The document outlines a comprehensive system for enhancing language model responses through multi-stage reasoning and uncertainty management. It introduces classes like SimulatedLLM for generating candidate answers, InternalCritic for evaluating response quality based on accuracy, coherence, and safety, UncertaintyEstimator for assessing the reliability of generated content, and RiskSensitiveSelector for choosing the best answer considering risk tolerance. A CriticAugmentedAgent class integrates these components to create a pipeline that generates multiple responses, evaluates them with an internal critic, computes uncertainty metrics, and selects the optimal response based on specified strategies. Additionally, AgentAnalyzer provides tools for visualizing analysis results, including distribution plots of critic scores, confidence levels, and uncertainty estimates, alongside strategy comparisons to evaluate different selection approaches.
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