aiqbench analysis
RideWay introduces a critical new benchmark for evaluating AI agents by measuring interaction efficiency alongside simple task completion, addressing the issue of frustrating redundant queries in tool-using systems. The study reveals that excessive dialogue turns impose significantly higher friction on users than extra tool calls, challenging current evaluation metrics that focus solely on success rates. This work is essential for advancing the practical deployment of autonomous agents in service environments where workflow smoothness matters as much as final outcomes.
RideWay: Benchmarking Efficient Task Completion for Tool-Using Language Agents
arXiv:2609.17985v1 Announce Type: new Abstract: AI agents are usually evaluated by whether they complete a task. In interactive service settings, a successful agent can still frustrate users by asking repeated questions, performing redundant searches, or making avoidable revisions. We introduce RideWay, an efficiency-centered benchmark for ridehailing agents in a stateful tool-calling environment, together with Efficiency Utility, a success-gated metric that discounts successful trajectories for excess tool calls and user-facing turns relative to task-specific reference effort. Human paired preferences calibrate the relative penalties, reflecting an aggregate service-workflow trade-off: extra dialogue often creates visible friction, whereas extra tool use can sometimes verify constraints or preserve user intent. Across 58 tasks and 24 models, the fitted penalty for excess turns is about twice that for excess tool calls. On task-disjoint held-out preferences, Efficiency Utility achieves 78.7% accuracy overall: 90.6% when trajectories differ in turns, but chance-level accuracy when they differ solely in tool calls - the axis on which human annotators agree least. RideWay therefore makes interaction efficiency measurable alongside task success, while exposing the boundary of count-based tool-use evaluation.
Abstract:AI agents are usually evaluated by whether they complete a task. In interactive service settings, a successful agent can still frustrate users by asking repeated questions, performing redundant searches, or making avoidable revisions. We introduce RideWay, an efficiency-centered benchmark for ridehailing agents in a stateful tool-calling environment, together with Efficiency Utility, a success-gated metric that discounts successful trajectories for excess tool calls and user-facing turns relative to task-specific reference effort. Human paired preferences calibrate the relative penalties, reflecting an aggregate service-workflow trade-off: extra dialogue often creates visible friction, whereas extra tool use can sometimes verify constraints or preserve user intent. Across 58 tasks and 24 models, the fitted penalty for excess turns is about twice that for excess tool calls. On task-disjoint held-out preferences, Efficiency Utility achieves 78.7% accuracy overall: 90.6% when trajectories differ in turns, but chance-level accuracy when they differ solely in tool calls - the axis on which human annotators agree least. RideWay therefore makes interaction efficiency measurable alongside task success, while exposing the boundary of count-based tool-use evaluation.
Artificial Intelligence (cs.AI)
arXiv:2609.17985 [cs.AI]
(or arXiv:2609.17985v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.17985 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 266 words
Related coverage
[AINews] High Taste Testers Ep. 1
Latent Space · Oct 17, 2026

Academia is for Ambition — Alex Zhang, MIT
Latent Space · Oct 2, 2026
Untrusted advice for AI control: Short, strong advice significantly uplifts weak LLMs
LessWrong · Jul 27, 2026

Frontier Risk Report (February to March 2026)
METR · May 19, 2026