Discovering Physical Representation Languages
Quick summary
arXiv:2609.23381v1 Announce Type: cross Abstract: Before a machine can discover a physical law, it must discover what its measurements are: which observations live on cells, which are intensive or extensive, which sectors are dual, and which distinctions are merely gauge. We introduce physical representation-language discovery, the problem of recovering this hidden ontology directly from anonymous controlled experiments. We give an identifiability theory and constructive polynomial-time procedure that recovers a carrier and differential sequence, measurement types and orientation twist, noninv
Key takeaways
- arXiv:2609.23381v1 Announce Type: cross Abstract: Before a machine can discover a physical law, it must discover what its measurements are: which observations live on cells, which are intensive or extensive, which sectors are dual, and which distinctions are merely gauge.
- We introduce physical representation-language discovery, the problem of recovering this hidden ontology directly from anonymous controlled experiments.
- We give an identifiability theory and constructive polynomial-time procedure that recovers a carrier and differential sequence, measurement types and orientation twist, noninv
Why it matters
The significance is not only the legal text but how it changes product design. Decisions around “Discovering Physical Representation Languages” may reshape data collection, model training, output accountability and market access.
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