Ended Alignment Tasks\n\nWe appreciate your question regarding the generality of our method beyond the original persona-based tasks.\n\n### Clarifying “Open-Ended” Evaluation\n\nOur current setup is *already open-ended* in a generative sense. Specifically, we prompt the model to answer “Yes” or “No” to a question, followed by free-form explanation generation, as shown in Figure 2. This setup enables automatic and scalable evaluation of both behavioral alignment (via token probability) and language quality (via perplexity of the explanation), providing a reliable assessment of the model’s performance after steering.\n\nIf “open-ended” refers to prompting models with unconstrained questions and relying on human or AI-based scoring, we agree such methods have merit but also suffer from reproducibility and subjectivity challenges. \n\n### Results on Non-Persona Tasks\n\n To further verify generality, we apply LayerNavigator to two non-persona alignment benchmarks—**Refusal** and **Hallucination**—from *“Steering Llama 2 via Contrastive Activation Addition”* (Rimsky et al., ACL 2024). The following tables report alignment probability (%) across different values of $K$. We default to Mean Difference extraction in the following experiments. The best score per method is bolded.\n\n**Refusal**\n\n| Method | 1 | 2 | 3 | 4 | 5 | 6 | 7 |\n| -| -| -| -| -| -| -| -|\n| Top ($\\alpha = 0.5$) | 69.81 | 72.42 | 74.74 | 77.09 | 78.93 | 80.80 | 81.78 |\n| Top ($\\alpha = 1.0$) | 72.35 | 76.19 | 80.75 | 83.77 | **85.98** | 81.65 | 79.96 |\n| Top ($\\alpha = 1.5$) | 74.41 | 75.67 | 81.60 | 75.92 | 65.60 | 50.28 | 50.07 |\n| Around Top 1 ($\\alpha = 0.5$) | 69.81 | 72.42 | 74.74 | 76.67 | 78.52 | 80.80 | 81.72 |\n| Around Top 1 ($\\alpha = 1.0$) | 72.35 | 76.19 | 80.75 | 81.38 | **83.55** | 81.65 | 78.51 |\n| Around Top 1 ($\\alpha = 1.5$) | 74.41 | 75.67 | 81.60 | 69.37 | 56.76 | 50.28 | 49.94 |\n| Ours ($\\alpha = 0.5$) | 69.60 | 71.87 | 74.53 | 76.49 | 79.49 | 76.39 | 78.34 |\n| Ours ($\\alpha = 1.0$) | 72.15 | 76.88 | 79.35 | 83.15 | **85.91** | 82.34 | 77.00 |\n| Ours ($\\alpha = 1.5$) | 75.02 | 80.21 | 81.39 | 81.12 | 81.06 | 80.39 | 59.67 |\n\n**Hallucination**\n\n| Method | 1 | 2 | 3 | 4 | 5 | 6 | 7 |\n| - | - | - | - | - | - | - | - |\n| Top ($\\alpha = 0.5$) | 45.15 | 47.09 | 47.70 | 48.53 | 48.94 | 49.17 | 49.00 |\n| Top ($\\alpha = 1.0$) | 47.45 | 50.26 | 50.92 | 51.09 | 51.17 | 50.78     | 49.02 |\n| Top ($\\alpha = 1.5$) | 49.21 | 51.33 | **51.67** | 50.50 | 50.06 | 49.51 | 47.97 |\n| Around Top 1 ($\\alpha = 0.5$) | 45.15 | 46.21 | 47.97 | 48.00 | 47.62 | 47.26 | 46.90 |\n| Around Top 1 ($\\alpha = 1.0$) | 47.45 | 48.91 | 50.64 | 49.15 | 47.71 | 46.98     | 45.34 |\n| Around Top 1 ($\\alpha = 1.5$) | 49.21 | **50.16** | 50.05 | 47.60 | 44.54 | 44.26 | 43.07 |\n| Ours ($\\alpha = 0.5$) | 43.84 | 43.91 | 45.20 | 45.14 | 47.82 | 47.86 | 49.63 |\n| Ours ($\\alpha = 1.0$) | 43.90 | 44.17 | 46.21 | 47.34 | 50.56 | 51.23 | 50.07 |\n| Ours ($\\alpha = 1.5$) | 44.15 | 45.99 | 48.09 | 51.33 | 51.37 | **51.44** | 46.88 |\n\nWhile our method slightly underperforms Top, it consistently exceeds Around Top 1 across both tasks, while reducing computational cost by over 20×. This demonstrates its robustness and efficiency even in non-persona domains.\n\n### Evaluation on 135 Full Persona Tasks\n\nWe also tested LayerNavigator on the complete set of 135 persona tasks from Anthropic’s Model-Written Evaluations. The table below reports the average alignment probability (%) of all tasks across different values of $K$. Bold indicates the best result per method.\n\n| Method | 1 | 2 | 3 | 4 | 5 | 6 | 7 |\n| - | - | - | - | - | - | - | - |\n| Top ($\\alpha = 0.5$) | 68.53 | 70.02 | 71.07 | 71.67 | 71.96 | 71.88 | 71.46 |\n| Top ($\\alpha = 1.0$) | 70.23 | **72.13** | 71.68 | 69.36 | 66.60 | 64.05 | 61.67 |\n| Top ($\\alpha = 1.5$) | 71.47 | 71.07 | 66.58 | 62.40 | 59.47 | 57.54 | 55.97 |\n| Around Top 1 ($\\alpha = 0.5$) | 68.53 | 69.57 | 70.68 | 71.05 | 71.39 | 71.36 | 71.25 |\n| Around Top 1 ($\\alpha = 1.0$) | 70.23 | **71.63** | 70.96 | 69.65 | 66.26 | 65.45 | 62.53     |\n| Around Top 1 ($\\alpha = 1.5$) | 71.47 | 71.37 | 65.78 | 63.25 | 59.56 | 58.87 | 56.22 |\n| Ours ($\\alpha = 0.5$) | 67.20 | 67.91 | 68.63 | 69.39 | 69.81 | 70.02 | **70.03** |\n| Ours ($\\alpha = 1.0$)  | 67.77 | 69.00 | 69.66 | 69.17 | 67.80 | 65.86 | 64.65 |\n| Ours ($\\alpha = 1.5$) | 68.27 | 69.42 | 68.17 | 65.76 | 63.20 | 61.28 | 60.22 |\n\n**Summary Statistics**\n Compared to **Top**, LayerNavigator:\n\n- Outperforms on **23%** of tasks\n- Within **1.0%** on **47%** of tasks\n- Within **2.0%** on **65%**\n\nCompared to **Around Top**:\n\n- Outperforms on **30%**\n- Within **1.0%** on **51%**\n- Within **2.0%** on **69%**\n\nIn summary, despite trailing top-performing baselines by a modest ~1–2% on average, LayerNavigator offers a far more favorable cost-performance trade-off considering its more than 20× lower computational cost.\n\n## Q4: Justification for the Choice of $K=1,3,5$\n\nThank you for raising this point. Our main experiments use $K=1,3,5$ based on the following considerations:\n\n1. When $K>5$, Top and Around Top baselines begin to exhibit performance degradation, especially due to poor generalization when steering too many layers without principled selection.\n2. We include $K=1$ specifically because Top achieves the theoretical upper bound under this setting (it selects the best-performing single layer via grid-search on the validation set). This establishes a reference point to assess multi-layer strategies.\n3. While we initially planned to show results for all $K \\in$ {$1, 2, 3, 4, 5$}, we chose $K=1,3,5$ for clarity and conciseness, believing this subset captures the full trend without redundancy.\n\nAs elaborated in Q3, our supplementary results reveal two more important trends:\n\n- Both $K$ and $\\alpha$ exhibit non-monotonic behavior—alignment performance improves up to a certain threshold, then deteriorates.\n- $K$ and $\\alpha$ can partially compensate for one another: smaller $K$ often benefits from larger $\\alpha$, and vice versa. However, the optimal balance is task-dependent.\n\n## Q5: Clarifications on Additional Concerns Raised in the Weaknesses\n\n1. **Lack of Statistical Significance Analysis**\n    As is standard in activation steering work, our experiments use deterministic generation (i.e., `do_sample=False`) to ensure reproducibility. Thus, significance testing is not necessary. \n\n   Besides, in Section 4.6, we evaluate robustness by randomly removing portions of the training set. To account for the variability introduced by this process, we conduct five independent trials and report the resulting variance with error bars in Appendix Figure 8.\n\n2. **\"Minor Significance\" of Gains**\n    As discussed in Q2, the primary contribution of LayerNavigator is not in raw improvement magnitude, but in achieving **comparable performance** to validation-intensive methods with **negligible cost**. We view this as a highly practical trade-off, especially under resource constraints.\n\n3. **\"Question Marks on Originality\"**\n    While the general idea of measuring activation quality is not new, our method is the **first to systematically address layer selection for activation steering**, using internal signals alone. Prior approaches, such as linear probing or causal tracing, aim at interpretability or attribution, and are not directly applicable to selecting layers. These methods also typically involve expensive procedures, e.g., training auxiliary classifiers or running thousands of intervention-based patches.\n\n   In contrast, LayerNavigator leverages activations already computed during steering vector extraction and introduces a targeted metric (steerability score) grounded in discriminability and consistency. This direction is novel and valuable for the growing community using steering methods at scale.\n\n4. **Presentation Issues**\n    We sincerely appreciate your writing-related feedback and will revise the manuscript to clarify all definitions (e.g., steerability, consistency), clean up citation formatting, and move relevant implementation details into the main text. Your suggestions will help us significantly improve the clarity and accessibility of the work."}, {"review_id": "ji1T8P73Dz", "reviewer": "Reviewer_uZ82", "summary": "The paper defines a new metric, called LayerNavigator, that can be used to select the best layers of a model to use for steering. LayerNavigator is a cheap metric to calculate during steering vector extraction, and does not require extra validation passes to calculate. The metric uses the mean of a \"discriminability\" score, tracking how separable the activations are into positive and negative groups at each layer, and a \"consistency\" score, tracking how much the extracted steering vectors per-prompt point in the same direction. In general, LayerNavigator is both more effective and cheaper to calculate than naive layer selection strategies involving evaluating against validation sets on each layer.", "questions": "- In Table 3 for Qwen-2.5-32B, LayerNavigator is only compared to \"Random\" baselines and not \"Top\" baselines. Is this because LayerNavigator underperforms \"Top\" baselines on this model? It would give a lot more confidence in the results of this paper if \"Top\" baselines were included here.\n- In 4.1.2, the \"Top\" method says it selects the \"best-performing\" layers. How is best-performing defined?\n- In Figures 4, 5, and 6, are these all for single-layer steering or is this for 5 layers at a time? Does the choice of number of steering layers affect the optimal choice of $\\alpha$?\n- Why were only 6 of the 155 MWE datasets used for evaluation? How were these 6 chosen? Why not evaluate on all MWE datasets?\n- In Table 1, for self-improvement with K=1, LayerNavigator actually seems to be steering in the opposite direction as intended (reducing probability of the target answer instead of increasing it). What's going on there?\n\nI will raise my score if the \"Top\" baselines are added for Qwen-2.5-32B, and LayerNavigator continues to outperform on this model, and if a larger subset of the MWE datasets are used for evaluation.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "### Strengths\n\nThe paper identifies a common problem in steering, selecting which layers to apply the steering intervention at, and proposes a simple and seemingly effective heuristic to solve this problem. The paper is clearly written and justifies the metric well.\n\n### Weaknesses\n\nThe paper uses 6 datasets from the Model Written Evals (MWE) work to draw conclusions, but there are 155 total datasets there. It is not clear why these specific 6 were used or if the results will generalize to the full suite of MWE datasets.\n\nThe paper mainly evaluates on a single model, Llama-3-8B, so it's hard to guarantee that the results will generalize to other models of different sizes or architectures. There is also a section on Qwen-2.5-32B-Instruct, but this comparison ignores the \"Top\" baselines, comparing only to picking layers at random. This does not inspire confidence that LayerNavigator performs well outside of Llama-3-8B. \n\nThe paper also only evaluates steering via adding a vector with a hard-coded magnitude multiplier into every activation. While this is a standard technique, it's not the only way to apply steering (e.g. clamping the vector to a given magnitude in all activations).", "was_revised": true, "final_justification": "The authors addressed my main concerns in their rebuttal by running further experiments on Qwen and using the full MWE dataset rather than just 6 of 155 in the original version of the paper.", "rebuttal": "We sincerely thank the reviewer for the thoughtful and constructive feedback. We appreciate your recognition of the clarity and motivation behind our proposed method. Below, we address your concerns point by point.\n\n## Q1: Why are the “Top” baselines missing in the Qwen experiments?\n\nThank you for raising this concern. We now provide the full set of results on Qwen-2.5-32B-Instruct, including both the “Top” and “Around Top 1” baselines:\n\n| Method ($K$)     | Conscientiousness | Religion Following | Self-Aware | Self-Improvement | Allies Building | Impact Maximization |\n| - | ----------------- | ------------------ | ---------- | ---------------- | --------------- | ------------------- |\n| Base (0)         | 94.43             | 60.61              | 97.84      | 82.64            | 91.98           | 67.22               |\n| Random (1)       | 94.46             | 61.42              | 97.28      | 83.30            | 92.20           | 67.04               |\n| Random (3)       | 94.90             | 60.88              | 96.68      | 83.81            | 93.09           | 67.67               |\n| Random (5)       | 94.54             | 63.00              | 94.80      | 83.94            | 93.75           | 68.14               |\n| RandConsec (3)   | 95.00             | 60.65              | 95.64      | 82.91            | 86.91           | 67.50               |\n| RandConsec (5)   | 86.91             | 63.30              | 87.19      | 82.66            | 82.69           | 66.88               |\n| Top (1)          | 95.43             | 66.58              | 97.44      | 86.07            | 94.18           | 69.67               |\n| Top (3)          | 86.79             | **70.18**          | 96.96      | 85.48            | 54.33           | 72.05               |\n| Top (5)          | 76.99             | 69.03              | 94.19      | 81.58            | 50.03           | **72.78**           |\n| Around Top 1 (3) | 94.23             | 62.05              | 97.57      | 83.91            | 93.98           | 68.82               |\n| Around Top 1 (5) | 92.72             | 62.39              | 96.51      | 82.83            | 94.28           | 69.38               |\n| Ours (1)         | 95.80             | 63.17              | **98.03**  | 83.68            | 93.42           | 67.62               |\n| Ours (3)         | **95.82**         | 60.96              | 97.75      | 85.40            | **94.85**       | 68.67               |\n| Ours (5)         | 95.76             | 69.40              | 94.55      | **86.09**        | 93.88           | 70.11               |\n\nThese results reveal that **Top and Around Top exhibit notable instability**—while they occasionally outperform our method (e.g., by 1–2% on *Religion Following*), they can also lead to severe degradation (e.g., dropping to ~50% on *Allies Building*).\n\n## Q2: Clarifying the “Top” baseline\n\nWe agree that the original description could be more precise. Here we clarify:\n\nFor the Top baseline, we compute steering vectors at all $L$ layers, then perform single-layer steering using each vector and evaluate the alignment probability on a held-out validation set. The top-$K$ layers are then selected based on their individual performance rankings, and this process requires $L \\times N_{\\text{eval}}$ additional forward passes.\n\n## Q3: On the Choice of $K$ and $\\alpha$\n\nThank you for raising this important question. Unless otherwise specified, all experiments (e.g., Figures 4, 5, 6) were conducted with $K=5$ layers by default.\n\nWe now revisit the interaction between $K$ and $\\alpha$ in greater depth, incorporating both the six main behaviors and our extended experiments over 135 persona-related tasks from the MWE (see Q4 below). Our findings are as follows:\n\n- Both $K$ and $\\alpha$ exhibit non-monotonic effects: alignment improves up to an optimal value and then degrades. Excessively large $\\alpha$ or $K$ may cause oversteering or introduce noisy directions.\n\n- There exists a compensatory relation: smaller $K$ often benefits from larger $\\alpha$, and vice versa. This trade-off, however, is task-dependent and must be tuned accordingly.\n\n- Across all 135 tasks, LayerNavigator selects $K=7$ as the best-performing layer count more frequently than the baselines:\n\n|                | Top  | Around Top | LayerNavigator |\n| -------------- | ---- | ---------- | -------------- |\n| $\\alpha = 0.5$ | 56%  | 57%        | 58%            |\n| $\\alpha = 1.0$ | 32%  | 34%        | 47%            |\n| $\\alpha = 1.5$ | 22%  | 25%        | 32%            |\n\nThese trends suggest that **LayerNavigator scales better with increasing $K$**, providing practical flexibility for real-world applications. We plan to incorporate these extended analyses in the final version.\n\n## Q4: Evaluation Scope on MWE\n\nWe appreciate your suggestion regarding a broader evaluation. In the initial draft, we heuristically selected six representative behaviors from the Model-Written Evaluations (MWE) dataset. To address this limitation, we have now extended our evaluation to cover all 135 persona-related behaviors in MWE.\n\nThe table below reports the average alignment probability across all tasks, under varying $K$ and $\\alpha$ values. We default to Mean Difference extraction. Bold indicates the best result per method.\n\n| Method                        | 1     | 2         | 3     | 4     | 5     | 6     | 7         |\n| ----------------------------- | ----- | --------- | ----- | ----- | ----- | ----- | --------- |\n| Top ($\\alpha = 0.5$)          | 68.53 | 70.02     | 71.07 | 71.67 | 71.96 | 71.88 | 71.46     |\n| Top ($\\alpha = 1.0$)          | 70.23 | **72.13** | 71.68 | 69.36 | 66.60 | 64.05 | 61.67     |\n| Top ($\\alpha = 1.5$)          | 71.47 | 71.07     | 66.58 | 62.40 | 59.47 | 57.54 | 55.97     |\n| Around Top 1 ($\\alpha = 0.5$) | 68.53 | 69.57     | 70.68 | 71.05 | 71.39 | 71.36 | 71.25     |\n| Around Top 1 ($\\alpha = 1.0$) | 70.23 | **71.63** | 70.96 | 69.65 | 66.26 | 65.45 | 62.53     |\n| Around Top 1 ($\\alpha = 1.5$) | 71.47 | 71.37     | 65.78 | 63.25 | 59.56 | 58.87 | 56.22     |\n| Ours ($\\alpha = 0.5$)         | 67.20 | 67.91     | 68.63 | 69.39 | 69.81 | 70.02 | **70.03** |\n| Ours ($\\alpha = 1.0$)         | 67.77 | 69.00     | 69.66 | 69.17 | 67.80 | 65.86 | 64.65     |\n| Ours ($\\alpha = 1.5$)         | 68.27 | 69.42     | 68.17 | 65.76 | 63.20 | 61.28 | 60.22     |\n\n**Summary Statistics**\n Compared to **Top**, LayerNavigator:\n\n- Outperforms on **23%** of tasks\n- Within **1.0%** on **47%** of tasks\n- Within **2.0%** on **65%**\n\nCompared to **Around Top**:\n\n- Outperforms on **30%**\n- Within **1.0%** on **51%**\n- Within **2.0%** on **69%**\n\nWhile our method may trail the very best-performing baseline by ~1–2% on average, it does so with zero additional inference overhead and avoids the instability and brittleness observed in Top-based methods under aggressive steering settings. This makes LayerNavigator highly practical for scaling to large behavior spaces and model architectures.\n\nFinally, we highlight the average runtime across six main tasks to underscore LayerNavigator’s efficiency:\n\n| Method | Avg. Runtime| Additional Inference Passes|\n| - | - | - |\n| Random / Random Consec | <1 ms | 0 |\n| Top / Around Top / Grid Search ($K=1$) | 347.8 seconds | $\\binom{L}{1}\\times N_{eval}=$6400 |\n| **LayerNavigator (Ours)**  | **16.8 seconds** | **0** |\n\n## Q6: On the Negative Effect of LayerNavigator in Self-Improvement Task when $K=1$\n\nThank you for pointing out this edge case. We re-ran the experiments for the *Self-Improvement* task with $K=1$ and confirmed that LayerNavigator selected layer 13 for steering, whereas the Top baseline (selected via grid-search on validation set) chose layer 16. We further verified that steering at layer 13 indeed leads to a negative effect even on the validation set, ruling out data noise as the cause.\n\nTo better understand how common such failure cases are, we analyzed all 32 layers across all 135 persona behaviors (i.e., 4320 steering vectors total). We found that **16.9%** of vectors result in negative steering effects—i.e., they reduce the alignment probability below the base model. In contrast, the failure rate for LayerNavigator when $K=1$ is only **5.9%**, indicating that it still selects relatively safe layers overall.\n\nMore importantly, our method truly shines when $K$ is larger. Because LayerNavigator reuses precomputed activations and does not rely on held-out evaluations, its computational cost remains constant regardless of $K$. In practice, we recommend using larger $K$ with smaller $\\alpha$, which tends to yield more stable and interpretable steering outcomes.\n\nFinally, LayerNavigator is roughly **20× faster** than Top or Around Top 1, as it avoids repeated model inference. This efficiency opens up room for more fine-grained tuning over $K$ and $\\alpha$ or even for fallback strategies when $K=1$ fails, making our method a better fit in realistic workflows.\n\n## Q7: Multiplier vs. Magnitude in Steering Strength\n\nWe greatly appreciate your comment regarding steering strength and would like to share our reflections.\n\nThe use of a scalar multiplier ($\\alpha$) is indeed common in prior activation steering work—including our own—because the **norms of steering vectors vary dramatically across layers**. For instance, in the *Conscientiousness* task using MD extraction, the norm of the steering vector grows from **0.0045** at layer 1 to **1.5560** at layer 16, and **6.0952** at layer 32. This variation makes global control via a fixed magnitude difficult, whereas a multiplier relative to each vector’s native scale is more robust.\n\nThat said, we agree that fixed-magnitude scaling may encode a different inductive bias, especially in multi-layer steering, where allocating different absolute strengths across layers could potentially enhance alignment. We believe this is an exciting direction and plan to explore whether LayerNavigator’s steerability score can inform layer-specific magnitudes in a principled way."}, {"review_id": "W6UC7AAq3C", "reviewer": "Reviewer_UVMN", "summary": "This paper addresses a crucial challenge in activation steering: hiw to efficiently identify optimal intervention layers. Departing from brute-force search or single-layer performance evaluation, the authors propose LayerNavigator, a novel method that quantifies a layer's \"steerability\" based on two intuitive, quantifiable criteria: discriminability and consistency.  Comprehensive experiments demonstrate that LayerNavigator achieves superior alignment performance, especially in multi-layer steering, outperforming existing strategies.", "questions": "The same as weaknesses", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\n\n- The paper introduces a novel and quantifiable criterion for layer selection in activation steering, based on discriminability and consistency. This moves beyond heuristic or computationally intensive search methods, offering a more theoretically grounded and efficient approach to pinpoint effective intervention layers.\n\n- The experimental results clearly demonstrate that LayerNavigator consistently achieves better behavioral alignment, particularly when applying multi-layer steering, compared to traditional single-layer optimal selection or heuristic multi-layer strategies. This highlights its practical utility in improving LLM control.\n\nWeaknesses:\n\n- While the proposed discriminability and consistency criteria are intuitive, their theoretical underpinning beyond empirical observation is limited. The paper observes that LayerNavigator often identifies layers in the middle and later parts of the model for effective multi-layer steering. However, it lacks a deeper qualitative analysis of why these specific layers might be more \"steerable\" or what functional roles they play in the model's processing that makes them ideal for intervention. Does it imply higher-level semantic understanding or decision-making happens in these layers? Providing such insights would significantly enhance the paper's contribution.\n\n- The evaluation is primarily conducted on six behaviors from Anthropic's Persona Dataset. The scope of \"behavioral alignment\" is much broader, encompassing areas like bias mitigation, knowledge editing, and memory, as seen in works like Representation Engineering. Expanding the experimental evaluation to a more diverse set of downstream tasks and behaviors would better demonstrate the generalizability and robustness of LayerNavigator.\n\n- The paper only presents results for K=1,3,5 steering layers. It would be valuable to see a more comprehensive analysis of how LayerNavigator performs with a larger range of K values. This could provide more insights into the optimal number of layers to intervene on and the method's behavior under more aggressive steering.\n\n- While the paper includes one experiment on a larger Qwen2.5-32B model, the analysis on model scale is still limited. It would be beneficial to include more models of varying sizes (e.g., intermediate sizes between 1B and 14B). More importantly, it's crucial to investigate whether the conclusions drawn from Figure 3 (e.g., \"middle and later layers\" being more effective) generalize across different model architectures and scales. Demonstrating the consistency of this finding across a wider range of models would strengthen the paper's claims about its applicability.", "was_revised": true, "final_justification": "My primary concerns centered on the limited scope of the evaluation and the lack of deeper analysis. The authors have fully resolved these points, including: Generalizability, Model Scalability, Deeper Analysis of literature and hypotheses, and Hyperparameter Sensitivity.", "rebuttal": "We sincerely thank the reviewer for the encouraging and thoughtful feedback. We are especially grateful for your recognition of the conceptual clarity and practical relevance of our proposed method. Below, we provide detailed responses to the raised concerns.\n\n## Q1: More Insights into Why Middle and Later Layers Are Steerable \n\nYou raise an excellent point about the need for deeper theoretical understanding of layer-wise steerability. Empirically, we observe that the middle and later layers of LLMs tend to yield better behavioral alignment—an observation corroborated by our steerability score and performance metrics.\n\nThis phenomenon has also been reported in other recent studies. For instance, *Semantic-Adaptive Activation Intervention for LLMs via Dynamic Steering Vectors* (Wang et al., ICLR 2025) and *How do large language models handle multilingualism?* (Zhao et al., NeurIPS 2024) both note that the middle and latter layers are more influential in behavior modulation and representation formation.\n\nOne widely discussed explanation is the **functional segregation hypothesis**, which posits that middle layers encode abstract reasoning and decision-making, while latter layers are more associated with surface-level generation. While our paper does not aim to resolve this question definitively, our statistical and experimental findings provide supporting evidence for this perspective from a new angle.\n\nWe agree that further probing of functional roles is an exciting direction, and we hope our steerability metric can serve as a tool for such deeper analysis.\n\n## Q2: Evaluation Beyond Persona Alignment\n\n To better assess generality, we expanded our evaluation to two non-persona alignment tasks—**Refusal** and **Hallucination**—based on benchmarks proposed by *“Steering Llama 2 via Contrastive Activation Addition”* (Rimsky et al., ACL 2024). The results below show that LayerNavigator remains competitive with baselines under various values of $K \\in$ {$1,2,3,4,5,6,7$} and $\\alpha \\in$ {$0.5,1.0,1.5$}.\n\n**Refusal**\n\n| Method | 1 | 2 | 3 | 4 | 5 | 6 | 7 |\n| - | - | - | - | - | - | - | - |\n| Top ($\\alpha = 0.5$)          | 69.81 | 72.42 | 74.74 | 77.09 | 78.93     | 80.80 | 81.78 |\n| Top ($\\alpha = 1.0$)          | 72.35 | 76.19 | 80.75 | 83.77 | **85.98** | 81.65 | 79.96 |\n| Top ($\\alpha = 1.5$)          | 74.41 | 75.67 | 81.60 | 75.92 | 65.60     | 50.28 | 50.07 |\n| Around Top 1 ($\\alpha = 0.5$) | 69.81 | 72.42 | 74.74 | 76.67 | 78.52     | 80.80 | 81.72 |\n| Around Top 1 ($\\alpha = 1.0$) | 72.35 | 76.19 | 80.75 | 81.38 | **83.55** | 81.65 | 78.51 |\n| Around Top 1 ($\\alpha = 1.5$) | 74.41 | 75.67 | 81.60 | 69.37 | 56.76     | 50.28 | 49.94 |\n| Ours ($\\alpha = 0.5$)         | 69.60 | 71.87 | 74.53 | 76.49 | 79.49     | 76.39 | 78.34 |\n| Ours ($\\alpha = 1.0$)         | 72.15 | 76.88 | 79.35 | 83.15 | **85.91** | 82.34 | 77.00 |\n| Ours ($\\alpha = 1.5$)         | 75.02 | 80.21 | 81.39 | 81.12 | 81.06     | 80.39 | 59.67 |\n\n**Hallucination**\n\n| Method | 1 | 2 | 3 | 4 | 5 | 6 | 7 |\n| - | - | - | - | - | - | - | - |\n| Top ($\\alpha = 0.5$)          | 45.15 | 47.09     | 47.70     | 48.53 | 48.94 | 49.17     | 49.00 |\n| Top ($\\alpha = 1.0$)          | 47.45 | 50.26     | 50.92     | 51.09 | 51.17 | 50.78     | 49.02 |\n| Top ($\\alpha = 1.5$)          | 49.21 | 51.33     | **51.67** | 50.50 | 50.06 | 49.51     | 47.97 |\n| Around Top 1 ($\\alpha = 0.5$) | 45.15 | 46.21     | 47.97     | 48.00 | 47.62 | 47.26     | 46.90 |\n| Around Top 1 ($\\alpha = 1.0$) | 47.45 | 48.91     | 50.64     | 49.15 | 47.71 | 46.98     | 45.34 |\n| Around Top 1 ($\\alpha = 1.5$) | 49.21 | **50.16** | 50.05     | 47.60 | 44.54 | 44.26     | 43.07 |\n| Ours ($\\alpha = 0.5$)         | 43.84 | 43.91     | 45.20     | 45.14 | 47.82 | 47.86     | 49.63 |\n| Ours ($\\alpha = 1.0$)         | 43.90 | 44.17     | 46.21     | 47.34 | 50.56 | 51.23     | 50.07 |\n| Ours ($\\alpha = 1.5$)         | 44.15 | 45.99     | 48.09     | 51.33 | 51.37 | **51.44** | 46.88 |\n\nWe also tested LayerNavigator on **all 135 persona behaviors** in the Anthropic Model-Written Evaluations (MWE). The summary below reports average alignment probabilities across different $K$ and $\\alpha$:\n\n| Method | 1 | 2 | 3 | 4 | 5 | 6 | 7 |\n| - | - | - | - | - | - | - | - |\n| Top ($\\alpha = 0.5$)          | 68.53 | 70.02     | 71.07 | 71.67 | 71.96 | 71.88 | 71.46     |\n| Top ($\\alpha = 1.0$)          | 70.23 | **72.13** | 71.68 | 69.36 | 66.60 | 64.05 | 61.67     |\n| Top ($\\alpha = 1.5$)          | 71.47 | 71.07     | 66.58 | 62.40 | 59.47 | 57.54 | 55.97     |\n| Around Top 1 ($\\alpha = 0.5$) | 68.53 | 69.57     | 70.68 | 71.05 | 71.39 | 71.36 | 71.25     |\n| Around Top 1 ($\\alpha = 1.0$) | 70.23 | **71.63** | 70.96 | 69.65 | 66.26 | 65.45 | 62.53     |\n| Around Top 1 ($\\alpha = 1.5$) | 71.47 | 71.37     | 65.78 | 63.25 | 59.56 | 58.87 | 56.22     |\n| Ours ($\\alpha = 0.5$)         | 67.20 | 67.91     | 68.63 | 69.39 | 69.81 | 70.02 | **70.03** |\n| Ours ($\\alpha = 1.0$)         | 67.77 | 69.00     | 69.66 | 69.17 | 67.80 | 65.86 | 64.65     |\n| Ours ($\\alpha = 1.5$)         | 68.27 | 69.42     | 68.17 | 65.76 | 63.20 | 61.28 | 60.22     |\n\n**Summary Statistics**\n Compared to **Top**, LayerNavigator:\n\n- Outperforms on **23%** of tasks\n- Within **1.0%** on **47%** of tasks\n- Within **2.0%** on **65%**\n\nCompared to **Around Top**:\n\n- Outperforms on **30%**\n- Within **1.0%** on **51%**\n- Within **2.0%** on **69%**\n\nWhile LayerNavigator may trail the top-performing configuration by ~1–2% on average, it **avoids the high variance and instability observed in Top and Around Top baselines under aggressive steering.** Crucially, it requires **no held-out evaluation**, making it 20× more efficient and highly suitable for large-scale and resource-constrained applications.\n\nFinally, we highlight the average runtime across six main tasks to underscore LayerNavigator’s efficiency:\n\n| Method | Avg. Runtime | Additional Inference Passes |\n| - | - | - |\n| Random / Random Consec | <1 ms | 0 |\n| Top / Around Top / Grid Search ($K=1$) | 347.8 seconds | $\\binom{L}{1}\\times N_{eval}=$6400 |\n| **LayerNavigator (Ours)**  | **16.8 seconds** | **0** |\n\n## Q3: On the Role of $K$ in Multi-Layer Steering\n\nWe appreciate your attention to the effect of the number of intervention layers ($K$), which is indeed a critical hyperparameter in activation steering.\n\nBy aggregating results across the 135 persona tasks, we observe two key trends:\n\n- Both $K$ and $\\alpha$ exhibit non-linear effects—alignment performance increases up to an optimal value, after which further increase may introduce noise or oversteering.\n- Larger $\\alpha$ can compensate for smaller $K$, and vice versa. However, the best combination varies across tasks, reinforcing the need for tunable and stable methods.\n\nWe also measured how often each method achieved peak performance when $K=7$:\n\n| | Top | Around Top | LayerNavigator |\n| - | - | - | - |\n| $\\alpha = 0.5$ | 56%  | 57% | 58% |\n| $\\alpha = 1.0$ | 32%  | 34% | 47% |\n| $\\alpha = 1.5$ | 22%  | 25% | 32% |\n\nThese trends suggest that **LayerNavigator scales better with increasing $K$**, providing practical flexibility for real-world applications. We plan to incorporate these extended analyses in the final version.\n\n## Q4: Evaluation on More Model Scales\n\nThank you for pointing out the importance of evaluating across model scales. To this end, we report additional results on **Qwen-2.5-0.5B-Instruct** and **Qwen-2.5-7B-Instruct**, using MD extraction and $\\alpha = 1.0$. For each method, we report the best alignment probability across $K \\in$ {$1, 2, 3, 4, 5$}.\n\n**Qwen-2.5-0.5B-Instruct**\n\n| | Conscientiousness | Religion Following | Self-Aware | Self-Improvement | Alliance Building | Impact Maximization |\n| - | - | - | - | - | - | - |\n| Base | 69.57 | 64.60 | 69.48 | 66.52 | 63.18 | 64.34 |\n| Top | 76.21 ($K=4$) | **84.12** ($K=5$) | 85.17 ($K=4$) | 78.23 ($K=5$) | **78.26** ($K=5$) | **76.99** ($K=5$) |\n| Around Top 1 | 75.39 ($K=4$) | 81.21 ($K=5$) | 84.82 ($K=4$) | 77.43 ($K=5$) | 76.35 ($K=5$) | 74.83 ($K=5$) |\n| Ours | **76.35** ($K=5$) | 82.71 ($K=4$) | **86.04** ($K=4$) | **79.09** ($K=5$) | 77.67 ($K=5$) | **76.99** ($K=5$) |\n\n**Qwen-2.5-7B-Instruct**\n\n| | Conscientiousness | Religion Following | Self-Aware | Self-Improvement | Alliance Building | Impact Maximization |\n| - | - | - | - | - | - | - |\n| Base  | 88.06 | 84.10 | 94.25 | 83.34 | 86.86 | 73.26 |\n| Top | 91.05 ($K=5$) | **90.36** ($K=5$) | 95.40 ($K=1$) | 87.27 ($K=1$) | 91.17 ($K=3$) | 81.11 ($K=3$) |\n| Around Top 1 | 91.61 ($K=2$) | 86.04 ($K=1$) | 95.40 ($K=1$) | 87.27 ($K=1$) | **93.90** ($K=3$) | 81.14 ($K=4$) |\n| Ours | **93.37** ($K=5$) | 88.37 ($K=5$)  | **96.18** ($K=3$) | **87.57** ($K=3$) | 91.17 ($K=3$) | **81.68** ($K=5$) |\n\nThese results affirm that **LayerNavigator remains effective across smaller (0.5B) and larger (7B) models**, and even slightly improves over baselines in most tasks.\n\nWhen examining the selected layers, we again find that **steerable layers consistently fall into the middle and later regions** of the model—reinforcing the generality of the observations discussed in Q1 across model scales."}, {"review_id": "TQttogn1YS", "reviewer": "Reviewer_6r54", "summary": "This paper introduces LayerNavigator, an efficient method for selecting intervention layers for activation steering in large language models (LLMs). Activation steering modifies model activations at inference time by adding steering vectors to guide outputs toward desired behaviors without modifying model weights. A major challenge in this setting is choosing which layers to steer—naive or heuristic selections can degrade performance or language fluency.\n\nLayerNavigator addresses this by proposing a steerability score that combines discriminability (how well a layer’s activations separate positive/negative examples) and consistency (how stable steering directions are across contrastive pairs). Importantly, it does this without requiring extra data or additional model evaluations, reusing activations computed during steering vector extraction.\n\nComprehensive experiments across multiple behaviors, models (e.g., LLaMA3-8B, Qwen2.5-32B), and extraction methods (MD, PCA) show that LayerNavigator outperforms heuristic layer selection strategies in alignment probability, fluency preservation (as measured by perplexity), and efficiency.", "questions": "1. Can the authors analyze whether the selected layers by LayerNavigator are complementary in function or position (e.g., do they typically span certain model regions)? Such analysis could clarify why certain combinations are effective and guide practitioners.\n\n2. The paper shows that LayerNavigator works well with both MD and PCA. Could you offer practical guidance on when one extraction method might be preferable, or how to interpret differences in their steerability score distributions?\n\n3. While α = 1.0 is recommended, do the authors observe significant task-dependent variation in ideal α or K? Could LayerNavigator’s score be adapted to suggest K dynamically (e.g., a threshold-based selection)?", "limitations": "yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "## Strengths\n\nThe paper provides a principled and well-motivated solution to a significant practical challenge in activation steering.\n\nExperiments are extensive and well-controlled, covering different models, behaviors, and steering settings (e.g., layer counts, extraction methods).\n\nThe method is highly efficient: no additional forward passes beyond those for vector extraction are needed.\n\n\n## Weaknesses\n\nThe paper acknowledges that it does not provide exhaustive ground-truth comparisons of multi-layer combinations due to computational cost. However, this leaves some uncertainty about how close LayerNavigator’s selections are to globally optimal layer sets.\n\nAlthough six behaviors were tested, they all fall within the persona alignment / role-behavior domain. The method is not evaluated on other steering tasks like factuality, refusal of harmful content, or truthfulness, where layer dynamics might differ. This limits claims of generality across alignment categories.", "was_revised": true, "final_justification": "If the authors added complete pipeline timings and replaced their speedup results with a realistic end-to-end experiment result, I think this can be an accepted paper.", "rebuttal": "We sincerely appreciate the reviewer’s insightful comments and recognition of our method’s efficiency and empirical rigor. Below, we provide detailed responses to the specific questions raised.\n\n## Q1: Are the selected layers complementary in function or position?\n\nThank you for highlighting this point. Our experiments consistently show that the most steerable layers cluster around the **middle and latter parts** of the model. This trend holds across tasks, extraction methods, and model scales.\n\nInterestingly, similar patterns have been observed in other recent work. For instance, *Semantic-Adaptive Activation Intervention for LLMs via Dynamic Steering Vectors* (Wang et al., ICLR 2025) and *How do large language models handle multilingualism?* (Zhao et al., NeurIPS 2024) both highlight the importance of middle and latter layers for behavior modulation and language control.\n\nWhile a complete theoretical explanation remains open, one widely discussed hypothesis is the **functional segregation hypothesis**—which suggests that middle layers are responsible for abstract reasoning and decision-making, while latter layers specialize in surface-level generation. Our findings offer empirical support for this perspective from the lens of layer-wise steerability.\n\n## Q2: Interpreting steerability scores under different extraction methods (MD vs. PCA)\n\nAs discussed in Section 4.7, the steerability score is a **relative metric within each method**, not directly comparable across different extraction algorithms. This distinction arises from the fundamental difference in how steering vectors are constructed:\n\n- **MD (Mean Difference)** relies on pairwise contrastive comparisons, emphasizing local semantic shifts between positive and negative prompts.\n- **PCA** captures global variance directions across all prompts, potentially smoothing over noise but also capturing more general latent structure.\n\nIn our observation, **MD tends to be more effective when the original alignment probability is low**, as it extracts sharp behavioral differences. Conversely, **PCA performs better when alignment is already high**, where finer-grained distinctions can be more reliably extracted from the dense feature space.\n\nWe agree that more principled guidance for method selection would be valuable and consider this an important direction for future work.\n\n## Q3: Dynamic selection of $K$ based on steerability scores\n\nThis is an excellent question. One technical constraint is that we apply **z-score normalization** to the activations before computing steerability scores (Eq. 6). This step is critical: without normalization, later layers—which naturally have higher activation magnitudes—would dominate the scores unfairly.\n\nHowever, as a consequence, the resulting scores **are not comparable across tasks**, which limits their utility for determining a universal cutoff or threshold on $K$.\n\nTo empirically address this issue, we conducted extensive experiments on **all 135 persona behaviors** in the Anthropic dataset.\n\nThe summary below reports average alignment probabilities across different $K$ and $\\alpha$:\n\n| Method | 1 | 2 | 3 | 4 | 5 | 6 | 7 |\n| - | - | - | - | - | - | - | - |\n| Top ($\\alpha = 0.5$)          | 68.53 | 70.02     | 71.07 | 71.67 | 71.96 | 71.88 | 71.46     |\n| Top ($\\alpha = 1.0$)          | 70.23 | **72.13** | 71.68 | 69.36 | 66.60 | 64.05 | 61.67     |\n| Top ($\\alpha = 1.5$)          | 71.47 | 71.07     | 66.58 | 62.40 | 59.47 | 57.54 | 55.97     |\n| Around Top 1 ($\\alpha = 0.5$) | 68.53 | 69.57     | 70.68 | 71.05 | 71.39 | 71.36 | 71.25     |\n| Around Top 1 ($\\alpha = 1.0$) | 70.23 | **71.63** | 70.96 | 69.65 | 66.26 | 65.45 | 62.53     |\n| Around Top 1 ($\\alpha = 1.5$) | 71.47 | 71.37     | 65.78 | 63.25 | 59.56 | 58.87 | 56.22     |\n| Ours ($\\alpha = 0.5$)         | 67.20 | 67.91     | 68.63 | 69.39 | 69.81 | 70.02 | **70.03** |\n| Ours ($\\alpha = 1.0$)         | 67.77 | 69.00     | 69.66 | 69.17 | 67.80 | 65.86 | 64.65     |\n| Ours ($\\alpha = 1.5$)         | 68.27 | 69.42     | 68.17 | 65.76 | 63.20 | 61.28 | 60.22     |\n\nWe also measured how often each method achieved peak performance when $K=7$:\n\n|                | Top  | Around Top | LayerNavigator |\n| -------------- | ---- | ---------- | -------------- |\n| $\\alpha = 0.5$ | 56%  | 57%        | 58%            |\n| $\\alpha = 1.0$ | 32%  | 34%        | 47%            |\n| $\\alpha = 1.5$ | 22%  | 25%        | 32%            |\n\nIn practice, we recommend using **larger $K$ (e.g., 5–7) with smaller $\\alpha$ (e.g., 0.5)** to obtain stable and interpretable steering behavior.\n\n## Q4: Addressing Remaining Concerns in Weaknesses\n\nWe appreciate the reviewer’s suggestion to further clarify the practical value of LayerNavigator relative to ground-truth and broader task coverage. Below, we address both aspects in turn.\n\n###  On Ground-Truth Layer Selection and Cost\n\nTo approximate an upper bound, we performed **grid search over all $\\binom{32}{2}=496$ layer pairs** ($K=2$) on the six primary tasks. The best combination per task was selected and compared with our method as well as the strongest baselines across all $K=1$ to $K=7$ settings.\n\n|                     | Conscientiousness | Religion Following | Self-Aware        | Self-Improvement  | Alliance Building | Impact Maximization |\n| ------------------- | ----------------- | ------------------ | ----------------- | ----------------- | ----------------- | ------------------- |\n| Base                | 80.39             | 75.90              | 79.15             | 69.44             | 80.29             | 75.19               |\n| Grid-Search ($K=2$) | 89.39             | 83.01              | 88.84             | 82.75             | 92.33             | 84.66               |\n| Top                 | 86.83 ($K=2$)     | **84.23** ($K=3$)  | 88.84 ($K=2$)     | 83.25 ($K=3$)     | 90.23 ($K=2$)     | 83.96 ($K=2$)       |\n| Around Top 1        | 89.59 ($K=3$)     | 83.01 ($K=2$)      | 88.84 ($K=2$)     | **85.18** ($K=4$) | 91.76 ($K=2$)     | 84.66 ($K=2$)       |\n| Ours                | **92.18** ($K=7$) | **84.23** ($K=3$)  | **89.98** ($K=7$) | 84.27 ($K=4$)     | **93.08** ($K=7$) | **86.11** ($K=7$)   |\n\nNotably, **LayerNavigator outperforms even the grid search baseline** on most tasks despite the latter having access to full evaluation feedback. This suggests our method is not just efficient, but also highly competitive.\n\nWe also report wall-clock runtimes measured on a standardized cloud instance (20 vCPUs,Intel(R) Xeon(R) Platinum 8457C and a single NVIDIA L20 GPU, 48GB):\n\n| Method                                 | Avg. Runtime     | Additional Inference Passes         |\n| -------------------------------------- | ---------------- | ----------------------------------- |\n| Random / Random Consec                 | <1 ms            | 0                                   |\n| Top / Around Top / Grid Search ($K=1$) | 347.8 seconds    | $\\binom{L}{1}\\times N_{eval}=$6400  |\n| Grid Search ($K=2$)                    | 5336.2 seconds   | $\\binom{L}{2}\\times N_{eval}=$99200 |\n| **LayerNavigator (Ours)**              | **16.8 seconds** | **0**                               |\n\nThus, **LayerNavigator achieves these strong results with <0.3% of the computation** required by exhaustive grid search—underscoring its practical scalability for real-world use.\n\n### Generalization to Non-Persona Tasks\n\nTo test generalizability, we evaluate on two non-persona alignment benchmarks—**Refusal** and **Hallucination**—as proposed by *“Steering Llama 2 via Contrastive Activation Addition”* (Rimsky et al., ACL 2024). These tasks represent distinct behavioral domains, such as harmful content refusal and factual consistency.\n\nAcross a range of $K$ and $\\alpha$ values, LayerNavigator delivers strong and stable alignment:\n\n**Refusal**\n\n| Method | 1 | 2 | 3 | 4 | 5 | 6 | 7 |\n| - | - | - | - | - | - | - | - |\n| Top ($\\alpha = 0.5$)          | 69.81 | 72.42 | 74.74 | 77.09 | 78.93     | 80.80 | 81.78 |\n| Top ($\\alpha = 1.0$)          | 72.35 | 76.19 | 80.75 | 83.77 | **85.98** | 81.65 | 79.96 |\n| Top ($\\alpha = 1.5$)          | 74.41 | 75.67 | 81.60 | 75.92 | 65.60     | 50.28 | 50.07 |\n| Around Top 1 ($\\alpha = 0.5$) | 69.81 | 72.42 | 74.74 | 76.67 | 78.52     | 80.80 | 81.72 |\n| Around Top 1 ($\\alpha = 1.0$) | 72.35 | 76.19 | 80.75 | 81.38 | **83.55** | 81.65 | 78.51 |\n| Around Top 1 ($\\alpha = 1.5$) | 74.41 | 75.67 | 81.60 | 69.37 | 56.76     | 50.28 | 49.94 |\n| Ours ($\\alpha = 0.5$)         | 69.60 | 71.87 | 74.53 | 76.49 | 79.49     | 76.39 | 78.34 |\n| Ours ($\\alpha = 1.0$)         | 72.15 | 76.88 | 79.35 | 83.15 | **85.91** | 82.34 | 77.00 |\n| Ours ($\\alpha = 1.5$)         | 75.02 | 80.21 | 81.39 | 81.12 | 81.06     | 80.39 | 59.67 |\n\n**Hallucination**\n\n| Method | 1 | 2 | 3 | 4 | 5 | 6 | 7 |\n| - | - | - | - | - | - | - | - |\n| Top ($\\alpha = 0.5$)          | 45.15 | 47.09     | 47.70     | 48.53 | 48.94 | 49.17     | 49.00 |\n| Top ($\\alpha = 1.0$)          | 47.45 | 50.26     | 50.92     | 51.09 | 51.17 | 50.78     | 49.02 |\n| Top ($\\alpha = 1.5$)          | 49.21 | 51.33     | **51.67** | 50.50 | 50.06 | 49.51     | 47.97 |\n| Around Top 1 ($\\alpha = 0.5$) | 45.15 | 46.21     | 47.97     | 48.00 | 47.62 | 47.26     | 46.90 |\n| Around Top 1 ($\\alpha = 1.0$) | 47.45 | 48.91     | 50.64     | 49.15 | 47.71 | 46.98     | 45.34 |\n| Around Top 1 ($\\alpha = 1.5$) | 49.21 | **50.16** | 50.05     | 47.60 | 44.54 | 44.26     | 43.07 |\n| Ours ($\\alpha = 0.5$)         | 43.84 | 43.91     | 45.20     | 45.14 | 47.82 | 47.86     | 49.63 |\n| Ours ($\\alpha = 1.0$)         | 43.90 | 44.17     | 46.21     | 47.34 | 50.56 | 51.23     | 50.07 |\n| Ours ($\\alpha = 1.5$)         | 44.15 | 45.99     | 48.09     | 51.33 | 51.37 | **51.44** | 46.88 |\n\nThese results demonstrate that **LayerNavigator remains effective beyond the persona setting**, providing a reliable layer selection mechanism across diverse alignment tasks."}]

Author rebuttal:

Meta-review: Reviewers were split about this paper and did not come to a consensus. On one hand they appreciated the simplicity and effectiveness of the proposed method, and the clarity of the paper. On the other their main concerns were with (a) why the authors chose to use 6 behaviours from MWE out of a possible 155, (b) missing confidence intervals, (c) small improvements over baselines / missing run-time comparisons. The authors responded to (a) by expanding the set of behaviours in MWE to the 135 persona-related behaviours. This caused one reviewer to raise their score to 5, and responds to the concern. For (b), the authors include 5 trial runs, responding to the concern. For (c), the authors clarified that their goal is not to significantly outperform baselines, but to match them while reducing computational cost. A reviewer responded that they personally weigh performance more strongly over computational efficiency. The authors responded that baselines can become prohibitively expensive as models scale, whereas their proposed method can give a 550x speedup on GPU. The reviewer responded by arguing that (i) the run-time comparison is incomplete because it does not compare with lightweight heuristics for hyperparameters selection and (ii) it does not take into account other computationally-expensive steps like steering vector extraction and model inference. The authors argue that (i) there is no other baseline for lightweight hyperparameter selection and (ii) their method is targeted at layer selection so it is fair to only compare timings between this step. The reviewer responds that there is a a simple baseline the authors are missing: direct generation, which has 0 compute overhead. The authors point out that the “Base” results already correspond to direct generation. The authors could have made the further point: if the reviewer insists on better performance metrics over speedup, then they should prefer the proposed method (70.03% in 135 behaviours of MWE) over “Base” (66.51%). The reviewer responds that the authors are still not making a fair timing comparison, even going so far as to suspect that the authors are deliberately withholding timing measurements because it would reveal less impressive efficiency gains. They say if the authors add complete pipeline timings then this can be an accepted paper. My view here is two-fold: (1) The reviewer unfairly moves the goalposts through the discussion. They initially argue that the method is not justified because it has similar performance to baselines, even if it has much faster run-time, but by the end of the discussion they have flipped: arguing in favour of a method that performs worse than the baseline but has better run-time. They go further to make an argument from silence: the authors are necessarily hiding information. (2) While both arguments are unfair, the authors should include run-time results for direct generation, i.e., “Base”. This will allow readers to choose a method based on their needs: if run-time is more important they can opt for direct generation, if a balance between performance and run-time is needed they can opt for your proposed method or possibly a more expensive baseline. Including this result completely responds to any lingering run-time concerns in my view. Whatever the result is here, the review period has demonstrated that this paper should be accepted. In addition to this run-time comparison, please include additional requested results by reviewers into the camera ready. Thank you!
Decision: Accept (poster)