arXiv:2608. 14227v1 Announce Type: new Abstract: Preprocessing invariance is an appealing goal for spectral foundation models: a frozen model should remain useful when laboratories preprocess spectra differently.
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arXiv:2608. 02157v1 Announce Type: new Abstract: Raman spectroscopy enables non-destructive, label-free molecular characterization across materials science, biomedicine and process monitoring.
The study evaluates whether multimodal models produce consistent decisions when presented with equivalent optical spectra in different representations—tables in nanometers, micrometers, scientific notation, or photon energy, and plots in wavelength or energy. Using datasets of silicon metasurfaces and gold nanoparticles, the authors test several models (d3-nano, Jev 1.13, GPT‑6 Luna variants, Qwen3.5‑2B) and find that representation changes can shift accuracy by up to 41 percentage points, with some models benefiting from photon‑energy inputs while others suffer. Fine‑tuning with LoRA improves performance on trained formats but often relies on a shortcut of abstaining when data are truncated; training with answerable truncations mitigates this shortcut, revealing significant gaps in models’ handling of missing evidence.
arXiv:2610.09709v1 Announce Type: new Abstract: Can we determine whether a foundation model will generalize out-of-distribution (OOD) before any target data is available? Existing diagnostics require...
The paper investigates the use of the squared norm of a whitened foundation‑model embedding as a training‑free likelihood surrogate. It shows that the apparent Gaussianity of whitened coordinates stems from the projection central limit theorem, not from a true joint Gaussian distribution, and that the norm is systematically over‑dispersed compared to a Gaussian reference. The authors explain that whitening reverses the encoder’s spectral hierarchy, concentrating norm contributions in near‑degenerate directions dominated by noise, and propose interpreting the squared norm as a Mahalanobis measure of semantic atypicality rather than a log‑likelihood.
arXiv:2607. 13006v1 Announce Type: new Abstract: A growing family of indices scores how predictable a series is from its spectrum.
Raman spectroscopy enables non-destructive, label-free molecular characterization across materials science, biomedicine and process monitoring. Predictive Raman datasets often contain few labelled spectra and thousands of ordered wavenumbers, with informative variation within bands and across distant spectral regions.