PRGA gates wireless intent execution with progressive evidence stages, cutting time-to-first-safe-action by 23-27% and control-plane bytes by 52-54% on 3GPP benchmarks while rejecting all stale inputs and staying within a 0.5pp unsafe-action margin.
An AI-driven intent-based network architecture,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
GSID applies an adaptive configuration encoder and inconsistency dynamic attention on bipartite graphs to detect protocol configuration anomalies, reporting threefold F1 improvement and 23.2% accuracy gain over baselines.
citing papers explorer
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Executor-Side Progressive Risk-Gated Actuation for Agentic AI in Wireless Supervisory Control
PRGA gates wireless intent execution with progressive evidence stages, cutting time-to-first-safe-action by 23-27% and control-plane bytes by 52-54% on 3GPP benchmarks while rejecting all stale inputs and staying within a 0.5pp unsafe-action margin.
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Enhancing Network Resilience via Graph-Based Anomaly Detection in Sovereign Functions
GSID applies an adaptive configuration encoder and inconsistency dynamic attention on bipartite graphs to detect protocol configuration anomalies, reporting threefold F1 improvement and 23.2% accuracy gain over baselines.