Towards Computational Provenance: Carrying Causal-State Evidence in Generated Text
The study tests if generated text can carry verifiable evidence of which internal state caused a result during model computation, even when the final output is identical across different computational paths. Researchers used controlled arithmetic tasks and cryptographic receipts to link specific intermediate states to word choices in generated text, proving that causal information can be embedded in language.