A new arXiv preprint (2610.12325) poses a direct question about how large language models behave as scientific agents: do they rely on their initial task context, or do they change course in response to experimental feedback? The setting is neural operator adaptation, a task where an LLM presumably guides adjustments to a learned operator model.
The abstract frames this as an empirical question but stops short of presenting results in the available text. It describes the study's motivation and domain, but not the methodology, baseline, or conclusions. As a single source, it offers no comparison or corroboration.
What is clear is the significance: understanding whether LLM agents are static planners or feedback-driven experimenters matters for building reliable AI research assistants. Until the full paper is available, the answer remains open.