Wavelet as Tokenizer: Preliminary Results on a Shared Wavelet Token Schema for Natural Signals
arXiv:2606.02631v1 Announce Type: cross Abstract: This paper studies whether audio, images, and video can share a common wavelet token schema rather than relying on separate modality-specific latent grids. It introduces a preliminary continuous-token model built around a one-level Haar DWT/IDWT frontend, a shared coefficient-token layout, optional structural metadata, lightweight modality value adapters, and a shared token-wise encoder-decoder trunk. On Speech Commands, EuroSAT RGB, and DAVIS 2017 data, a dense shared model reaches 39.92 dB audio, 29.37 dB image, and 23.93 dB video PSNR. A mat
The proliferation of various data modalities and the drive for more efficient, unified AI models across these formats necessitate research into shared representational schemas.
A unified token schema for natural signals could pave the way for more general artificial intelligence, significantly reducing computational overhead and simplifying model architectures across diverse applications.
Current modality-specific latent grids and model architectures would gradually be replaced by more generalized systems, improving interoperability and reducing the need for specialized design per data type.
- · AI model developers
- · Multimodal AI platforms
- · Cloud computing providers
- · Hardware manufacturers for AI
- · Developers of highly specialized, single-modality AI solutions
- · Legacy AI infrastructure focused on siloed data types
Further research and development in unified tokenization and multi-modal foundational models will accelerate.
Reduced complexity and improved efficiency could make advanced AI training more accessible, potentially democratizing aspects of AI development.
The development of truly general-purpose AI agents capable of seamlessly understanding and generating across all data types could be significantly accelerated.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG