OAMF: Optics-Accelerated Multimodal Learning with Markov Temporal Priors and Fourier Regularization
Bibliographic record
Abstract
OAMF (Optics-Accelerated Multimodal Learning with Markov Temporal Priors and Fourier Regularization), a hybrid optical-digital framework designed for sequence comprehension spanning visual, auditory, textual, and inertial sensor modalities. OAMF tackles three enduring issues in multimodal learning: elevated latency and energy consumption for high-resolution visual processing, degradation of temporal coherence due to asynchronous or absent modalities, and frequency-domain discrepancies that undermine cross-modal alignment. The visual stream undergoes preprocessing using an optical neural front-end that executes spatial Fourier transforms and convolutions in hardware, producing near-zero-latency feature maps that are digitized and minimally adjusted by compact residual blocks. A Markov state-space layer preserves temporal consistency in latent dynamics and facilitates smooth deterioration in the absence of inputs. Training incorporates Fourier-domain consistency losses to align spectra across modalities and a physics-in-the-loop regularizer that aligns measured optical transfer functions while adhering to phase quantization. OAMF is engineered to diminish end-to-end latency and energy consumption while enhancing accuracy and temporal stability across representative multimodal activities, including classification, retrieval, and question answering. Ablations delineate the roles of the optical front-end, Markov priors, and spectral regularization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".