Towards Computational Provenance: Carrying Causal-State Evidence in Generated Text

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Towards Computational Provenance: Carrying Causal-State Evidence in Generated Text

A language model's output does not by itself provide verifiable evidence about the internal computation that produced it. We study computational provenance: whether generated text can carry detectable evidence of which causally relevant internal state occurred. We test a bounded form of this idea in two controlled architectures: a modular feed-forward neural network and a transformer-based model. Both architectures are trained on the same arithmetic task with a mandatory pathway through two disc

Source: ArXiv AI