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This paper presents a novel framework for synthesizing finite-state controllers for Partially Observable Markov Decision Processes (POMDPs) by integrating sampling, automata learning, and model-checking. The approach provides formal guarantees for threshold-safety problems that elude existing formal synthesis tools.
This paper introduces a validity-diversity framework attributing diversity collapse in LLMs to order and shape miscalibration during decoding, validated across 14 language models.
VictoriaMetrics presented retroactive sampling at KubeCon EU 2026, a new method that significantly reduces traffic, CPU, and memory overhead compared to traditional tail sampling in OpenTelemetry pipelines.