aiqbench analysis
The paper argues that standard sequence models, which encode position by token index, are ill-suited to physical signals like astronomical spectra recorded by different instruments. Its method uses continuous coordinates, a logarithmic wavelength basis, and a Fourier decoder, and it reports state-of-the-art results with 12.2M parameters. These are abstract-level claims, so readers should check the benchmarks and baselines before treating the parameter-efficiency result as general. The coordinate-encoding idea may transfer to other irregularly sampled scientific data.
OmniSpectra: A Unified Foundation Model for Universal Spectra Representation Learning
arXiv:2601.15351v2 Announce Type: replace-cross Abstract: Standard sequence models encode position by token rank rather than physical coordinate, typically forcing practitioners to destructively resample continuous physical signals such as spectra observed across heterogeneous instruments. We identify this coordinate mismatch as a structural limitation in cross-instrument representation learning, and propose OmniSpectra, a wavelength-native foundation model for spectral representation learning: a continuous-coordinate masked autoencoder that discards the integer pixel indexing of sequence modeling. Mapping physical coordinates to a logarithmic basis turns the multiplicative scaling induced by cosmological redshift into a uniform additive shift. In continuous rotary position embeddings, this shared shift cancels from relative token distances rather than producing grid-dependent positional distortion. We further counter the spectral bias inherent to implicit neural decoders with a physics-grounded, resolution-gated Fourier decoder that provides anti-aliasing. Pretrained on eight surveys and benchmarked against seven baselines, our model reaches state-of-the-art performance with only 12.2M parameters while outperforming substantially larger models, indicating that encoding physical structure can substitute for parameter scale.
Abstract:Standard sequence models encode position by token rank rather than physical coordinate, typically forcing practitioners to destructively resample continuous physical signals such as spectra observed across heterogeneous instruments. We identify this coordinate mismatch as a structural limitation in cross-instrument representation learning, and propose OmniSpectra, a wavelength-native foundation model for spectral representation learning: a continuous-coordinate masked autoencoder that discards the integer pixel indexing of sequence modeling. Mapping physical coordinates to a logarithmic basis turns the multiplicative scaling induced by cosmological redshift into a uniform additive shift. In continuous rotary position embeddings, this shared shift cancels from relative token distances rather than producing grid-dependent positional distortion. We further counter the spectral bias inherent to implicit neural decoders with a physics-grounded, resolution-gated Fourier decoder that provides anti-aliasing. Pretrained on eight surveys and benchmarked against seven baselines, our model reaches state-of-the-art performance with only 12.2M parameters while outperforming substantially larger models, indicating that encoding physical structure can substitute for parameter scale.
Instrumentation and Methods for Astrophysics (astro-ph.IM); Artificial Intelligence (cs.AI)
arXiv:2601.15351 [astro-ph.IM]
(or arXiv:2601.15351v2 [astro-ph.IM] for this version)
https://doi.org/10.48550/arXiv.2601.15351 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 255 words