BWhat changed
Trains linear hidden-state classifiers against binarized semantic-entropy targets, estimating uncertainty from one generation instead of repeated test-time sampling.
Scientific scope
Trains linear hidden-state classifiers against binarized semantic-entropy targets, estimating uncertainty from one generation instead of repeated test-time sampling. Requires hidden-state access and sampling-based training targets; low test-time overhead does not remove training cost. Before-generation and end-of-generation locations differ. Source v1 discrete-probability paragraph appears to divide sample counts by cluster count K rather than sample count N; implementation/formula reconciliation not independently replayed. No universal truth guarantee or biological application claim.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Requires hidden-state access and sampling-based training targets; low test-time overhead does not remove training cost. Before-generation and end-of-generation locations differ. Source v1 discrete-probability paragraph appears to divide sample counts by cluster count K rather than sample count N; implementation/formula reconciliation not independently replayed. No universal truth guarantee or biological application claim.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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