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QUIVER introduces a formal framework for quantifying how perturbations propagate through compound AI systems structured as computation graphs, defining sensitivity matrices, trajectory divergence, bifurcation thresholds, and distribution faithfulness, with validation on production and public pipelines.
This paper formalizes workflow learning in multi-agent LLM pipelines as an interface-constrained semi-Markov decision process (IC-SMDP) and proposes IC-ICQQ, an asynchronous decentralized Q-learning algorithm with a finite-sample bound that decomposes error sources, providing the first finite-sample guarantee for neural Q-learning under decentralized partial observability.
This paper presents a microservice architecture for production document AI pipelines that combine classification, OCR, and LLM extraction, sharing design decisions and batch profiling insights that reveal OCR, not LLM parsing, dominates latency.