From Imperative to Declarative: Towards LLM-friendly OS Interfaces for Boosted Computer-Use Agents

Computer-use agents (CUAs) powered by large language models (LLMs) have emerged as a promising approach to automating computer tasks, yet they struggle with the existing human-oriented OS interfaces-graphical user interfaces (GUIs). GUIs force LLMs to decompose high-level goals into lengthy, error-prone sequences of fine-grained actions, resulting in low success rates and an excessive number of LLM calls.

We propose Declarative Model Interface (DMI), an abstraction that transforms existing GUIs into three declarative primitives: access, state, and observation, thereby providing novel OS interfaces tailored for LLM agents. Our key idea is policy-mechanism separation: LLMs focus on high-level semantic planning (policy) while DMI handles low-level navigation and interaction (mechanism). DMI does not require modifying the application source code or relying on application programming interfaces (APIs).

We evaluate DMI with Microsoft Office Suite (Word, Pow-erPoint, Excel) on Windows. Integrating DMI into a leading GUI-based agent baseline improves task success rates by 67% and reduces interaction steps by 43.5%. Notably, DMI completes over 61% of successful tasks with a single LLM call.