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This is a Experimental Study study.

October 10, 2025Open Access

Self-Regulating, Knowledge-Driven Distributed Software Systems: Mindful Machines

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Mindful Machines excel in resilience through automated fault recovery and elastic scaling, enhancing performance in distributed settings.
The digital genome enables self-regulation and ethical constraints, contributing to the development of trustworthy AI systems.
The architecture's explainability is strengthened through event history and auditable decision trails, allowing for better transparency.
Real-time adaptability to behavioral changes without retraining is a significant advancement over conventional monolithic ML pipelines.

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Implication

Introduction of Mindful Machines enhances self-regulation and adaptability in distributed software systems, suggesting improved resilience and explainability.

Cite This Study

Mikkilineni et al. (2025) studied this question.

synapsesocial.com/papers/68e861907ef2f04ca37e3f86 https://doi.org/10.20944/preprints202510.0020.v1

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Implication

Introduction of Mindful Machines enhances self-regulation and adaptability in distributed software systems, suggesting improved resilience and explainability.

Self-Regulating, Knowledge-Driven Distributed Software Systems: Mindful Machines

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Implication

Cite This Study

Self-Regulating, Knowledge-Driven Distributed Software Systems: Mindful Machines

Key Points

Discussion

Implication

Cite This Study

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