Title: BRIDGE: A Risk-Aware Framework for Evaluating Behavioral Fidelity in LLM Agents
Primary area: interpretability and explainable AI
Keywords: LLM-based Agents, Behavioral Fidelity, Evaluation Frameworks, Conformal Prediction, Guardrails, Trustworthiness, Dialog Behavior Analysis, Conversational Recommender Systems

Abstract: We study behavioral fidelity: whether a conversational agent reliably enacts intended aims. While recent work evaluates accuracy or task success, it offers limited tools for calibrated behavioral assessment in multi-turn dialog. We present \textsc{BRIDGE}, a selective evaluation framework with deterministic gates for validity and safety, a probabilistic judge for trait scoring, and a conformal-inspired mapper that converts scores to \{\texttt{yes}, \texttt{ambiguous}, \texttt{no}\} using per-trait dual thresholds centered at empirical human prevalence with half-width \((1-\kappa)/2\), where \(\kappa\) is interrater agreement. A dialog passes only if all relevant traits are \texttt{yes}; otherwise, it abstains and escalates. Thresholds are derived from a small, disjoint, human-annotated calibration set. On 5{,}960 dialogs across two domains and two aims, 87.7\% pass without human review, and abstentions concentrate in semantically appropriate traits. Stored thresholds match the declared rule to machine precision.

TL;DR: BRIDGE measures behavioral fidelity via validity/safety gates, a calibrated probabilistic judge, and conformal style dual thresholds that trigger abstention and escalation; tested on 5,960 multi turn synthetic dialogs.