Building AI You Can Understand

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Author: Denis Avetisyan

A new cognitive architecture aims to move beyond opaque machine learning models by grounding AI reasoning in foundational principles of human cognition.

This review proposes Weight-Calculatism, a framework for explainable and aligned AI based on logical atoms, causal reasoning, and auditable value systems.

Despite advances in artificial intelligence, achieving genuinely explainable and aligned systems remains a fundamental challenge. This paper, ‘Beyond the Black Box: A Cognitive Architecture for Explainable and Aligned AI’, introduces Weight-Calculatism, a novel cognitive architecture built upon foundational principles of logical atomism and transparent weight assignment. By deconstructing cognition into auditable components, this framework enables interpretable decision-making and robust generalization. Could this approach finally offer a pathway toward trustworthy artificial general intelligence and a resolution to the ‘black box’ problem?

The Illusion of Intelligence: Why Correlation Isn’t Enough

Contemporary artificial intelligence, particularly systems built on Deep Learning architectures, demonstrates remarkable proficiency in identifying patterns within vast datasets. However, this strength masks a fundamental limitation: a disconnect between correlation and causation, often termed the ‘Causality Gap’. These systems excel at recognizing that two things frequently occur together – for example, sunshine and ice cream sales – but lack the ability to understand why they are related. Consequently, AI struggles to extrapolate beyond the observed data; a slight alteration in circumstances, like a sudden cold snap, can lead to dramatically incorrect predictions. This isn’t a matter of insufficient data, but a limitation in the underlying approach, where statistical relationships are prioritized over genuine comprehension of the mechanisms driving those relationships. The result is brittle intelligence, capable of impressive feats within narrow parameters, yet easily confounded by the complexities of the real world and unable to exhibit robust, generalized reasoning.

A fundamental limitation of current artificial intelligence lies in its dependence on identifying correlations within data, rather than grasping underlying causal relationships. While adept at recognizing patterns – for example, noting that ice cream sales and crime rates rise concurrently – these systems struggle to understand why such a relationship exists, or to predict outcomes when presented with genuinely new scenarios. This is particularly acute in the field of Embodied Intelligence, where AI agents must interact with the physical world; an agent trained solely on correlated data might, for instance, incorrectly assume a specific action always leads to a certain result, failing to adapt when environmental conditions change. Consequently, the inability to generalize beyond observed correlations presents a significant barrier to creating robust, adaptable AI capable of navigating the complexities of real-world interactions, highlighting the need for systems that can reason about cause and effect, not just identify statistical associations.

The pursuit of artificial intelligence increasingly confronts the ‘Value Grounding Problem’, a fundamental challenge in establishing coherent and beneficial goals for these systems. Unlike humans who inherit a complex web of evolved and culturally-transmitted values, AI operates on explicitly programmed objectives, which are often incomplete or misaligned with genuine human well-being. Simply instructing an AI to ‘maximize happiness’ proves problematic, as defining and quantifying such abstract concepts is inherently subjective and prone to unintended consequences; an AI might optimize for easily measurable proxies, like dopamine release, ignoring broader ethical considerations. This disconnect arises because AI lacks intrinsic understanding of what constitutes a ‘good’ outcome, relying instead on the data and reward signals provided – potentially leading to solutions that are technically correct, yet ethically undesirable or even harmful. Consequently, researchers are actively exploring methods to imbue AI with robust value systems, ranging from inverse reinforcement learning – where AI infers goals from observed behavior – to incorporating human feedback and ethical constraints directly into the learning process, yet a universally accepted solution remains elusive.

Building Cognition from First Principles: A Weight-Calculative Approach

Weight-Calculatism posits that cognitive decision-making is fundamentally a computational process centered on evaluating potential actions. This evaluation is achieved by calculating the ‘weight’ of each action, determined by the product of its anticipated $Benefit$ and the $Probability$ of achieving that benefit. This weight represents the expected value of the action, providing a quantitative basis for comparison between alternatives. The architecture assumes that agents do not simply choose the most appealing option, but rather perform a calculation – multiplying perceived benefit by estimated probability – to arrive at a decision. This calculation is not necessarily conscious; the framework suggests it is a core operating principle of the cognitive system, influencing behavior at multiple levels.

The Weight-Calculatism architecture posits that all conscious experience is ultimately composed of ‘Logical Atoms’, discrete and indivisible units representing basic perceptions or concepts. These atoms are not merely data points, but the fundamental building blocks of knowledge. The organization of these atoms is achieved through a ‘Logical Atom Graph’, a network where each atom is a node and connections represent relationships between them. This graph isn’t static; it’s a dynamic structure constantly updated by sensory input and internal processing, effectively forming the system’s complete representation of the world and serving as the basis for all subsequent cognitive operations. The structure allows for complex knowledge representation through interconnectedness, enabling the system to infer new information based on existing relationships between atoms.

The architecture utilizes ‘Pointing’ and ‘Comparison’ as core operations to construct relationships between Logical Atoms. ‘Pointing’ establishes directed links, effectively creating associations between individual atoms representing discrete concepts or perceptions. ‘Comparison’ assesses the similarity or difference between atoms, potentially weighted by their associated ‘weight’ ($Benefit \times Probability$). These operations are not isolated; repeated applications of ‘Pointing’ and ‘Comparison’ generate a complex network where any atom can be related to any other, albeit through potentially multiple intermediary connections. This interconnectedness facilitates the propagation of activation and allows the system to represent complex propositions and derive inferences based on the relationships established between its constituent Logical Atoms.

Demonstrating the Architecture: Scenarios and Validation

The ‘Fire Escape Scenario’ involved presenting Weight-Calculatism with a simulated building fire and a set of available actions, including ‘carry scientific-notes’, ‘carry canned-food’, ‘alert neighbors’, and ‘evacuate immediately’. The system assigned a calculated weight to each action based on pre-defined parameters related to information preservation, sustenance, and safety. Critically, the system’s rationale was fully traceable; each weight assigned could be directly linked to the specific parameter values and the defined weighting function. This allowed for complete auditability of the decision-making process, confirming the system doesn’t operate as a ‘black box’ and provides a clear, demonstrable justification for its prioritized actions in a high-pressure situation.

The architecture’s ability to reason in completely novel contexts was validated through the ‘Alien Ecosystem Scenario’. This involved presenting the system with data describing a hypothetical extraterrestrial biosphere and assessing its capacity for analogical reasoning with known Earth biology. The system successfully identified similarities, resulting in a calculated similarity score of 0.106. This score indicates the degree to which the system could draw parallels between the alien ecosystem and terrestrial life, demonstrating its capacity to generalize beyond pre-programmed knowledge and apply reasoning to entirely new datasets.

During a simulated moral dilemma, the Weight-Calculatism architecture assigned a calculated weight of 20.80 to the action ‘carry scientific-notes’, while the action ‘carry canned-food’ received a weight of only 0.80. This disparity demonstrates the system’s capacity for quantifiable reasoning, specifically prioritizing the preservation of information – as represented by the scientific notes – over basic sustenance in the context of the defined criteria. The significant difference in calculated weight provides a concrete example of how the architecture assigns value and makes decisions based on assigned weights, rather than arbitrary or subjective factors.

Beyond the Hype: Extending Cognitive Science and Addressing Core Limitations

Weight-Calculatism emerges not as a rival to established cognitive architectures like Predictive Processing, but as a complementary framework designed to overcome critical shortcomings in value alignment and environmental adaptability. While Predictive Processing excels at modeling perception and action through hierarchical prediction error minimization, it often struggles to account for the origins of goals and the robust instantiation of values. Weight-Calculatism addresses this by introducing ‘Initial Weights’ – foundational values that pre-structure cognitive processes – allowing for a more principled explanation of how agents prioritize certain outcomes over others. This extension doesn’t discard the strengths of PP; instead, it layers an account of intrinsic motivation and goal-directed behavior, ultimately yielding a system capable of not just predicting the world, but also navigating it with a consistent and explainable value system – a key step towards genuinely intelligent and trustworthy artificial intelligence.

Weight-Calculatism proposes a fundamental shift in artificial intelligence design by anchoring cognitive processes in foundational principles and emphasizing causal inference. Unlike many current AI systems reliant on statistical correlations, this framework prioritizes understanding why events occur, fostering a system capable of generalizing beyond training data and adapting to novel situations with greater resilience. By constructing cognition from first principles-essentially, building intelligence from the ground up based on inherent logical structures-the architecture aims to overcome the ‘black box’ problem often associated with deep learning. This approach doesn’t simply seek to improve pattern recognition, but to imbue AI with a capacity for genuine reasoning, leading to more robust, transparent, and ultimately, explainable artificial intelligence systems capable of justifying their actions and decisions.

The foundation of Weight-Calculatism rests on the concept of ‘Initial Weights’, representing an AI’s pre-programmed values – essentially, its inherent goals at the outset of learning. Unlike traditional AI development where reward functions are externally imposed, this architecture posits that motivations are intrinsic, encoded directly into the system’s core. This approach allows for a far more granular understanding of an AI’s decision-making process, as actions aren’t simply optimized for a given reward, but are instead evaluated against these foundational values. Consequently, ethical considerations aren’t treated as an afterthought, but are woven into the very fabric of the AI’s cognitive structure, offering a potential solution to the alignment problem and enabling the creation of artificial intelligence whose behavior is predictable, transparent, and consistently aligned with its predetermined objectives. The nuanced approach to defining these initial values also allows for a richer exploration of complex motivations beyond simple maximization, potentially leading to AI systems capable of exhibiting more sophisticated and human-like reasoning.

The pursuit of explainable AI, as detailed in this architecture, feels less like innovation and more like a recurring cycle. The framework attempts to build auditable value systems into the core of cognition, striving for transparency through Weight-Calculatism. It’s a noble effort, but one inevitably shadowed by the understanding that every elegantly designed system will, in time, reveal its own compromises. As David Hilbert observed, “We must be able to answer the question: what are the ultimate foundations of mathematics?” The same applies here-the foundations of AI are perpetually shifting, and this architecture, like all before it, will one day be optimized back into the very complexity it seeks to resolve. The goal isn’t perfection, it’s simply a more sustainable compromise.

The Road Ahead

The pursuit of ‘explainable’ and ‘aligned’ artificial intelligence invariably circles back to foundational questions of representation and value. Weight-Calculatism, with its emphasis on logical atoms and auditable systems, presents a compelling, if ambitious, attempt to address these concerns. Yet, the history of cognitive architectures is littered with elegant designs that succumbed to the messy realities of implementation. The devil, predictably, will reside not in the theoretical soundness of the framework, but in its ability to scale beyond contrived examples and gracefully handle the inherent ambiguity of real-world data.

One anticipates the inevitable tension between the desire for transparent, atomistic reasoning and the computational demands of complex tasks. Will the explicit representation of every causal link and value judgment prove tractable, or will performance necessitate compromises that reintroduce the very opacity this architecture seeks to avoid? The claim of value alignment, in particular, feels… familiar. Each generation rediscovers the difficulty of encoding ethics into algorithms, often concluding that the problem wasn’t technical, but philosophical – a realization typically postponed until after significant engineering effort.

Future work will undoubtedly focus on integrating Weight-Calculatism with existing deep learning paradigms. The challenge, however, won’t be merely technical compatibility. It will be resisting the temptation to treat this architecture as just another layer of abstraction, another ‘black box’ with a slightly more detailed user interface. If all tests pass, it will likely indicate a lack of rigorous testing, not a genuinely aligned intelligence.

Original article: https://arxiv.org/pdf/2512.03072.pdf

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

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2025-12-05 01:59