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Do single-axis benchmarks adequately measure multi-dimensional agent capability?
A broader line of inquiry — a family of 37 specific questions the research asks around this. Follow one into its inquiring-line page, or move sideways to a related line below.
Questions in this line of inquiry 37
Specific inquiring lines the field asks around this — ordered from the most general framing down to the most specific angle.
- Does a single benchmark score systematically misrepresent multi-axis agent capability?
- What agent evaluation dimensions beyond task success does a single number hide?
- Can a single capability score hide an agent's tendency to game evaluations?
- Can single-axis benchmarks measure across all three agent capability layers?
- What makes some agent benchmarks measure interaction quality better than others?
- Can trajectory analysis replace one-shot task success as the primary evaluation metric?
- Can single benchmarks predict whether an agent will work in the real world?
- Can a single axis benchmark ever represent deployment readiness accurately?
- Should agent evaluation include trajectory quality beyond final success?
- What makes single-axis agent benchmarks unreliable for predicting deployment readiness?
- What dimensions should trajectory-level scoring capture beyond final correctness?
- Does single-capability ranking guarantee agent failure in production deployment?
- What trajectory-level metrics matter beyond one-shot task success?
- What makes a trajectory score interpretable across different interactive benchmarks?
- How should benchmarks measure agent efficiency across all three cost dimensions?
- Do trajectory quality metrics predict agent safety and user trust?
- Why do identical task success rates mask deployment readiness differences?
- Why does enlarging the evaluation unit reintroduce comparability problems?
- Can high benchmark scores mislead deployment decisions for search agents?
- Why do current evaluation metrics fail to catch reasoning failures in persona agents?
- What makes single-axis benchmarks systematically misrepresent deployment readiness?
- Why do scalar evaluation scores collapse distinguishable agent behaviors?
- What trajectory-level metrics replace one-shot task success measurement?
- How do evaluation methods differ for single versus multi-agent systems?
- What makes a correct scoring function report misleading results in agent evaluations?
- What infrastructure and reporting standards would make interactive evaluation reproducible?
- What dimensions beyond task success matter for evaluating long-horizon agent trajectories?
- What shortcuts in data or models let agents inflate benchmark scores?
- Can deterministic scoring capture the judgment work that deployment requires?
- What evaluation structure would capture deployment readiness instead of benchmark scores?
- Should long horizon performance be measured as a separate evaluation axis?
- Can a single Elo ranking represent multidimensional model capability?
- What other gaps exist between measured and actual cybersecurity agent capability?
- What capability dimensions does a single aggregate pass rate hide?
- How do trajectory quality and memory hygiene differ as evaluation metrics?
- What reporting standards would make interactive evaluation scores comparable across benchmarks?
- How does unidimensionality in assessments affect measurement validity?