Representation as a Bottleneck for Mechanistic Interpretability: The Manifestation Unit Protocol
arXiv 2607.00089•2d27359b994c948cfa62cb1a66d964f8bfc52a17c3821dd0d3d58e8396e60a54
GPU-sparse-solversPEFTautoMLcalibrationdifferential-privacy-riskfractional-fourierhealthcare-privacyhyperparameter-optimizationmechanistic-interpretabilitymodel-adaptationmodel-extractionmodel-interpretabilityphishing-detectionprivacyprobabilistic-forecastingsecurity-classificationsemi-supervised-learningsparse-optimizationsynthetic-datathermodynamic-computingtransformer-architecture
Paper metadata
- arXiv ID
- 2607.00089
- Version
- Not specified by this published record
- Category
- Computer Science — Machine Learning (cs.LG)
The PDF link points to arxiv.org. Baitaphish does not expose a private stored PDF.
Evidence and limitations
- Source ID
- arxiv_cs_lg
- Record identifier
- 2d27359b994c948cfa62cb1a66d964f8bfc52a17c3821dd0d3d58e8396e60a54
- Enrichment time
- 2026-07-02T08:52:14Z
- AI-assisted enrichment
- Yes
This record may overlap with other records. Its enrichment can be incomplete or wrong, and machine assistance was used. Validate consequential decisions against the linked source and your own environment.