HAMON: Passive Optical Sequence Mixing for Long-Horizon Forecasting
Source published ·Modelwire updated
Original coverage: arXiv cs.LG ↗·How Modelwire adds context
The development
Researchers propose HAMON, a diffractive optical system that replaces learned digital temporal mixing with passive phase masks and free-space diffraction for time-series forecasting. The work challenges a core assumption in deep learning: that complex forecasting tasks require dense neural representations. By encoding historical sequences onto optical apertures and letting physics compute future values directly, HAMON exploits evidence that many benchmarks admit simple, approximately linear solutions. This substrate-level rethinking of forecasting hardware could reshape how practitioners think about the compute-complexity tradeoff and whether neural density is necessary or merely convenient for temporal prediction.
Modelwire’s AI-generated summary of coverage from arXiv cs.LG.
Modelwire analysis
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The deeper provocation in HAMON is not the hardware itself but the epistemological claim underneath it: if passive optics with no learned parameters can match trained neural nets on standard forecasting benchmarks, those benchmarks may be measuring something far simpler than the field has assumed, and the models winning on them may be winning for the wrong reasons.
This connects directly to the same-day arXiv paper on phase in neural representations ('The Importance of Phase in Neural Representations'). That work found that modern classifiers converge on Fourier phase as the dominant internal signal, essentially discovering that a physics-grounded transform already does most of the representational work. HAMON arrives at a structurally similar conclusion from the opposite direction: rather than finding phase inside a trained network, it starts with phase optics and asks whether the network was ever necessary. Together, the two papers suggest a quiet convergence around the idea that learned complexity in certain domains is redundant with physical or mathematical structure that was always available.
The critical test is whether HAMON's benchmark parity holds on genuinely non-linear, distribution-shifted forecasting tasks outside the standard ETT and Weather datasets. If it degrades sharply there while transformers do not, the result confirms benchmark simplicity rather than optical computing as a viable general forecasting substrate.
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Coverage behind this analysis
These archive entries ground the connection in our analysis. They are ordered by source publication date, with links to our coverage and the original sources.
·arXiv cs.LG
The Importance of Phase in Neural Representations: An Internal Oppenheim-Lim Test of Image Classifiers
Researchers tested whether trained image classifiers internally mirror a foundational property of human vision: that Fourier phase dominates object identity while magnitude is largely irrelevant. Using causal interventions across ViT-B/16, GFNet, and ResNet-50, they found that swapping phase between images causes predictions to follow the phase donor, while deleting magnitude information barely degrades accuracy. This…
MentionsHAMON · diffractive optical computing · time-series forecasting · transformers
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