… The assay removes knowledge dependencies, semantics and ambiguity from evaluation, avoids lexical and tokenization confounds, and provides a direct measure of procedural reliability beyond standard knowledge-based benchmarks. …
… Yet their reliability on complex, low-frequency interactions is still poor, limiting user trust. …
… However, existing benchmarks often fail to faithfully reflect human judgment, especially for strong frontier models, due to limited task difficulty and coarse-grained evaluation protocols. …
… The key finding: capital-agent reliability is an operating-layer problem. The largest reliability gains came from prompt compilation, typed controls, policy validation, execution guards, memory semantics, and full instruction-to-settlement observability. …
…AI-generated summary Agent evaluation requires assessing complex multi-step behaviors involving tool use and intermediate reasoning, making it costly and expertise-intensive. A natural question arises: can frontier coding assistants reliably…
… Yet, in conversational benchmarks, which simulate real-world customer-centric issue resolution scenarios, these agents frequently fail due to the cascading effects of incorrect decision-making. …
… Using Bradley-Terry modeling , we construct a multilingual leaderboard , interpret human preference using SHAP analysis and analyze leaderboard reliability alongside model strengths and trade-offs across perceptual dimensions. …
… Existing ICU benchmarks typically treat historical clinician actions as ground truth. …
… We use this difference as a reliability signal, prefix consistency , that weights each candidate answer by how often it reappears under regeneration. …
… Despite its importance, existing relevant benchmarks largely evaluate agents on pre-specified or synthesized files with limited real-world dependencies, leaving workspace-level evaluation underexplored. …