ATOD: An Evaluation Framework and Benchmark for Agentic Task-Oriented Dialogue Systems

Abstract:Agentic task-oriented dialogue (TOD) requires systems to track concurrent goals, dependencies, and long-horizon state. We examine goal-lifecycle recovery from fixed dialogue trajectories. ATOD contains 1,000 synthetic dialogues annotated for six advanced-TOD properties, and ATOD-Eval defines metrics for dependency-sensitive completion, memory recall, and proactivity. We implement a symbolic-vector memory evaluator for ATOD-Eval. Among five prompt-only predictors and six matched-backbone memory baselines, our evaluator is the only configuration above 90% in both goal detection F1 and conditional status accuracy on medium dialogues. On complex dialogues, no baseline exceeds it on both metrics; it has the highest conditional status accuracy within the memory-based block and the lowest measured per-turn latency. These experiments assess lifecycle tracking rather than interactive agent task success. A cross-family judge swap and a manual audit with 94.0% agreement provide initial checks on measurement reliability. Code and data will be released at this https URL.

Camera-ready version accepted at AACL-IJCNLP 2026. 18 pages. Code and data: this https URL

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)

arXiv:2601.11854 [cs.CL]

(or arXiv:2601.11854v3 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2601.11854 Focus to learn more arXiv-issued DOI via DataCite

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