Seeing, Understanding, and Acting: Guiding AI with Internal Signals

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Author: Denis Avetisyan

New research demonstrates a method for directly influencing the behavior of vision-language-action models by observing and manipulating their internal representations.

A framework combines feature-observability and feature-controllability to access and modify internal Variational Latent Alignment (VLA) representations, enabling real-time robot policy steering through a linear observer that extracts features from the Transformer’s internal states and a minimal linear controller which aligns these representations with desired constraints-all without requiring policy fine-tuning.
A framework combines feature-observability and feature-controllability to access and modify internal Variational Latent Alignment (VLA) representations, enabling real-time robot policy steering through a linear observer that extracts features from the Transformer’s internal states and a minimal linear controller which aligns these representations with desired constraints-all without requiring policy fine-tuning.

This work introduces a framework for real-time control of AI systems by steering activations within transformer networks, enabling adaptable robotics without the need for retraining.

While mechanistic interpretability has unlocked insights into large language models, applying these techniques to embodied agents remains challenging due to the complexity of multi-modal inputs and hybrid architectures. This work, ‘Observing and Controlling Features in Vision-Language-Action Models’, addresses this gap by introducing a framework for understanding and manipulating internal representations within these vision-language-action (VLA) models. Specifically, we demonstrate that linearly encoded features can be observed via classification and accurately steered using minimal, control-grounded interventions, enabling real-time adaptation of robotic behavior without fine-tuning. Could this approach unlock truly interpretable and adaptable embodied intelligence, aligning robotic actions with nuanced user preferences and task demands?

Bridging Perception and Action: The Algorithmic Imperative

Conventional robotic systems often falter when confronted with tasks demanding more than simple, pre-programmed actions. These limitations stem from a fundamental challenge: bridging the gap between sensing the environment and executing appropriate responses. While adept at repetitive motions in controlled settings, robots struggle with the ambiguity and complexity of real-world scenarios. Nuance, such as recognizing subtle object variations, understanding implied intentions, or adapting to unforeseen circumstances, requires a level of perceptual reasoning that surpasses the capabilities of traditional algorithms. This difficulty isn’t merely a matter of improving sensor accuracy; it’s a core problem of how robots interpret sensory data and translate it into meaningful action, hindering their ability to operate effectively in unstructured and dynamic environments.

Recent advancements in robotics are increasingly focused on Vision-Language-Action (VLA) models as a means to overcome limitations in handling complex, real-world scenarios. These models represent a departure from traditional robotic systems by unifying diverse data streams – visual input from cameras, natural language instructions, and the necessary action commands – into a cohesive framework. This integration allows robots to not simply react to their environment, but to understand instructions expressed in human language and translate them into appropriate physical actions. By processing these multi-modal inputs simultaneously, VLAs can resolve ambiguities, generalize to novel situations, and ultimately perform tasks requiring a degree of reasoning previously unattainable in robotics, paving the way for more adaptable and intuitive human-robot collaboration.

The effectiveness of Vision-Language-Action Models (VLAs) is fundamentally rooted in the Transformer architecture, a neural network design initially revolutionizing natural language processing. This architecture’s core mechanism, self-attention, allows the model to weigh the importance of different input elements – be it pixels in an image, words in a command, or potential actions – when making decisions. Unlike prior sequential processing methods, Transformers process all inputs in parallel, capturing complex relationships and dependencies crucial for understanding multi-modal data. This capability allows VLAs to not simply recognize objects and language, but to reason about their interplay and translate that understanding into effective action, representing a significant leap towards more adaptable and intelligent robotic systems. The Transformer’s scalability and capacity for pre-training on vast datasets further enhance the VLA’s performance, enabling it to generalize to novel situations and tasks with remarkable efficiency.

Two distinct VLA architectures are presented: a standard autoregressive Transformer and a hybrid Transformer-Flow Matching network where Flow Matching layers, functioning as an 'action expert,' attend to corresponding layers within the Transformer.
Two distinct VLA architectures are presented: a standard autoregressive Transformer and a hybrid Transformer-Flow Matching network where Flow Matching layers, functioning as an ‘action expert,’ attend to corresponding layers within the Transformer.

Feature Observability: Unveiling the Internal Logic

Effective control of Very Large Agent (VLA) behavior necessitates insight into the internal representations the model utilizes for decision-making. This concept, termed Feature-Observability, posits that understanding what features a VLA is attending to, and how those features correlate with specific actions, is a prerequisite for reliable steering. Without observability into these internal states, attempts to modify behavior are essentially black-box interventions, lacking the precision needed to achieve desired outcomes. Feature-Observability is not simply about accessing raw activations; it requires identifying and interpreting the features that are demonstrably relevant to the VLA’s exhibited behavior, allowing for targeted and predictable manipulation.

Sparse Autoencoders (SAEs) are utilized to discover and isolate behaviorally relevant features within a Vector Large Language Model (VLLM). These autoencoders are trained to reconstruct input data-such as text prompts-using a limited number of active neurons in a bottleneck layer. This sparsity constraint forces the SAE to learn a compressed, efficient representation of the input, effectively identifying the most salient features driving the model’s behavior. The activations of these sparse features can then be directly observed and analyzed, providing insight into the internal workings of the VLLM and enabling targeted interventions to understand and control its responses. The learned sparse representation serves as a lower-dimensional embedding of the input, allowing for efficient access and manipulation of the model’s internal state.

Feature-Controllability enables targeted modification of a model’s output by directly altering the activations within its learned internal representations. This is achieved by identifying specific feature activations that correspond to particular behavioral characteristics. Once identified, these activations can be systematically adjusted – either increased or decreased – to predictably influence the model’s subsequent behavior. This process bypasses the typical input space and operates directly on the model’s latent space, offering a more precise and interpretable form of control than traditional input-based methods. Successful implementation requires a robust method for mapping feature activations to observable behaviors and a mechanism for selectively manipulating those activations without disrupting overall model function.

By constraining the representation to within bounds [latex]\zeta_{min},\zeta_{max}[/latex], our minimal controller ensures stable classification, unlike other interventions or unconstrained representations.
By constraining the representation to within bounds [latex]\zeta_{min},\zeta_{max}[/latex], our minimal controller ensures stable classification, unlike other interventions or unconstrained representations.

Architectural Validation: OpenVLA and π0.5 in Practice

OpenVLA is an architecture built upon the Transformer model, specifically designed for processing multiple data modalities simultaneously. Its efficacy is demonstrated through validation on the BridgeData V2 dataset, a benchmark resource for evaluating multi-modal learning systems. The Transformer architecture allows OpenVLA to effectively capture relationships between different input types, enabling robust performance in tasks requiring integration of diverse sensory information. Performance metrics and detailed results from the BridgeData V2 validation are available in the associated research publications, outlining the model’s capabilities in handling complex multi-modal scenarios.

π0.5 is an architecture that integrates transformer networks with the Flow Matching generative modeling technique. Flow Matching allows π0.5 to generate trajectories by learning a continuous normalizing flow, effectively transforming a simple distribution into a complex, desired data distribution. This approach aims to improve the quality and diversity of generated outputs compared to standard transformer-based generative models. Evaluation of π0.5 was conducted using the Libero dataset, a benchmark for robotic manipulation tasks, to assess its performance in generating feasible and successful robot motions.

Linear Observers and Linear Controllers are integral to the performance of both the OpenVLA and π0.5 architectures when applied to robotic manipulation. These control mechanisms facilitate the manipulation of internal representations within the models, enabling near-perfect constraint satisfaction during task execution. Specifically, the implementation of these linear control systems results in a closed-loop task success rate exceeding 90%, indicating a high degree of reliability and precision in completing robotic manipulation objectives. This level of performance is achieved by precisely regulating the internal states of the model to adhere to task constraints and desired outcomes.

Training a linear classifier on transformer layers using [latex]\pi_{0.5}[/latex] consistently outperforms baseline predictions-assessed by maximum absolute error (MAE) and accuracy-across both the Libero and BridgeData V2 datasets when utilizing the best performing layer.
Training a linear classifier on transformer layers using [latex]\pi_{0.5}[/latex] consistently outperforms baseline predictions-assessed by maximum absolute error (MAE) and accuracy-across both the Libero and BridgeData V2 datasets when utilizing the best performing layer.

Towards True Autonomy: Implications and Future Trajectories

Recent advances in robotics leverage the power of manipulating a robot’s internal representation of its environment to achieve remarkably adaptable behavior. Instead of relying on pre-programmed responses, these methods allow for closed-loop control, where the robot continuously refines its understanding and actions based on sensory feedback. This fine-grained control over internal representations – essentially, how the robot ‘thinks’ about its surroundings – enables it to adjust to unexpected obstacles, recover from disturbances, and even learn new skills on the fly. By directly modulating these internal states, researchers are moving beyond rigid automation towards systems capable of genuine, nuanced adaptation – a crucial step towards robots that can reliably operate in the unpredictable complexities of the real world.

A significant advancement in robotics lies in the capacity for Variable Latent Action (VLA) policies to enable generalization to novel scenarios and dynamic environments. Rather than being rigidly programmed for specific tasks, these systems learn internal representations that allow a robot to adapt its behavior without explicit retraining. This is achieved by subtly steering the latent space – the core of the robot’s decision-making process – enabling it to effectively navigate unforeseen obstacles, respond to unexpected changes, and maintain performance in previously unencountered situations. The capacity to manipulate these internal states represents a shift from reactive programming to proactive adaptation, suggesting a future where robots can operate with greater autonomy and resilience in real-world settings, mirroring the adaptability observed in biological systems.

Continued development necessitates extending these adaptable control methods beyond current limitations, specifically targeting more intricate robotic challenges. The true potential of internally steered robotic behavior will be realized through synergistic integration with established planning algorithms; this combination promises systems capable of not only reacting to immediate changes but also proactively strategizing and executing complex sequences of actions. Future research will likely explore hierarchical architectures, where high-level planners define goals and VLAs provide the nuanced, low-level control needed for robust execution in dynamic and unpredictable environments, ultimately paving the way for truly autonomous and versatile robotic systems.

Intervening in the representation space of the policy at different layers with varying strengths α modulates yaw and gripper actions, with the effect diminishing at greater depths due to increasing representation magnitude as indicated by the growing [latex]L_2[/latex]-norm with layer depth.
Intervening in the representation space of the policy at different layers with varying strengths α modulates yaw and gripper actions, with the effect diminishing at greater depths due to increasing representation magnitude as indicated by the growing [latex]L_2[/latex]-norm with layer depth.

The pursuit of feature observability, as detailed in the paper, mirrors a fundamental tenet of robust system design. One could almost anticipate this need with Claude Shannon’s observation: “The most important thing in communication is to convey information.” The paper elegantly demonstrates how dissecting internal representations-essentially, the ‘information’ within a Vision-Language-Action model-allows for precise behavioral control. This isn’t merely about achieving desired outcomes; it’s about understanding how those outcomes are reached. If the model’s decision-making process feels opaque, one hasn’t yet revealed the invariant – the underlying, provable logic governing its actions. The framework provides a means to expose that logic, transforming a ‘black box’ into a transparent system where alignment can be verified and maintained without the crutch of constant retraining.

What Lies Ahead?

The demonstrated capacity to observe and manipulate internal representations within Vision-Language-Action models, while practically useful, merely postpones the inevitable confrontation with foundational ambiguities. The current framework achieves control without retraining – a commendable engineering feat – but sidesteps the more pressing question of what is actually being controlled. These models remain, at their core, empirical mappings; a successful manipulation does not constitute understanding. A provable algorithm, one rooted in a formal specification of desired behavior, remains the elusive goal.

Future work must move beyond feature attribution and embrace a commitment to mechanistic interpretability, rigorously defining the semantics of these internal representations. Activation steering, while effective, offers little insight into the computational principles governing these networks. The field requires a formal language for describing robot intentions, allowing for verification of model behavior against logical constraints. Without such a language, control will forever be tethered to brute-force experimentation.

The current focus on ‘real-time alignment’ risks conflating expediency with elegance. True progress demands a departure from treating these models as black boxes, and a resolute commitment to building systems whose behavior can be predicted – and proven – from first principles. Only then will the promise of genuinely intelligent robotics be realized, and the distinction between ‘working’ and ‘correct’ become meaningful.

Original article: https://arxiv.org/pdf/2603.05487.pdf

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

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2026-03-07 15:55