QUIVER: A Formal Framework for Quantifying Perturbation Propagation and Bifurcation in Compound AI Systems
arXiv:2605.23956v1 Announce Type: cross Abstract: Compound AI systems that chain multiple LLM calls into directed computation graphs are now the dominant architecture for production AI. Although these architectures leverage heterogeneous nodes with mixed-mode outputs, no existing framework quantifies how perturbations propagate through such pipelines, where nodes are stochastic and execution paths can diverge structurally. We introduce QUIVER, a formal framework for measuring perturbation propagation in graph-structured LLM pipelines. The framework defines: (1) a sensitivity matrix with type-d
The rapid deployment of production-grade Compound AI Systems, particularly those chaining multiple LLMs, necessitates robust methods for understanding their behavior and failure modes.
A formal framework for quantifying perturbation and bifurcation in these systems is critical for their reliable deployment, trustworthiness, and debugging of advanced AI applications.
The ability to systematically measure and manage the propagation of errors and sensitivity within complex AI agent architectures will improve development cycles and system stability.
- · AI developers
- · Enterprises deploying AI agents
- · AI safety researchers
- · DevOps for AI
- · Ad-hoc AI system integrators
- · AI companies with opaque system architectures
QUIVER provides a standardized methodology for evaluating the resilience and predictability of compound AI systems.
Improved debugging and understanding of AI agent interactions could accelerate the development and adoption of more complex autonomous systems.
Formal verification and certification processes for AI agents may emerge, impacting regulatory landscapes and market entry for new AI products.
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Read at arXiv cs.LG