A neurocognitive governance model formalizes a Pre-Action Governance Reasoning Loop that consults global, workflow, agent, and situational rules before each action, yielding 95% compliance accuracy with zero false escalations in a retail supply-chain implementation.
Kahneman,Thinking, Fast and Slow
4 Pith papers cite this work. Polarity classification is still indexing.
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cs.AI 4years
2026 4representative citing papers
Tri-Spirit decomposes autonomous AI into planning, reasoning, and execution layers on heterogeneous hardware, yielding 75.6% lower latency, 71.1% less energy, and 77.6% offline task completion in 2000-task simulations.
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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Think Before You Act -- A Neurocognitive Governance Model for Autonomous AI Agents
A neurocognitive governance model formalizes a Pre-Action Governance Reasoning Loop that consults global, workflow, agent, and situational rules before each action, yielding 95% compliance accuracy with zero false escalations in a retail supply-chain implementation.
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Rethinking AI Hardware: A Three-Layer Cognitive Architecture for Autonomous Agents
Tri-Spirit decomposes autonomous AI into planning, reasoning, and execution layers on heterogeneous hardware, yielding 75.6% lower latency, 71.1% less energy, and 77.6% offline task completion in 2000-task simulations.
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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.
- A Two-Dimensional Framework for AI Agent Design Patterns: Cognitive Function and Execution Topology