src_fb50be84b835134c75
- Source-provided label
- Not reported
- Evidence class
- mentioned in report
- Confidence
- Not reported
- Observed
- Oct 07, 2026 04:00 UTC
- Retrieved
- Oct 08, 2026 03:43 UTC
Weight Oracles: Reading Neural Network Weights with Language Models
arXiv:2610.07334v1 Announce Type: cross Abstract: Interpretability methods for neural networks are predominantly reactive: they analyse activations produced during specific forward passes, requiring known inputs to find hidden capabilities such as backdoors. We propose Weight Oracles, fine-tuned language models that diagnose properties of a target network by reading its raw weights directly, witho
Reference details
- Source category
- Research & news
- Report title
Weight Oracles: Reading Neural Network Weights with Language Models- First reported
- Oct 07, 2026 04:00 UTC
- Last reported
- Oct 07, 2026 04:00 UTC
- Catalog updated
- Oct 08, 2026 03:43 UTC
- Record ID
07a7094086a00b3fb41af26781501b17577172987055fadaad056877b04c7d64
Published indicators
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Source observations
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Review source update dates and status
- src_fb50be84b835134c75Collection status unverifiedLast successful batch: Unverified
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