StreamDecisionBench: Evaluating Decisions in Force on Evolving Language Streams

Abstract:Language models increasingly make real-time decisions in applications that apply the latest answer until a newer one arrives. A late answer can prolong an outdated decision, such as a call recorder still running while a customer reads out card details, an error offline accuracy misses. We make three contributions. First, we release StreamDecisionBench (SDB), a dataset of eight streaming scenarios in four application families, with executable reference decisions derived from public rules. Second, we propose an evaluation protocol and a metric, in-force accuracy: the share of time the applied decision is correct across update intervals of 0.5-8 s. It reflects accuracy and latency jointly, attributing each error to judgment, latency or both. Third, we evaluate thirteen single-model settings, and this attribution separates speed-limited from judgment-limited models: slower, more accurate models lose 42-51% of the time to outdated answers, a fast model 34% to wrong ones. We therefore test hybrids in which a slow model corrects a fast one; with the right pairing and configuration, a hybrid outperforms every single model. However, even the best evaluated system keeps a correct decision in force only about two-thirds of the time, leaving a substantial gap for real-time use.

27 pages, 9 figures. Code and data: this https URL

Computation and Language (cs.CL)

arXiv:2609.38612 [cs.CL]

(or arXiv:2609.38612v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.38612 Focus to learn more arXiv-issued DOI via DataCite

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