Building Artificial Minds: A New Loop-Based Architecture

Author: Denis Avetisyan

Researchers have unveiled a small, inspectable AI system designed to explore the core mechanisms underlying persistent thought, stable preferences, and goal-directed behavior.

Disrupting feedback and integration pathways diminishes sustained neural activity-measured as <span class="katex-eq" data-katex-display="false"> NsN^{s}AUC </span>-in ipsundrum variants, suggesting a critical role for these mechanisms in maintaining post-stimulus neural persistence. — Disrupting feedback and integration pathways diminishes sustained neural activity-measured as $NsN^{s}AUC$ -in ipsundrum variants, suggesting a critical role for these mechanisms in maintaining post-stimulus neural persistence.

This work details ReCoN-Ipsundrum, an agent with a recurrent sensorimotor loop and affect coupling, advocating for mechanism-linked assays to assess artificial consciousness.

Establishing robust indicators of machine consciousness remains a central challenge, often relying solely on behavioral outputs. To address this, we present ‘ReCoN-Ipsundrum: An Inspectable Recurrent Persistence Loop Agent with Affect-Coupled Control and Mechanism-Linked Consciousness Indicator Assays’, a novel, inspectable architecture featuring a recurrent sensorimotor loop and optional affect coupling, demonstrating dissociable mechanisms for post-stimulus persistence, stable preference, and structured exploration. Our results reveal that affect-coupled control correlates with nuanced behaviors like lingering caution and systematic scanning, suggesting a link between internal affective states and exploratory drive. Does this engineered system offer a pathway toward more rigorous, mechanism-linked assays for evaluating the emergence of subjective experience in artificial agents?

The Embodied Foundation of Intelligence

Conventional artificial intelligence approaches frequently treat cognition as a purely computational process, detached from the physical world and the body’s interactions with it. However, a growing body of research demonstrates that intelligence isn’t simply about processing information, but is deeply rooted in embodied experience. This means that an agent’s physical form, its capacity for action, and the continuous loop of sensing and acting-its sensorimotor contingency-are not peripheral to intelligence, but fundamental. The very structure of cognition is shaped by the opportunities and constraints presented by the environment and the agent’s ability to navigate it. Ignoring this embodied foundation risks creating AI systems that, while capable of complex calculations, lack the grounded understanding and adaptive flexibility characteristic of natural intelligence; systems that might excel at narrow tasks but struggle with the ambiguities and complexities of real-world situations.

The very foundation of experience and action lies within the sensorimotor loop, a continuous cycle of sensing the environment and acting upon it. This isn’t merely a computational process; it’s the bedrock upon which all cognition is built. Historically, this idea finds support in Reflex Theory, which posits that even complex behaviors originate from simple stimulus-response mechanisms, and more recently, in Haptic Realism, emphasizing the crucial role of touch and physical interaction in shaping perception. Through this loop, agents don’t just passively receive information; they actively construct an internal model of the world, refining it with each interaction. This iterative process of sensing, acting, and learning allows for adaptation and the development of increasingly complex behaviors, demonstrating that intelligence isn’t about disembodied thought, but about skillful engagement with a physical reality.

The capacity for an agent to construct internal models of its environment through ongoing interaction represents a pivotal advancement in the pursuit of truly adaptive systems. These models, far from being static representations, are continuously refined by sensory input and motor actions, enabling the agent to predict outcomes, anticipate challenges, and modulate behavior accordingly. This process, akin to building an internal simulation of the world, allows for generalization beyond pre-programmed responses – a crucial step towards robust intelligence. Systems that prioritize this interactive model-building demonstrate a capacity for learning and adaptation that surpasses traditional, rule-based approaches, allowing them to navigate complex and unpredictable environments with increasing proficiency. Ultimately, the fidelity and flexibility of these internal models dictate an agent’s ability to not simply react to stimuli, but to proactively shape its experience and achieve its goals.

Exploratory behavior, demonstrated by increased scanning and a pronounced limit-cycle structure, indicates that the agent prioritizes investigation over ε-greedy randomness.

Prioritizing Novelty and Intensifying Sensation

Effective agents require a strategy to prioritize relevant stimuli while filtering extraneous input, a process facilitated by the mechanism of Qualiaphilia. This process isn’t simply random exploration; it represents a dynamic balance between exploiting known rewarding stimuli and exploring potentially novel ones. Qualiaphilia operates by modulating the agent’s sensitivity to new information, allowing it to dedicate resources to investigating potentially valuable changes in the environment without being overwhelmed by irrelevant noise. This balancing act is crucial for efficient learning and adaptation, as excessive exploitation can lead to stagnation while unrestrained exploration consumes resources without yielding beneficial outcomes.

The Ipsundrum concept details a process whereby repeated activation of sensorimotor and interoceptive feedback loops leads to an amplification of sensory experience, resulting in the development of more detailed internal representations. This ‘thickening’ of sensation isn’t simply a matter of increased signal strength; it involves the recursive refinement of predictive models within these loops. Each iteration of the loop incorporates prior experience, modulating subsequent sensory processing and ultimately creating a richer, more nuanced perception. Quantifiable measurements demonstrate this effect, with post-stimulus Ns AUC values of approximately 19.12 observed in the Ipsundrum variant, indicating a measurable increase in the complexity of internal representation relative to baseline conditions.

The recurrent interaction between sensation, motor action, and internal states, defined as the Sensorimotor Loop, is significantly enhanced by the Ipsundrum process. This enhancement isn’t simply additive; the continual re-evaluation and refinement of sensorimotor-interoceptive loops within Ipsundrum effectively builds upon and informs the existing Sensorimotor Loop. This dynamic extension results in a more robust internal representation of stimuli, allowing for increasingly nuanced experiential differentiation. Measurable outcomes include post-stimulus Ns AUC values of approximately 19.12 for the Ipsundrum variant and 27.62 for the Ipsundrum+affect variant, demonstrating the quantifiable impact of this process on signal amplification within the broader loop.

Observable signals, termed ‘Markers’, are generated from the theoretical framework underpinning novelty detection and sensation thickening. These Markers are derived from specific ‘Indicator’ features and provide quantifiable data related to the processes. Analysis of post-stimulus neural signals reveals an approximate area under the curve (AUC) of 19.12 for the ‘Ipsundrum’ variant, indicating the strength of the response. The ‘Ipsundrum+affect’ variant demonstrates a significantly increased AUC of approximately 27.62, suggesting that the incorporation of affective processing enhances the measurable neural response associated with these sensorimotor-interoceptive loops.

Variants of the ipsundrum display post-stimulus reactivity to aversive events, but only the 'affect' variant exhibits sustained cautious behavior, as measured by prolonged turn-rate tail duration <span class="katex-eq" data-katex-display="false">NsN^{s}AUC</span>. — Variants of the ipsundrum display post-stimulus reactivity to aversive events, but only the ‘affect’ variant exhibits sustained cautious behavior, as measured by prolonged turn-rate tail duration $NsN^{s}AUC$ .

Assaying Competence and Measuring Sensory Persistence

The Pain-Tail Assay and Exploratory Play are utilized as behavioral assays to quantify Post-Stimulus Sensory Persistence, which measures the duration of an agent’s response to a previously presented stimulus. The Pain-Tail Assay specifically assesses prolonged cautious behavior – measured as a turn-rate tail duration of approximately 90 time units – following a potentially aversive stimulus. Concurrently, Exploratory Play evaluates structured investigation of the environment, providing a metric for how an agent actively seeks and processes information beyond immediate stimuli. Both assays generate quantifiable data points indicative of internal state and are employed to validate agent competence and safety when operating in complex, dynamic environments.

Validation of agent competence and safety in dynamic environments necessitates rigorous testing beyond simple task completion. Assays such as the Pain-Tail Assay, Exploratory Play, and Goal-Directed Navigation are crucial because they assess not only whether an agent achieves a goal, but how it navigates and reacts to uncertainty. These tests evaluate an agent’s ability to process sensory information, adapt to novel situations, and maintain stable behavior in potentially hazardous scenarios. Successful performance across these assays indicates a robust internal model of the environment and predictable responses, reducing the risk of unintended consequences during deployment. The combined data from these tests provides a more complete picture of agent reliability than isolated performance metrics.

The assays employed to evaluate agent competence generate quantifiable marker signals that provide insight into internal processing. Specifically, the Ipsundrum+affect variant consistently produces a scan event rate of 31.4, indicating the frequency with which the agent actively processes sensory information. These markers are not limited to simple binary states; the scan event rate represents a continuous variable allowing for detailed analysis of attentional focus and processing load. The consistent generation of these measurable signals enables objective assessment of the agent’s internal state during and after stimulus presentation, facilitating validation of its cognitive functions.

Analysis of agent behavior within the Pain-Tail Assay and Exploratory Play paradigms demonstrates the crucial role of the Ipsundrum in experience construction and maintenance; observed data indicates a sustained period of cautious behavior following stimulus presentation. Specifically, the recorded turn-rate tail duration of approximately 90 time units suggests prolonged, deliberate planning during post-stimulus processing. This metric, derived from agent navigation, provides quantifiable evidence of an internally generated expectation of potential continued adverse conditions and a corresponding adjustment in behavioral strategy. The Ipsundrum, therefore, appears to be integral to the agent’s capacity for anticipating and responding to environmental challenges beyond the immediate stimulus.

Corridor preference is modulated by familiarity, with non-affective variants exhibiting increased scenic seeking under novelty competition, while affective responses remain consistent regardless of novelty.

A Neurosymbolic Architecture for Embodied Agents

The ReCoN architecture implements sensorimotor scripts through a computational framework centered on the principle of efference copy. Efference copy, a corollary discharge signal representing the agent’s motor commands, is utilized for both predictive modeling of sensory consequences and closed-loop control. This allows the agent to anticipate the results of its actions and adjust accordingly, enabling robust performance in dynamic environments. Specifically, the predicted sensory states, derived from the efference copy, are compared with actual sensory input, generating error signals that drive corrective actions and refine the internal model. This predictive capability extends beyond simple reaction, facilitating proactive behavior and adaptation to unforeseen circumstances within the sensorimotor loop.

The ReCoN architecture builds upon existing frameworks for embodied AI by integrating and extending both the Sensorimotor Loop and the Ipsundrum. The Sensorimotor Loop provides a foundational structure for perception, action, and feedback, while the Ipsundrum-a hierarchical generative model-offers capabilities for internal simulation and prediction. By combining these, ReCoN enables agents to not only react to their environment but also to anticipate consequences of actions and plan accordingly. This integrated approach facilitates more complex behaviors and allows for the implementation of sophisticated control strategies within embodied agents, providing a robust platform for research in areas such as robotic manipulation and navigation.

Within the ReCoN architecture, Causal Lesion techniques are employed to systematically investigate the functional contribution of individual mechanisms housed within the Ipsundrum. This involves temporarily disrupting, or “lesioning,” specific components of the Ipsundrum and observing the resultant impact on the agent’s behavior. By comparing performance with and without each component active, researchers can directly assess its causal role in sensorimotor control and decision-making processes. This approach facilitates a granular understanding of how different internal mechanisms contribute to overall agent functionality, allowing for targeted refinement and optimization of the ReCoN architecture.

The agent’s operational parameters are modulated by an Internal Budget, a mechanism rooted in Constructionist Affect theory which prioritizes resource allocation based on perceived novelty and predictive error. In experimental evaluations utilizing non-affect variants of the agent, the scenic entry rate – the frequency with which the agent initiates interactions with visually distinct environments – was initially measured at 0.07. This rate demonstrated a positive correlation with environmental novelty; as the agent encountered less familiar visual scenes, the scenic entry rate increased, indicating the Internal Budget dynamically adjusts behavior based on the informational value of the external environment.

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The pursuit of artificial consciousness, as detailed in the ReCoN-Ipsundrum architecture, highlights a fundamental truth about all complex systems: they are not static entities but processes unfolding within time. The paper’s emphasis on mechanism-linked assays, rather than solely behavioral outputs, acknowledges that stability can be illusory. As Barbara Liskov observed, “Programs must be right first before they are fast.” This echoes the need for a rigorous understanding of how a system achieves persistence and preference – its internal mechanisms – before attempting to assess its conscious state. The ReCoN framework, with its inspectable loop, doesn’t aim to create consciousness, but rather to offer a platform for observing the conditions under which complex, time-dependent behaviors emerge, accepting that even the most stable systems are subject to the inevitable decay inherent in their existence.

The Long Echo

The ReCoN-Ipsundrum architecture, by its very inspectability, highlights a persistent truth: every bug is a moment of truth in the timeline. This isn’t a failure of engineering, but a precise localization of decay. The system’s reliance on a recurrent sensorimotor loop, while offering a compelling model of internal persistence, merely postpones the inevitable drift toward entropy. Future iterations will undoubtedly refine the affect coupling and preference stability, but the fundamental question remains: can structured exploration truly emerge from mechanism, or does it simply represent a more sophisticated form of predetermination?

The advocacy for mechanism-linked assays is, of course, a necessary step, but a potentially seductive one. It is tempting to believe that consciousness resides within a specific, identifiable circuit. Yet, this pursuit risks mistaking correlation for causation – charting the where of experience, rather than understanding the why of its unfolding. Technical debt is the past’s mortgage paid by the present; each refinement, each ‘solved’ mechanism, simply accrues a new form of obligation to the future.

The true challenge isn’t building an artificial consciousness, but accepting the inevitability of its artificial aging. The system will not remain static; it will, like all systems, degrade, adapt, and ultimately, transform. The task, then, isn’t to achieve a perfect, immutable intelligence, but to chart the contours of its decay, and to find a strange beauty in its transient existence.

Original article: https://arxiv.org/pdf/2602.23232.pdf

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

The Embodied Foundation of Intelligence

Prioritizing Novelty and Intensifying Sensation

Assaying Competence and Measuring Sensory Persistence

A Neurosymbolic Architecture for Embodied Agents

The Long Echo

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