Year: 2026
Type: conference-paper
Abstract: Scientific feasibility assessment asks whether a claim is consistent with established knowledge and whether experimental evidence could support or refute it.We frame feasibility assessment as a diagnostic reasoning task in which, given a hypothesis, a model predicts FEASIBLE or INFEASIBLE and justifies its decision.We evaluate large language models (LLMs) under controlled knowledge conditions (hypothesisonly, with experiments, with outcomes, or both) and probe robustness by progressively removing portions of the experimental and/or outcome context.Across multiple LLMs and two datasets, providing outcome evidence is generally more reliable than providing experiment descriptions.Outcomes tend to improve accuracy beyond what internal knowledge alone provides, whereas experimental text can be brittle and may degrade performance when the context is incomplete.These findings clarify when experimental evidence benefits LLMbased feasibility assessment and when it introduces fragility.
Institution: University of Maryland, Baltimore County
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G6181661201
OpenAlex ID: W7166868990