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While model and agent evaluation are inextricably linked, their technical benchmarks and metrics for success are fundamentally different.

Agent skills guide a developer through the repeated steps of clinical ASR evaluation: defining a profile, building a term-centered benchmark, reviewing pronunciations, generating synthetic audio, measuring ASR behavior, and choosing the next iteration.  In this post, the flywheel is the full improvement loop: build the benchmark, evaluate ASR behavior, use the results to decide what to change, and reevaluate after the change. The pipeline is one pass through part of that loop, such as generating sentences, adding pronunciation markup, synthesizing audio, and writing the manifest. The pipeline

AA-AgentPerf is a hardware benchmark created by Artificial Analysis that measures the number of concurrent AI agents an inference system can support while meeting predefined, model-specific performance service level objective (SLO) tiers. An SLO is defined as a specific threshold of output token speed and time-to-first-token (TTFT). The benchmark results are normalized per accelerator and per megawatt to enable comparison across hardware configurations.

The Ising Calibration 1.5 model is trained on data generated from partner contributions across multiple qubit modalities, including superconducting qubits, quantum dots, ions, neutral atoms, electrons on Helium, and others specializing in calibration and control.