AssayRouter: Historical Utility Priors for Frozen Molecular Predictor Routing
Quick summary
arXiv:2609.37285v1 Announce Type: new Abstract: Laboratories often face a new molecular assay with 16-64 labels and a bank of predictors whose training data and parameters are unavailable. The practical question is which frozen outputs to include in a small local model. AssayRouter treats completed assays as pseudo-targets and labels each candidate by its post-fit utility: the reduction in held-out discovery loss when the candidate is added to the local target predictor. A shared regressor learns to predict this utility from candidate behavior on the support set, without source identity; on a
Key takeaways
- arXiv:2609.37285v1 Announce Type: new Abstract: Laboratories often face a new molecular assay with 16-64 labels and a bank of predictors whose training data and parameters are unavailable.
- The practical question is which frozen outputs to include in a small local model.
- AssayRouter treats completed assays as pseudo-targets and labels each candidate by its post-fit utility: the reduction in held-out discovery loss when the candidate is added to the local target predictor.
Why it matters
The significance goes beyond a temporary access problem: “AssayRouter: Historical Utility Priors for Frozen Molecular Predictor Routing” exposes the operational cost of depending on one AI provider. Critical tasks need predefined fallback, queueing and human-continuation paths.
Member comments