arXiv (All)
open access
Neural Architecture Discovery via Autonomous Evolution
Recent progress in LLM agents has advanced the prospect of autonomous research. Yet whether AI can complete difficult long-horizon tasks, especially those that advance AI research itself, remains largely unexplored. We present ASI-Arch, a system for AI-driven AI research that autonomously conducts n
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
- Paton-Native AI Architectures: Admissibility-Driven Learning Systems within the Paton Framework#73396
- AI CFD Scientist: Toward Open-Ended Computational Fluid Dynamics Discovery with Physics-Aware AI Agents#168352
- EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery#151799
- AutoSDT: Scaling Data-Driven Discovery Tasks Toward Open Co-Scientists#1118396
- NS-Copilot: An LLM-Driven Agent System for Autonomous Neuroscience Analysis#999868
Record · ID 1248274
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