arXiv (All)
open access
Can Circuit Alignment Predict OOD Generalization?
Can out-of-distribution (OOD) generalization be predicted from a trained model's weights alone, without any target-domain data? Existing representational similarity metrics (CKA, SVCCA, RSA) compare activations rather than forecast generalization. We show they are provably insensitive to structural
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Are Generative Models Structural Representations?#92464
- Empowering federated learning for robust compound-protein interaction prediction across heterogeneous cross-pharma domains#625648
- Empowering federated learning for robust compound-protein interaction prediction across heterogeneous cross-pharma domains#625625
- Distance generalization in transformers: why bother with positional encoding?#999589
- Marginally Useful: An Information-Gap Identity in Conformal Prediction#652429
Record · ID 1115952
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive