Mechanical conscience is a supervisory filter that minimally corrects baseline AI policies to reduce cumulative deviation from admissible behavioral trajectories under epistemic uncertainty.
Edge computing: Vision and challenges
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
years
2026 7verdicts
UNVERDICTED 7representative citing papers
PA-LLM-RAG adds policy retrieval and dual-LLM verification to enable reliable low-latency mission orchestration in simulated IoBT environments, with Gemma-2B reaching 100% policy compliance at 4.17s latency.
Empirical case study on a flagship Android device profiles energy, latency, and quality trade-offs across eight LLMs, revealing a quantization energy paradox and identifying mid-sized models as practical sweet spots.
A pattern-based workflow engineering approach with AI assistance enables rapid development of sensor-driven applications across edge-to-core infrastructures, shown via reusable templates in environmental monitoring case studies.
Lyapunov-based lightweight AI agent achieves O(N) complexity for joint PQC-NOMA allocation in edge systems, with claimed 46x speedup over SCA and improved throughput in simulations.
An edge-cloud architecture fuses five sensor types into a four-dimensional risk score for elderly care, achieving 91% activity recognition accuracy and sub-3-second alerts while keeping raw data local.
A trust-aware federated hybrid intrusion detection framework using random forest, decision tree, and linear SVM is proposed for intelligent transport systems with edge computing.
citing papers explorer
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Mechanical Conscience: A Mathematical Framework for Dependability of Machine Intelligenc
Mechanical conscience is a supervisory filter that minimally corrects baseline AI policies to reduce cumulative deviation from admissible behavioral trajectories under epistemic uncertainty.
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Policy-Aware Edge LLM-RAG Framework for Internet of Battlefield Things Mission Orchestration
PA-LLM-RAG adds policy retrieval and dual-LLM verification to enable reliable low-latency mission orchestration in simulated IoBT environments, with Gemma-2B reaching 100% policy compliance at 4.17s latency.
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Sustainability Is Not Linear: Quantifying Performance, Energy, and Privacy Trade-offs in On-Device Intelligence
Empirical case study on a flagship Android device profiles energy, latency, and quality trade-offs across eight LLMs, revealing a quantization energy paradox and identifying mid-sized models as practical sweet spots.
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From Sensors to Insight: Rapid, Edge-to-Core Application Development for Sensor-Driven Applications
A pattern-based workflow engineering approach with AI assistance enables rapid development of sensor-driven applications across edge-to-core infrastructures, shown via reusable templates in environmental monitoring case studies.
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Lightweight Quantum Agent for Edge Systems: Joint PQC and NOMA Resource Allocation
Lyapunov-based lightweight AI agent achieves O(N) complexity for joint PQC-NOMA allocation in edge systems, with claimed 46x speedup over SCA and improved throughput in simulations.
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An Edge-Cloud Collaborative Architecture for Proactive Elderly Care: Real-Time Risk Assessment and Three-Level Emergency Response
An edge-cloud architecture fuses five sensor types into a four-dimensional risk score for elderly care, achieving 91% activity recognition accuracy and sub-3-second alerts while keeping raw data local.
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A Comparative Analysis of Machine Learning Models for Intrusion Detection in Intelligent Transport Systems
A trust-aware federated hybrid intrusion detection framework using random forest, decision tree, and linear SVM is proposed for intelligent transport systems with edge computing.