Repos: https://github.com/Forgis-Labs
- 5 papers into ICML Foundation models cracked text, images, audio, and video. They still can’t reason about time series, the modality that actually runs the physical world: vitals, power grids, markets, telemetry, machine signals. We’ve been building toward one solution: a world model for the physical world. Instead of a narrow model per problem, it learns the underlying dynamics of how complex systems behave over time, so it can reason about a signal it has never seen the same way it reasons about one it has. Our proving ground is the factory, but the idea generalizes to any sensor stream. It’s a single pipeline, published as four building blocks across 5 ICML 2026 workshops:
- FactoryNet: the data. A large-scale industrial sensor dataset for pretraining the full stack. (FMSD + AI4Physics)
- HEPA: the architecture. A foundation model for event prediction in time series, running on the edge. (FMSD, Spotlight)
- RASA: the graph. Shows transformers can reason over a system as a graph, where topology, not learned relation weights, drives multi-hop reasoning. (GFM)
- TEMPO: the language. Reads raw sensor streams and explains, in natural language, what a system is doing. (FMSD) Check it out and let us know if you have any technical questions! submitted by /u/Charming-Collar-3733
Originally posted by u/Charming-Collar-3733 on r/ArtificialInteligence
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