Yandori
Multi-Scale Temporal Prediction for Information Stream Compression
Yandori is a research project that introduces a neural architecture for compressing continuous information streams (e.g., news articles) into fixed-size world state embeddings. It uses multi-scale temporal prediction to learn which content is predictively relevant, and maintains a constant memory footprint regardless of deployment duration. The system processes articles from RSS/Atom feeds on a 1‑minute update cycle, using a transformer encoder, importance‑weighted aggregation, hierarchical memory buffers, and multi‑scale temporal predictors. The project is described in an arXiv preprint and is not presented as a commercial product.
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