arXiv (OAI Expanded)
open access
Efficient Auto-Interpretability of AI Models in Biology
Sparse autoencoders (SAEs), and other interpretability methods could turn AI models in Biology and other fields into engines of scientific discovery by explaining the superhuman capabilities of those models. However, a latent is only useful if we know three things: whether it is coherent, whether it
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
- Efficient Auto-Interpretability of AI Models in Biology#781049
- AutoResearchBench: Benchmarking AI Agents on Complex Scientific Literature Discovery#144352
- OpenScientist: evaluating an open agentic AI co-scientist to accelerate biomedical discovery#18838
- SCIRIGOR:Evaluating Open-Ended Scientific Analysis Beyond Final Scores#973792
- Quantitative Evidence Mining for Plausibility-Aware Biomedical AI#799597
Record · ID 779674
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