arXiv (All)
open access
ReCreate: Reasoning and Creating Domain Agents Driven by Experience
Large Language Model (LLM) agents are reshaping the industrial landscape. However, most practical agents still rely on human-designed scaffolds, making their creation and adaptation across diverse domains labor-intensive. This situation poses a central question: can we automatically create and evolv
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
- Towards a Systems Foundation for Agentic Skills: Architecture, Lifecycle, and Security#798236
- Towards a Systems Foundation for Agentic Skills: Architecture, Lifecycle, and Security#796823
- From Traces to Agentic Worlds: Agentic Language World Models for Interactive Environment Simulation#1202519
- Composing Task-specific Agent Harnesses at Test Time with Reusable Primitives#1143513
- Improving Environment-Agnostic Coding Agents: The Role of Scaffolding#1357944
Record · ID 1342389
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