aiqbench analysis
The paper introduces a benchmark that scores LLM tool-use agents on both final outcomes and whether they follow required procedures such as disclosures and consent, across six regulated customer-service domains. Its headline finding is that 55.5% of executions reach the correct end state through non-compliant processes, so outcome-only scores overstate agent readiness. For readers building or evaluating agents in regulated settings, the practical point is that process compliance needs its own measurement, and the rankings of models can change when it is included.
CXBench: A Benchmark for Process-Compliant LLM Agents in Regulated Customer-Experience Workflows
arXiv:2610.25954v1 Announce Type: new Abstract: Tool-using language agents increasingly perform regulated customer-service workflows, where a correct final state is insufficient: agents must also provide disclosures, obtain consent, and follow required procedures. Existing tool-agent benchmarks largely score final business states, accepting trajectories that reach correct outcomes through non-compliant processes. We introduce cxbench, a benchmark that evaluates outcome and process compliance separately across six regulated domains. Each task combines a customer request, a tool-based environment, and explicit process requirements. Rule cards link cited regulatory obligations to observable trace evidence. A hybrid grader combines deterministic checks for ordering and authority with an LLM judge for semantic requirements. We define a false-pass as an outcome-pass run that fails process checks. Across seven models and 120 tasks, 55.5% of the 840 retained executions are false-passes, representing 80.1% of outcome-passing runs; only 13.8% pass both checks. Process-inclusive evaluation changes the observed model ordering and exposes differences hidden by outcome-only scoring. cxbench makes these failures inspectable through trace-linked criteria.
Abstract:Tool-using language agents increasingly perform regulated customer-service workflows, where a correct final state is insufficient: agents must also provide disclosures, obtain consent, and follow required procedures. Existing tool-agent benchmarks largely score final business states, accepting trajectories that reach correct outcomes through non-compliant processes. We introduce cxbench, a benchmark that evaluates outcome and process compliance separately across six regulated domains. Each task combines a customer request, a tool-based environment, and explicit process requirements. Rule cards link cited regulatory obligations to observable trace evidence. A hybrid grader combines deterministic checks for ordering and authority with an LLM judge for semantic requirements. We define a false-pass as an outcome-pass run that fails process checks. Across seven models and 120 tasks, 55.5% of the 840 retained executions are false-passes, representing 80.1% of outcome-passing runs; only 13.8% pass both checks. Process-inclusive evaluation changes the observed model ordering and exposes differences hidden by outcome-only scoring. cxbench makes these failures inspectable through trace-linked criteria.
Artificial Intelligence (cs.AI)
arXiv:2610.25954 [cs.AI]
(or arXiv:2610.25954v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.25954 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 249 words