arXiv:2606.04106v1 Announce Type: new Abstract: Foundation models achieve generalization through massive-scale training on diverse data, but have limitations with transfer to truly unseen domains without paired training data. We propose principle-driven foundation models that encode signal-theoretic principles (Fourier decomposition, energy conservation, symmetry) rather than learn untethered statistical correlations. We hypothesize that domains differ not in fundamental physics, but in learnable transformations in time, frequency, magnitude, or phase. Training exclusively on radio-frequency (
The increasing limitations of massive-scale statistical correlation in foundation models are driving research into new architectural approaches that integrate fundamental principles for enhanced generalisation.
This research proposes a foundational shift in AI model training, moving from pure statistical learning to principle-driven approaches, which could unlock significant advancements in AI capabilities and transferability to unseen domains.
AI model development may increasingly integrate signal-theoretic principles (physics, energy conservation, symmetry) into their architecture, potentially leading to more robust and less data-hungry AI systems.
- · AI research institutions
- · Hardware manufacturers for specialized AI
- · Sectors requiring high AI generalization
- · Companies reliant solely on massive, untethered statistical correlation models
AI models exhibit improved generalization across diverse and previously unseen domains with less paired training data.
Development of new AI architectures and specialized hardware capable of encoding and processing signal-theoretic principles accelerates.
The definition and capabilities of 'general artificial intelligence' evolve to include inherent understanding of physical principles rather than just patterns.
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Read at arXiv cs.LG