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arXiv AI By Rayan Khoury, Shih-Yao Lin, Pratyush Mishra

SAGE: State-Grounded, Abstention-Aware Evaluation of Task-Oriented Dialogue Agents

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SAGE (State‑Grounded, Abstention‑Aware Evaluation) is a new framework for assessing task‑oriented dialogue agents that focuses on whether each turn correctly advances the workflow state rather than just the quality of the reply. It compiles workflow specifications and per‑turn state differences into schema‑grounded criteria, then evaluates them through a cascade of symbolic rules and encoder/NLI verifiers that abstain instead of guessing, producing a turn‑level decision with an evidence trace. In experiments across MultiWOZ, Schema‑Guided Dialogue, and ABCD datasets, SAGE‑Core—using only symbolic rules and on‑device encoders—outperforms all evaluated LLM‑based judges, including GPT‑4.1 variants, while incurring zero paid LLM cost.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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