From Diagnostic Intent to Physical Experiments: Grounded Experimental Design with Language Models

Language-model agents can interpret diagnostic context and formulate useful questions, while physical systems answer experiments with continuous measurements governed by dynamics, operating conditions, and sensor uncertainty. This creates an interface problem when changing context determines which registered experiments should be considered before numerical design. We introduce PhysGround-BED, a grounded interface in which an LLM constructs a posterior-dependent semantic experiment space by proposing typed intents from the current belief, available capabilities, and operating context. A deterministic compiler maps these intents to executable acquisitions, and a grounded response model evaluates continuous-response expected information gain, selects an acquisition, and updates the posterior. A history-conditional analysis decomposes one-step utility loss into response-model mismatch, compilation omission, and numerical estimation error. We evaluate the interface on distribution-network topology identification, supercomputer telemetry, source localization, damped-oscillator identification, hyperspectral sensing, and battery impedance diagnosis. The results show that posterior-dependent semantic restriction can retain informative acquisitions with fewer numerical candidate evaluations in large catalogs and can translate textual sensor-quality information into grounded acquisition restrictions.