arXiv:2602.00887v2 Announce Type: replace Abstract: Most existing language model agentic systems today are built and optimized for large language models (e.g., GPT, Claude, Gemini) via API calls; while powerful, this approach faces several limitations including high token costs and privacy concerns for sensitive applications. We introduce EffGen, an open-source agentic framework optimized for small language models (SLMs) that enables effective, efficient, and secure local deployment. EffGen makes four major contributions: (1) Enhanced tool-calling with prompt optimization that compresses input
The rapid advancement and saturation of large language models (LLMs) have exposed their limitations, particularly concerning cost, privacy, and local deployment, creating an urgent need for more efficient alternatives.
This development signifies a crucial step towards democratizing advanced AI agent capabilities, making them accessible and secure for a wider range of applications and organizations beyond the current centralized API model.
The ability to run capable autonomous agents on smaller, local models shifts the paradigm from reliance on powerful, costly cloud-based LLMs towards a more distributed, private, and potentially customisable AI infrastructure.
- · Edge computing providers
- · Small and medium businesses
- · Developers of custom AI agents
- · Privacy-focused industries
- · Cloud-based LLM providers (to some extent)
- · Companies reliant solely on API-based AI services
Widespread adoption of small language models as autonomous agents, reducing deployment costs and improving data security for many applications.
Increased competition and innovation in the AI agent space, leading to more specialized and efficient local AI solutions for niche use cases.
Potential for sovereign AI initiatives to leverage such frameworks for domestic AI development, reducing dependency on foreign AI infrastructure.
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Read at arXiv cs.CL