mlsanityData to 14 Oct 2026

CLI-Anything: Towards Agent-Native Computer Use

arXiv 2606.03854 ↗First posted 2 Jun 2026cs.HCcited by 9

On the map: LLM reasoning → Multimodal reasoning

also in 1 more
Abstract (arXiv)

As large language models advance in reasoning and tool use capabilities, researchers increasingly seek to leverage them for computer use agents that can interact with existing software. The dominant approach develops GUI agents that control applications through visual interfaces: interpreting screenshots, locating UI elements, and executing mouse clicks to mimic human interaction. This GUI-centric paradigm fundamentally misaligns with agent capabilities. Current GUI agents struggle with brittle pixel-level interactions, timing dependencies, and coordinate-based actions that break with interface changes. They force agents to emulate human perceptual limitations rather than leverage their computational strengths in structured data processing and programmatic control. CLI-Anything argues for agent-native computer use design. Instead of forcing agents to navigate visual layouts, we create interfaces aligned with how agents naturally operate: through structured commands, explicit state representations, and deterministic feedback. We transform existing applications into command-line harnesses that preserve functionality while exposing machine-readable protocols optimized for AI-native interaction. This eliminates the lossy visual-to-computational translation that plagues GUI agents. Rather than building sophisticated screen readers and click simulators, we should redesign interaction paradigms around agent strengths: precise programmatic control and deterministic execution. We examine the methodology, architecture, evidence, and future directions for this agent-native transformation of computer use. We have built CLI-Hub as a comprehensive platform that operationalizes this agent-native computer use vision. The platform provides methodology, architecture, and infrastructure for this fundamental transformation of computer use.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Placed in Multimodal reasoning, one of 17 directions in LLM reasoning. Builds on 22 of its 31 references that are in the map. First posted on arXiv 2 Jun 2026.

Builds on

22 of 31 references.

References · 22

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  3. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  4. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  5. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,191
  6. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 2,006
  7. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,947
  8. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,737
  9. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,402
  10. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,332
  11. 2022WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agentscited by 1,251
  12. 2023Mind2Web: Towards a Generalist Agent for the Webcited by 1,242
10 more
  1. 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,195
  2. 2025DeepSeek-V3.2: Pushing the Frontier of Open Large Language Modelscited by 815
  3. 2023API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMscited by 591
  4. 2026Kimi K2.5: Visual Agentic Intelligencecited by 501
  5. 2024AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agentscited by 468
  6. 2023AppAgent: Multimodal Agents as Smartphone Userscited by 415
  7. 2024AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agentscited by 401
  8. 2024WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?cited by 363
  9. 2023Android in the Wild: A Large-Scale Dataset for Android Device Controlcited by 319
  10. 2026Qwen3-Coder-Next Technical Reportcited by 71

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 9.

9 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026GUI vs. CLI: Execution Bottlenecks in Screen-Only and Skill-Mediated Computer-Use Agents
  2. 2024GUI Agents: A Survey
  3. 2024Agent S: An Open Agentic Framework that Uses Computers Like a Human
  4. 2025Computer-Use Agents as Judges for Generative User Interface
  5. 2026Agent-Computer Observation Interfaces Enable Dynamic Computer Use
  6. 2025XBOUND: Exploring Capability Boundaries of Device-Control Agents at the State Level
  7. 2024Ponder & Press: Advancing Visual GUI Agent towards General Computer Control
  8. 2026Beyond the GUI Paradigm: Do Mobile Agents Need the Phone Screen?