Menlo/Lucy
Lucy is a compact 1.7 billion parameter model developed by Menlo Research, built on Qwen3-1.7B, and optimized for agentic web search and lightweight browsing. It features machine-generated task vectors and pure reinforcement learning for efficient thinking processes. Designed to run on mobile devices, including CPU-only configurations, Lucy excels at strong agentic search using MCP-enabled tools and basic browsing capabilities, demonstrating higher accuracy than DeepSeek-v3 on SimpleQA.
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Overview
Lucy is a compact 1.7 billion parameter model developed by Menlo Research, specifically designed for agentic web search and lightweight browsing. Built upon the Qwen3-1.7B architecture, Lucy inherits advanced research capabilities while being highly optimized for efficient execution on mobile devices, even with CPU-only setups. Its efficiency is achieved through the use of machine-generated task vectors that refine thinking processes and smooth reward functions across various categories, utilizing pure reinforcement learning without supervised fine-tuning.
Key Capabilities
- Strong Agentic Search: Leverages MCP-enabled tools like Serper for robust Google Search integration.
- Basic Browsing: Supports web browsing through tools such as Crawl4AI and Serper.
- Mobile-Optimized: Engineered for lightweight operation, ensuring decent speed on CPU or mobile devices.
- Focused Reasoning: Employs machine-generated task vectors to optimize its thinking for search-related tasks.
Good for
- Developers building mobile applications requiring on-device agentic web search.
- Integrating lightweight browsing capabilities into resource-constrained environments.
- Use cases demanding efficient, focused reasoning for information retrieval tasks.
Evaluation Highlights
Lucy demonstrates impressive performance for its size, achieving higher accuracy than DeepSeek-v3 on the SimpleQA benchmark, using the same MCP methodology as other Menlo models like Jan-Nano.