NanoResearch: A Multi-Agent Framework for Personalized Research Automation
A new paper on arXiv introduces NanoResearch, a multi-agent framework designed to personalize research automation. The authors argue that current LLM-powered systems produce uniform outputs that fail to accommodate individual researchers' resource configurations, methodological preferences, and output formats. NanoResearch addresses this through tri-level co-evolution: a skill bank distills recurring operations into compact procedures, enabling accumulation of reusable procedural knowledge across projects, retention of user-specific experience across sessions, and internalization of implicit preferences. The framework aims to make research automation genuinely usable by adapting to each user's unique needs.
Key facts
- NanoResearch is a multi-agent framework for personalized research automation.
- Current LLM-powered systems produce uniform outputs that under-serve individual users.
- Personalization requires accumulating procedural knowledge, retaining user experience, and internalizing implicit preferences.
- The framework uses tri-level co-evolution with a skill bank.
- The paper is published on arXiv with ID 2605.10813.
- The approach targets researchers with different resource configurations and preferences.
- The system aims to automate the full research pipeline from ideation to paper writing.
- The paper was announced as new on arXiv.
Entities
Institutions
- arXiv