aiqbench analysis
This paper provides a rigorous diagnostic showing that lower error in text-conditioned neural PDE surrogates does not inherently prove the model understands the semantic meaning of its prompts. By employing controlled experiments with constant-sentence controls and contrastive alignment, the authors demonstrate that current methods often fail to isolate whether text truly steers physical solutions or merely correlates with task labels. This methodological caution is critical for advancing trustworthy AI in scientific computing, where distinguishing genuine semantic competence from spurious pattern matching is essential.
Does Text Steer Neural PDE Surrogates? A Controlled Diagnostic with OperatorCLIP
arXiv:2609.38517v1 Announce Type: new Abstract: Lower error from a text-conditioned neural surrogate does not, by itself, show that the model uses the meaning of the text. We examine this attribution problem with OperatorCLIP, comparing an unconditioned FNO, a constant-sentence FiLM control, and a fixed task description trained with contrastive alignment. Three-seed experiments cover Darcy2D, ShallowWater2D, and three-dimensional compressible Navier-Stokes (CNS3D). Constant conditioning has lower mean test error on both 2D tasks. Relative to this control, task text plus alignment has a similar mean on ShallowWater2D and CNS3D and a higher mean on Darcy2D; these descriptive comparisons have substantial seed uncertainty. The latter comparison changes both prompt content and loss, so it isolates neither effect. The text encoder is trained from scratch, and each conditioned model sees only one description during training. In this regime, pairwise InfoNCE cannot identify matched pairs and has minimum $\log B$. Prompt interventions show no reliable semantic ordering. This methodological caution demonstrates why pathway controls are needed; it neither establishes semantic competence of the encoder nor tests the effectiveness of text under varying physical context.
Title:Does Text Steer Neural PDE Surrogates? A Controlled Diagnostic with OperatorCLIP
Abstract:Lower error from a text-conditioned neural surrogate does not, by itself, show that the model uses the meaning of the text. We examine this attribution problem with OperatorCLIP, comparing an unconditioned FNO, a constant-sentence FiLM control, and a fixed task description trained with contrastive alignment. Three-seed experiments cover Darcy2D, ShallowWater2D, and three-dimensional compressible Navier-Stokes (CNS3D). Constant conditioning has lower mean test error on both 2D tasks. Relative to this control, task text plus alignment has a similar mean on ShallowWater2D and CNS3D and a higher mean on Darcy2D; these descriptive comparisons have substantial seed uncertainty. The latter comparison changes both prompt content and loss, so it isolates neither effect. The text encoder is trained from scratch, and each conditioned model sees only one description during training. In this regime, pairwise InfoNCE cannot identify matched pairs and has minimum $\log B$. Prompt interventions show no reliable semantic ordering. This methodological caution demonstrates why pathway controls are needed; it neither establishes semantic competence of the encoder nor tests the effectiveness of text under varying physical context.
8 pages, 2 figures, 2 tables. Accepted to NeurIPS 2026 Workshop on Representation for the Physical Sciences
Machine Learning (cs.LG)
arXiv:2609.38517 [cs.LG]
(or arXiv:2609.38517v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.38517 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 296 words
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