QUIVER: A Formal Framework for Quantifying Perturbation Propagation and Bifurcation in Compound AI Systems
The article introduces QUIVER, a formal framework designed to quantify perturbation propagation and bifurcation in compound AI systems. This framework addresses the challenges posed by the stochastic nature of nodes and divergent execution paths in AI architectures. QUIVER has been validated on various production pipelines, demonstrating its ability to reveal sensitivity profiles and predict trajectory bifurcations.
- ▪QUIVER is a framework for measuring perturbation propagation in graph-structured LLM pipelines.
- ▪It defines a sensitivity matrix that classifies edges and identifies bifurcation thresholds.
- ▪The framework has been validated on two production enterprise pipelines and a public multihop QA pipeline.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23956 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
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Computer Science > Artificial Intelligence arXiv:2605.23956 (cs) [Submitted on 11 May 2026] Title:QUIVER: A Formal Framework for Quantifying Perturbation Propagation and Bifurcation in Compound AI Systems Authors:Prashanti Nilayam, Sankalp Nayak View a PDF of the paper titled QUIVER: A Formal Framework for Quantifying Perturbation Propagation and Bifurcation in Compound AI Systems, by Prashanti Nilayam and 1 other authors View PDF HTML (experimental) Abstract:Compound AI systems that chain multiple LLM calls into directed computation graphs are now the dominant architecture for production AI.
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