• Computer Science > Artificial Intelligence [Submitted on 24 Feb 2026] Title:ActionEngine: From Reactive to Programmatic GUI Agents via State Machine Memory View PDF HTML (experimental)Abstract:Existing Graphical User Interface (GUI) agents operate through step-by-step calls to vision language models–taking a screenshot, reasoning about the next action, executing it, then repeating on the new page–resulting in high costs and latency that scale with the number of reasoning steps, and limited accuracy due to no persistent memory of previously visited pages. • We propose ActionEngine, a training-free framework that transitions from reactive execution to programmatic planning through a novel two-agent architecture: a Crawling Agent that constructs an updatable state-machine memory of the GUIs through offline exploration, and an Execution Agent that leverages this memory to synthesize complete, executable Python programs for online task execution. • To ensure robustness against evolving interfaces, execution failures trigger a vision-based re-grounding fallback that repairs the failed action and updates the memory. • This design drastically improves both efficiency and accuracy: on Reddit tasks from the WebArena benchmark, our agent achieves 95% task success with on average a single LLM call, compared to 66% for the strongest vision-only baseline, while reducing cost by 11.8x and end-to-end latency by 2x. • Together, these components yield scalable and reliable GUI interaction by combini

Article Summaries:

  • ActionEngine: Enhancing GUI Agents with State‑Machine Memory

Researchers introduced ActionEngine, a training‑free framework that shifts GUI agents from reactive step‑by‑step execution to programmatic planning. The system employs a two‑agent architecture: a Crawling Agent builds an updatable state‑machine memory of interface elements through offline exploration, while an Execution Agent uses this memory to generate complete Python scripts for online tasks. When execution fails, a vision‑based fallback repairs the action and updates the memory. On the WebArena Reddit benchmark, ActionEngine achieved 95 % task success-up from 66 % for the best vision‑only baseline-while cutting cost by 11.8× and halving end‑to‑end latency.

Sources: