arXiv (All)
open access
SAEScientist-Bench: Can AI Agents Conduct Autonomous SAE Interpretability Research?
While research on recursive self-improvement (RSI) has predominantly automated model training pipelines, reliable autonomous development demands a missing pillar: post-hoc monitoring and auditing to understand what models learn and ensure safe alignment. Mechanistic interpretability tools are essent
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
- MILO: Automated Harness Discovery via Orchestrated Multi-Agent Evolution#1142950
- HarnessEvolve: Learning from Reference Trajectories for Reliable Agent Self-Evolution#820834
- Evolutionary Safety of Recursive Self-Improving AI: Taxonomy, Risk Discovery, and Evaluation#1105744
- Execution-bound advisory automation for agentic AI: a reproducible AIBOM-driven CSAF-VEX framework.#239950
- SWE-bench Science: Can Coding Agents Resolve Engineering Tasks in Science?#820620
Record · ID 999896
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