Evidence-based, non-interactive analysis of stability and coherence effects in large-scale AI systems using the SORT framework.
These demonstrations provide scenario-based structural diagnostics. They are not benchmarks, simulations, or optimization tools.
Active diagnostic applications within the SORT framework. Each application addresses a specific structural challenge in distributed AI systems.
Structural stability diagnostics for interconnect-induced performance collapse in distributed AI training and HPC systems. Identifies coupling patterns that cause economic instability despite nominal hardware health.
Diagnose incoherence between scheduler, orchestrator, runtime, and policy enforcement layers. Identifies control conflicts and retry amplification patterns that degrade system economics invisibly.
Stability control for agent workflows including coordination under shared objectives, tool-augmented execution chains, and feedback loops. Addresses emergent instabilities in autonomous agent ensembles.
Supporting materials for context and orientation. These documents provide background on the SORT framework and engagement scope.
Overview of SORT applications for executive and strategic audiences.
Download →Scope and boundary definitions for engagement models.
Download →Framework overview presentation. Operator definitions, validation protocols.
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