CUJBench: Benchmarking LLM-Agent on Cross-Modal Failure Diagnosis from Browser to Backend

• URL: http://arxiv.org/abs/2604.23455v2
• Date: Fri, 01 May 2026 07:18:53 GMT
• Title: CUJBench: Benchmarking LLM-Agent on Cross-Modal Failure Diagnosis from Browser to Backend
• Abstract summary: We present CUJBench, the first benchmark to combine browser-visible failure evidence with backend observability in a diagnostic framing.<n>The benchmark yields an overall accuracy of 19.7% with a ceiling of 52%, well below saturation.
• Score: 2.9612444540570113
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Automated failure diagnosis requires correlating browser-visible symptoms with backend observability signals, yet existing benchmarks do not evaluate this cross-modal reasoning task. Constructing one is non-trivial: multi-modal failure scenarios are costly to annotate, and live-environment capture introduces stochasticity that makes cross-run agent comparison unreliable. We present CUJBench, to our knowledge, the first benchmark to combine browser-visible failure evidence with backend observability in a diagnostic framing. CUJBench addresses annotation cost through an LLM-assisted generation pipeline with a multi-agent review loop and a three-layer annotation scheme, producing 87 labeled scenarios across five fault families, and ensures reproducibility by packaging each failure as a deterministic multi-modal snapshot with a fixed tool interface. Evaluating six frontier models under retrieval, browser-only, and full-toolset baselines, the benchmark yields an overall accuracy of 19.7% with a ceiling of 52%, well below saturation. Contrary to expectation, browser-only agents outperform full-toolset agents in aggregate, with expanded evidence access inducing unfocused exploration rather than improved synthesis. Trajectory analysis identifies cross-modal synthesis as the primary bottleneck: agents retrieve the decisive evidence but fail to attribute it correctly - a structural limitation uniform across all six models that model scale and richer tool access alone cannot resolve.

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