arXiv:2606.12674v1 Announce Type: new Abstract: Compact language models (LMs) reduce cost, latency, and deployment risk for tool agents. Yet MCP-style tool use requires more than isolated function calling: an agent must discover tools from live catalogs, satisfy schemas, preserve dependencies across intermediate outputs, and ground final responses in executed evidence. Small planners often generate plausible workflow graphs that fail under tool resolution, parameter validation, dependency tracking, or execution. We argue that this failure mode is poorly handled by small-corpus distillation. A
The paper addresses current limitations in small language model (LLM) agentic workflows by proposing a novel inference-time evolution mechanism, a timely advancement as compact LLM adoption accelerates.
This development could significantly enhance the reliability and efficiency of AI agents, making sophisticated tool use more accessible and robust even with smaller, more cost-effective models.
The ability of compact LMs to reliably execute complex, multi-step workflows, resolving issues like tool discovery, schema validation, and dependency tracking, fundamentally improving their practical utility.
- · AI Agent developers
- · SaaS companies leveraging AI
- · Industries seeking cost-effective automation
- · Companies relying on large, expensive LMs for agentic tasks
Compact AI agents become far more effective at complex, real-world tasks, reducing operational costs for many enterprises.
Increased adoption of AI agents across various sectors leads to accelerated automation of white-collar workflows and potentially new business models.
The enhanced practicality of smaller LMs could democratize advanced AI agent capabilities, fostering innovation and competition in the AI ecosystem.
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Read at arXiv cs.AI