ASPO combines multi-agent LLM proposals with deterministic enforcement in a MAPE-K loop to select conflict-free, resource-feasible security patterns for IoT, delivering 100% safety invariants and 21-23% tail latency/energy reductions on testbed workloads.
The vision of autonomic computing,
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
Proposes four architectural patterns—Hybrid Affordance Integration, Adaptive Visual Anchoring, Visual Hierarchy Synthesis, and Semantic Scene Graph—to balance non-determinism and latency of foundation models with enterprise requirements for determinism and real-time performance.
citing papers explorer
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Self-Adaptive Multi-Agent LLM-Based Security Pattern Selection for IoT Systems
ASPO combines multi-agent LLM proposals with deterministic enforcement in a MAPE-K loop to select conflict-free, resource-feasible security patterns for IoT, delivering 100% safety invariants and 21-23% tail latency/energy reductions on testbed workloads.
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A Pattern Language for Resilient Visual Agents
Proposes four architectural patterns—Hybrid Affordance Integration, Adaptive Visual Anchoring, Visual Hierarchy Synthesis, and Semantic Scene Graph—to balance non-determinism and latency of foundation models with enterprise requirements for determinism and real-time performance.