Decoding the Neural Network ‘Code’: Targeted Edits for Predictable AI

Author: Denis Avetisyan

Researchers have developed a new framework for understanding and controlling the inner workings of neural networks, allowing for precise, quantifiable manipulation of their behavior.

The study demonstrates that sparse autoencoders, when applied with ResNet-18 and ViT-B/16 backbones, achieve diminishing returns in reconstruction fidelity as training progresses-evidenced by the convergence of reconstruction and $L_1$ sparsity losses-and ultimately approximate, but do not perfectly replicate, original feature activations, as indicated by the deviation from ideal reconstruction ($Â = A$).

SALVE utilizes sparse autoencoders to identify latent features and permanently modulate network weights for mechanistic control and improved interpretability.

Despite the impressive performance of deep neural networks, understanding and controlling their internal mechanisms remains a significant challenge. This work introduces SALVE (Sparse Autoencoder-Latent Vector Editing), a novel framework that unifies mechanistic interpretability with direct model editing through the discovery of sparse, model-native features. By leveraging an $\ell_1$-regularized autoencoder and a feature-level saliency mapping technique, SALVE enables precise, permanent weight-space interventions for continuous modulation of network behavior and quantifiable robustness diagnostics. Could this approach pave the way for more transparent and controllable AI systems, moving beyond black-box predictions towards genuinely understandable intelligence?

The Entanglement Problem: Decoding Feature Control in Neural Networks

Despite their remarkable capabilities, deep neural networks frequently develop entangled features – internal representations where a single neuron or pathway responds to multiple, often overlapping, concepts. This means that attempting to influence a specific aspect of the network’s decision-making process often inadvertently affects others, hindering targeted interventions. Consequently, understanding what a network has learned becomes exceptionally difficult, as dissecting the contribution of individual features is hampered by this interconnectedness. The result is a complex system where isolating the basis for a particular output requires overcoming significant challenges, limiting both interpretability and the ability to predictably control model behavior.

The inherent complexity of deep neural networks often presents a significant challenge in discerning the function of individual learned features; traditional techniques frequently fail to isolate specific representations without inadvertently influencing others. This entanglement arises from the distributed nature of information storage within these models, where a single concept may be encoded across numerous neurons and layers. Consequently, attempts to manipulate a desired characteristic can produce unintended consequences, creating a ‘black box’ effect where the relationship between input and output remains opaque. This lack of granular control hinders reliable model behavior, particularly in scenarios demanding predictable and verifiable outcomes, as it becomes difficult to confidently steer the network towards a specific goal without triggering unforeseen actions or compromising overall performance.

The opacity of deep neural networks presents significant challenges in safety-critical applications, where the rationale behind a decision is as important as the decision itself. In domains such as autonomous driving, medical diagnosis, and aviation, a system’s inability to clearly articulate why it made a particular choice can have severe consequences. For instance, an autonomous vehicle must not only react to a pedestrian but also be able to demonstrate that its response was based on a correct interpretation of the situation – identifying the pedestrian as such, and not a static object. Similarly, a diagnostic tool flagging a potential health issue requires justification to allow medical professionals to validate the assessment. This need for transparency isn’t merely about trust; it’s about accountability, enabling effective error analysis, and ensuring that systems operate within defined safety parameters. Consequently, the pursuit of interpretable feature control is not simply an academic exercise, but a crucial step towards deploying reliable and trustworthy artificial intelligence in high-stakes environments.

Suppressing the dominant feature for the 'Church' class effectively eliminates its detectability in the ViT-B/16 model, as shown by the confusion matrices, while maintaining accuracy across other classes. — Suppressing the dominant feature for the ‘Church’ class effectively eliminates its detectability in the ViT-B/16 model, as shown by the confusion matrices, while maintaining accuracy across other classes.

Sparse Autoencoders: Distilling Latent Features for Interpretation

A Sparse Autoencoder is utilized to generate a compressed, latent representation of activations from a neural network model. This is achieved by training the autoencoder to reconstruct the input activation vector from a lower-dimensional code. The resulting latent space is characterized by sparsity, meaning that most of the values in the code are zero or near zero for any given input. This forces the autoencoder to learn a representation where each latent dimension corresponds to a distinct and potentially interpretable feature present in the original activations, effectively reducing dimensionality while preserving essential information. The degree of sparsity is controlled via regularization techniques applied during training, such as L1 regularization on the latent code activations.

Sparsity, when implemented as a regularization technique during autoencoder training, promotes the development of latent features that are statistically independent. This is achieved by adding a penalty term to the loss function, typically based on the $L_1$ norm of the latent activations, which forces many activations towards zero. By minimizing the number of active neurons in the latent space, the autoencoder is compelled to distribute the reconstruction responsibility across a larger set of features, preventing any single feature from dominating the representation. This results in a more robust and interpretable latent space where each active feature corresponds to a distinct aspect of the input data, enhancing the model’s ability to generalize and facilitating feature disentanglement.

The utilization of established backbone architectures, such as ResNet18 and ViT, facilitates the extraction of latent features through Sparse Autoencoders. These features, represented as activations within the autoencoder’s bottleneck layer, are not merely compressed data but potentially interpretable representations of the input. Subsequent visualization techniques, including activation mapping and feature space exploration, allow for qualitative assessment of these learned features. Furthermore, direct manipulation of these latent features – through methods like feature editing or interpolation – enables controlled modification of the input data, demonstrating the disentangled nature of the learned representation and enabling applications in areas like image generation and data analysis.

Analysis of a ResNet-18 model reveals that discovered latent features exhibit class-specific activation patterns and can be visualized as meaningful image components, as demonstrated by the emergence of golf ball characteristics from random noise during activation maximization.

Feature Manipulation: Direct Control Over Model Behavior

Following the identification of latent features within a neural network, techniques such as Weight Editing and Activation Steering allow for direct manipulation of model behavior. Weight Editing modifies the weights associated with specific features, effectively altering their contribution to the overall output. Activation Steering, conversely, focuses on modulating the activation values of neurons representing these features during inference. Both methods enable selective suppression or enhancement of learned representations, providing a mechanism to influence the model’s predictions without retraining or altering the underlying model architecture. This targeted intervention offers a degree of control over the model’s reasoning process and output generation.

Selective suppression or enhancement of latent features enables direct control over model behavior during inference. Techniques like Weight Editing modify the model’s parameters to diminish or amplify the influence of particular features on the output. Activation Steering operates by altering the activation values of neurons associated with these features. Both approaches allow for targeted adjustments without retraining the model, effectively ‘steering’ its predictions by manipulating the internal representations it utilizes. The magnitude of suppression or enhancement is typically controlled by a scaling factor, allowing for granular control over the degree of behavioral modification.

Grad-CAM (Gradient-weighted Class Activation Mapping) adaptations, specifically GradFAM (Gradient-based Feature Activation Mapping), provide a visualization technique to confirm the effect of feature manipulation. GradFAM identifies the regions of an input image most relevant to the activation of a targeted latent feature, allowing for a direct assessment of whether weight editing or activation steering successfully influenced that feature’s contribution to the model’s output. By generating a heatmap highlighting these relevant regions, GradFAM enables quantitative and qualitative verification that the intended feature is being suppressed or enhanced as expected, confirming the effectiveness of the behavioral control technique. This visual inspection is crucial for debugging and understanding the impact of feature-level interventions.

Analysis of the ViT-B/16 model reveals class-specific latent feature activations and demonstrates that dominant features are grounded in representative images for classes like 'Church' and 'English springer'. — Analysis of the ViT-B/16 model reveals class-specific latent feature activations and demonstrates that dominant features are grounded in representative images for classes like ‘Church’ and ‘English springer’.

Quantifying Feature Importance: Measuring Model Reliance

Feature manipulation offers a powerful means of assessing the significance of individual latent features within a neural network. By systematically altering or suppressing these features, researchers can directly measure the resulting changes in model accuracy, effectively quantifying each feature’s contribution to overall performance. A technique known as Class Suppression exemplifies this approach, wherein specific features are selectively disabled to observe the impact on classifying particular classes. The magnitude of the accuracy drop directly correlates with the feature’s importance for that class, revealing which features drive decisions and highlighting potential areas of model bias or vulnerability. This methodology moves beyond simple feature visualization, providing a robust, data-driven metric for understanding the internal workings of complex neural networks and pinpointing critical elements responsible for accurate prediction.

The concept of a Critical Suppression Threshold, denoted as $\alpha_{crit}$, offers a quantifiable measure of how much a predictive model relies on specific latent features for classifying particular classes. This threshold represents the minimum level of feature suppression required to significantly degrade the model’s performance for a given class, effectively pinpointing which features are most crucial for accurate prediction. A low $\alpha_{crit}$ value indicates a strong reliance – and therefore potential vulnerability – as even slight manipulation of that feature drastically reduces classification accuracy. Conversely, a high $\alpha_{crit}$ suggests a more robust classification strategy, less susceptible to feature-level attacks or inherent biases. By analyzing these thresholds across different classes, researchers gain valuable insight into the model’s decision-making process and can identify areas where the model may be unfairly prioritizing certain features or exhibiting discriminatory behavior.

Recent investigations reveal an unprecedented degree of control over machine learning model outputs through targeted feature suppression. Experiments consistently demonstrate the ability to reduce classification accuracy for specific classes to near zero by manipulating learned latent features – effectively “switching off” the model’s ability to recognize those categories. Notably, these results were significantly enhanced when the foundational backbone network was trained using smaller batch sizes, ranging from 8 to 16. This training regime fostered the development of more disentangled features, meaning each feature encoded more independent information, and thereby improved the precision with which specific features could be suppressed to impact targeted classes, representing a substantial advancement in understanding and controlling model reliance on individual feature representations.

Suppression of features in the ViT model reduces per-class accuracy for images of churches (Class 4) and reallocates prediction confidence, indicating sensitivity to these features.

Towards Robust and Interpretable AI: The Path Forward

Recent advancements demonstrate that disentangled feature representation – the ability of an AI model to separate distinct underlying factors of variation in data – is crucial for achieving robust and controllable artificial intelligence. This approach doesn’t simply allow a model to recognize patterns, but rather to understand how those patterns are constructed from independent characteristics. By learning these separate features, the system’s internal “latent space” – a compressed representation of the data – becomes amenable to targeted manipulation. Researchers are finding that subtly altering specific dimensions within this latent space can directly influence corresponding features in the output, enabling precise control over the model’s behavior and offering unprecedented insights into its decision-making process. This capability moves beyond mere prediction, fostering AI systems capable of adaptation, generalization, and, ultimately, greater trustworthiness.

Continued investigation centers on extending this methodology to encompass increasingly sophisticated artificial intelligence models and larger, more intricate datasets. Such expansion isn’t merely about scaling existing approaches; it demands novel strategies for managing the heightened complexity inherent in these systems. Researchers anticipate that successfully applying these techniques to more challenging scenarios will reveal previously obscured relationships between input features and model behavior, thereby dramatically enhancing both interpretability and robustness. This pursuit aims to move beyond simply achieving accurate predictions and instead foster a deeper understanding of how those predictions are made, ultimately paving the way for more reliable and trustworthy AI applications across diverse domains.

The pursuit of truly intelligent artificial systems necessitates a shift beyond mere predictive power towards genuine understanding and control. Recent advancements demonstrate that combining automated feature discovery with the ability to precisely manipulate the learned representations – often residing in a ‘latent space’ – offers a promising pathway. This synergy allows researchers to not only identify the core characteristics a model utilizes for decision-making, but also to selectively adjust these characteristics, effectively steering the AI’s behavior. Such targeted manipulation is critical for ensuring robustness against adversarial attacks and for debugging unexpected outcomes. Ultimately, this approach fosters transparency, enabling a deeper understanding of how an AI arrives at a particular conclusion, and thereby building systems that are not simply powerful, but also demonstrably trustworthy and aligned with human values.

Learned latent bases reveal that suppressing the dominant feature of class 4 redistributes model confidence among other classes, as demonstrated by prediction accuracy and distribution across ten realizations with standard deviation indicated by shaded regions.

The pursuit of understanding neural networks, as exemplified by SALVE, demands a rigor akin to mathematical proof. One might recall Paul Erdős stating, “A mathematician who doesn’t prove things is like a painter who doesn’t know how to draw.” This sentiment perfectly encapsulates the approach taken by the framework; it doesn’t merely observe behavior, but actively seeks to prove control through permanent weight edits. By leveraging sparse autoencoders to distill latent features, SALVE moves beyond superficial manipulation, offering a quantifiable and verifiable method for controlling model behavior – a true testament to the power of mechanistic interpretability and a rejection of solutions that ‘feel like magic’ without revealing the underlying invariant.

What’s Next?

The pursuit of mechanistic interpretability often feels like reverse engineering a Rube Goldberg machine – elegant in its convolutedness, yet ultimately unsatisfying. SALVE offers a tantalizing glimpse of direct control, but the framework’s reliance on sparse autoencoders introduces its own set of constraints. The latent space, while providing a convenient handle for editing, remains an abstraction – a mathematical convenience, not necessarily a faithful representation of the underlying computation. Future work must grapple with the question of whether these edits truly understand the model, or merely exploit correlations within its weight space.

A critical limitation lies in the identification of the ‘critical suppression threshold’. While empirically effective, this threshold remains a heuristic – a pragmatic compromise in the absence of a more fundamental principle. The field requires a theoretical justification for this value, or a method for its automatic derivation, lest the framework remain tethered to ad-hoc parameter tuning. One suspects that true mechanistic control will not come from finding the right features, but from defining them axiomatically.

Ultimately, the success of SALVE, and similar approaches, will hinge on a shift in perspective. The goal should not be to make neural networks more transparent, but to build models that are provably interpretable from first principles. Until then, feature editing will remain a powerful tool, but a fundamentally approximate one.

Original article: https://arxiv.org/pdf/2512.15938.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/