# ActionEngine: From Reactive to Programmatic GUI Agents via State Machine Memory

arXiv: https://arxiv.org/abs/2602.20502 · HTML: https://mlsanity.com/paper/2602.20502/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 17 of its 23 references that are in the map.
- First posted on arXiv 24 Feb 2026.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)
- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)
- LLM agents and tool use / [GUI and mobile agents](https://mlsanity.com/agents/gui-and-mobile-agents/)

## Builds on (most cited first, 17 of 17)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://mlsanity.com/paper/2307.13854/) (2023)
- [Mind2Web: Towards a Generalist Agent for the Web](https://mlsanity.com/paper/2306.06070/) (2023)
- [SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents](https://mlsanity.com/paper/2401.10935/) (2024)
- [A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis](https://mlsanity.com/paper/2307.12856/) (2023)
- [Synapse: Trajectory-as-Exemplar Prompting with Memory for Computer Control](https://mlsanity.com/paper/2306.07863/) (2023)
- [AutoDroid: LLM-powered Task Automation in Android](https://mlsanity.com/paper/2308.15272/) (2023)
- [Is Your LLM Secretly a World Model of the Internet? Model-Based Planning for Web Agents](https://mlsanity.com/paper/2411.06559/) (2024)
- [Web Agents with World Models: Learning and Leveraging Environment Dynamics in Web Navigation](https://mlsanity.com/paper/2410.13232/) (2024)
- [AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents](https://mlsanity.com/paper/2410.13825/) (2024)
- [AgentRewardBench: Evaluating Automatic Evaluations of Web Agent Trajectories](https://mlsanity.com/paper/2504.08942/) (2025)
- [GUI Agents: A Survey](https://mlsanity.com/paper/2412.13501/) (2024)
- [ScreenAgent: A Vision Language Model-driven Computer Control Agent](https://mlsanity.com/paper/2402.07945/) (2024)
- [Establishing Best Practices for Building Rigorous Agentic Benchmarks](https://mlsanity.com/paper/2507.02825/) (2025)
- [Symbiotic Cooperation for Web Agents: Harnessing Complementary Strengths of Large and Small LLMs](https://mlsanity.com/paper/2502.07942/) (2025)
- [PG-Agent: An Agent Powered by Page Graph](https://mlsanity.com/paper/2509.03536/) (2025)

## Built on by (most cited first, 1 of 1)

- [DeltaReplay: Task-Relative Memory Reuse for Mobile GUI Agents](https://mlsanity.com/paper/2610.11707/) (2026)

## Abstract (arXiv)

Many web agents operate through a reactive execution loop: they observe the current interface, reason about the next action, execute it, and repeat. This design incurs latency and cost that grow with the number of actions, while requiring agents to repeatedly rediscover how the same web application works. We present ActionEngine, a novel architecture that replaces step-by-step reasoning with programmatic execution using reusable knowledge of the application. A Crawling Agent explores the application offline and constructs an updatable state-machine memory that represents its GUI states, the operations available in each state, and the transitions between states. Unlike trajectory memory, this representation stores how the application works rather than solutions to individual tasks. At runtime, an Execution Agent uses this memory to synthesize a complete executable program in a single planning step, which is then executed deterministically without further planning calls. When the interface changes or the memory is incomplete, a reactive fallback repairs the failed action and updates the memory for future tasks. On 655 tasks across four WebArena domains, ActionEngine achieves a 91.2% success rate, outperforming the strongest reactive baseline, Claude Code, by 8.5 percentage points while reducing average task latency by 3.2x and cost by 8x.