Towards Computational Provenance: Carrying Causal-State Evidence in Generated Text
Quick summary
arXiv:2608.16868v1 Announce Type: cross Abstract: 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 discrete intermediate states, allowing different inter
Key takeaways
- arXiv:2608.16868v1 Announce Type: cross Abstract: 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.
Why it matters
“Towards Computational Provenance: Carrying Causal-State Evidence in Generated Text” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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