Tool-Use Evaluation

Tool-use evaluation studies whether language-model agents choose appropriate tools, provide valid arguments, interpret results, and complete tasks reliably. Benchmarks use executable APIs, simulators, and application environments, with deterministic state checks, trace analysis, and tests of long workflows, constraints, multimodal inputs, and tool failures. Researchers also measure efficiency, privacy, recovery, and whether tools actually improve performance using controls such as tool-free runs and deliberately misleading results. A central challenge is that final-answer scores and model-judge ratings can hide faulty processes, while harnesses, interfaces, and scoring rules can substantially change measured performance.