Cognitive Silicon: An Architectural Blueprint for Post-Industrial Computing Systems
- URL: http://arxiv.org/abs/2504.16622v1
- Date: Wed, 23 Apr 2025 11:24:30 GMT
- Title: Cognitive Silicon: An Architectural Blueprint for Post-Industrial Computing Systems
- Authors: Christoforus Yoga Haryanto, Emily Lomempow,
- Abstract summary: This paper presents a hypothetical full-stack architectural framework projected toward 2035, exploring a possible trajectory for cognitive computing system design.<n>The proposed architecture would integrate symbolic scaffolding, governed memory, runtime moral coherence, and alignment-aware execution across silicon-to-semantics layers.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Autonomous AI systems reveal foundational limitations in deterministic, human-authored computing architectures. This paper presents Cognitive Silicon: a hypothetical full-stack architectural framework projected toward 2035, exploring a possible trajectory for cognitive computing system design. The proposed architecture would integrate symbolic scaffolding, governed memory, runtime moral coherence, and alignment-aware execution across silicon-to-semantics layers. Our design grammar has emerged from dialectical co-design with LLMs under asymmetric epistemic conditions--creating structured friction to expose blind spots and trade-offs. The envisioned framework would establish mortality as a natural consequence of physical constraints, non-copyable tacit knowledge, and non-cloneable identity keys as cognitive-embodiment primitives. Core tensions (trust/agency, scaffolding/emergence, execution/governance) would function as central architectural pressures rather than edge cases. The architecture theoretically converges with the Free Energy Principle, potentially offering a formal account of how cognitive systems could maintain identity through prediction error minimization across physical and computational boundaries. The resulting framework aims to deliver a morally tractable cognitive infrastructure that could maintain human-alignment through irreversible hardware constraints and identity-bound epistemic mechanisms resistant to replication or subversion.
Related papers
- Theoretical Foundations for Semantic Cognition in Artificial Intelligence [0.0]
monograph presents a modular cognitive architecture for artificial intelligence grounded in the formal modeling of belief as structured semantic state.
Belief states are defined as dynamic ensembles of linguistic expressions embedded within a navigable manifold, where operators enable assimilation, abstraction, nullification, memory, and introspection.
arXiv Detail & Related papers (2025-04-29T23:10:07Z) - Coarse Set Theory for AI Ethics and Decision-Making: A Mathematical Framework for Granular Evaluations [0.0]
Coarse Ethics (CE) is a theoretical framework that justifies coarse-grained evaluations, such as letter grades or warning labels, as ethically appropriate under cognitive and contextual constraints.<n>This paper introduces Coarse Set Theory (CST), a novel mathematical framework that models coarse-grained decision-making using totally ordered structures and coarse partitions.<n>CST defines hierarchical relations among sets and uses information-theoretic tools, such as Kullback-Leibler Divergence, to quantify the trade-off between simplification and information loss.
arXiv Detail & Related papers (2025-02-11T08:18:37Z) - ActPC-Chem: Discrete Active Predictive Coding for Goal-Guided Algorithmic Chemistry as a Potential Cognitive Kernel for Hyperon & PRIMUS-Based AGI [0.0]
We explore a novel paradigm (labeled ActPC-Chem) for biologically inspired, goal-guided artificial intelligence (AI)<n>ActPC is centered on a form of Discrete Active Predictive Coding (ActPC) operating within an algorithmic chemistry of rewrite rules.
arXiv Detail & Related papers (2024-12-21T09:14:25Z) - ALPINE: Unveiling the Planning Capability of Autoregressive Learning in Language Models [48.559185522099625]
Planning is a crucial element of both human intelligence and contemporary large language models (LLMs)
This paper investigates the emergence of planning capabilities in Transformer-based LLMs via their next-word prediction mechanisms.
arXiv Detail & Related papers (2024-05-15T09:59:37Z) - Hierarchical Invariance for Robust and Interpretable Vision Tasks at Larger Scales [54.78115855552886]
We show how to construct over-complete invariants with a Convolutional Neural Networks (CNN)-like hierarchical architecture.
With the over-completeness, discriminative features w.r.t. the task can be adaptively formed in a Neural Architecture Search (NAS)-like manner.
For robust and interpretable vision tasks at larger scales, hierarchical invariant representation can be considered as an effective alternative to traditional CNN and invariants.
arXiv Detail & Related papers (2024-02-23T16:50:07Z) - Discrete, compositional, and symbolic representations through attractor dynamics [51.20712945239422]
We introduce a novel neural systems model that integrates attractor dynamics with symbolic representations to model cognitive processes akin to the probabilistic language of thought (PLoT)
Our model segments the continuous representational space into discrete basins, with attractor states corresponding to symbolic sequences, that reflect the semanticity and compositionality characteristic of symbolic systems through unsupervised learning, rather than relying on pre-defined primitives.
This approach establishes a unified framework that integrates both symbolic and sub-symbolic processing through neural dynamics, a neuroplausible substrate with proven expressivity in AI, offering a more comprehensive model that mirrors the complex duality of cognitive operations
arXiv Detail & Related papers (2023-10-03T05:40:56Z) - A Compositional Approach to Creating Architecture Frameworks with an
Application to Distributed AI Systems [16.690434072032176]
We show how compositional thinking can provide rules for the creation and management of architectural frameworks for complex systems.
The aim of the paper is not to provide viewpoints or architecture models specific to AI systems, but instead to provide guidelines on how a consistent framework can be built up with existing, or newly created, viewpoints.
arXiv Detail & Related papers (2022-12-27T18:05:02Z) - On Binding Objects to Symbols: Learning Physical Concepts to Understand
Real from Fake [155.6741526791004]
We revisit the classic signal-to-symbol barrier in light of the remarkable ability of deep neural networks to generate synthetic data.
We characterize physical objects as abstract concepts and use the previous analysis to show that physical objects can be encoded by finite architectures.
We conclude that binding physical entities to digital identities is possible in finite time with finite resources.
arXiv Detail & Related papers (2022-07-25T17:21:59Z) - Kernel Based Cognitive Architecture for Autonomous Agents [91.3755431537592]
This paper considers an evolutionary approach to creating a cognitive functionality.
We consider a cognitive architecture which ensures the evolution of the agent on the basis of Symbol Emergence Problem solution.
arXiv Detail & Related papers (2022-07-02T12:41:32Z) - Logical blocks for fault-tolerant topological quantum computation [55.41644538483948]
We present a framework for universal fault-tolerant logic motivated by the need for platform-independent logical gate definitions.
We explore novel schemes for universal logic that improve resource overheads.
Motivated by the favorable logical error rates for boundaryless computation, we introduce a novel computational scheme.
arXiv Detail & Related papers (2021-12-22T19:00:03Z) - A Mathematical Framework for Consciousness in Neural Networks [0.0]
This paper presents a novel mathematical framework for bridging the explanatory gap between consciousness and its physical correlates.<n>We do not claim that qualia are singularities or that singularities "explain" why qualia feel as they do.<n>We establish a framework that recognizes qualia as phenomena inherently beyond reduction to complexity, computation, or information.
arXiv Detail & Related papers (2017-04-04T18:32:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.