mlsanityData to 14 Oct 2026

UI-CUBE: Enterprise-Grade Computer Use Agent Benchmarking Beyond Task Accuracy to Operational Reliability

arXiv 2511.17131 ↗First posted 21 Nov 2025cs.SE cs.AIcited by 3

On the map: LLM agents and tool use → GUI and mobile agents

Abstract (arXiv)

While current Computer Use Agent (CUA) benchmarks measure task completion effectively, they provide limited assessment of enterprise deployment readiness, emphasizing functional correctness over the operational reliability required for production systems. We present UI-CUBE (UiPath Computer Use BEnchmark), a systematic benchmark comprising 226 tasks across two difficulty tiers designed to expose fundamental architectural limitations in current CUAs. Our evaluation covers simple UI interactions (136 tasks) and complex workflows including copy-paste tasks (50 tasks) and enterprise application scenarios (40 tasks), with systematic interface variation coverage, multi-resolution testing and automated validation of task success through the application state. Evaluation of five state-of-the-art models reveals a sharp capability cliff rather than gradual performance degradation. Simple UI interactions achieve 67-85% success rates (compared to 97.9% human performance), but complex workflows drop precipitously to 9-19%. Human evaluators with no prior application experience achieve only 61.2% on complex tasks despite near-perfect performance on simple tasks, establishing realistic performance ceilings. This discontinuous performance pattern -- where agents achieve 68-87% of human performance on simple tasks but only 15-32% on complex workflows -- indicates fundamental architectural limitations in memory management, hierarchical planning, and state coordination rather than incremental capability gaps addressable through better training or prompting. UI-CUBE functions as an enterprise-readiness diagnostic, revealing that while current CUAs can manipulate individual interface elements, they cannot yet function as reliable workflow automation tools. These findings provide architectural insights essential for developing production-ready CUAs capable of managing complex, multi-step enterprise processes.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in GUI and mobile agents, one of 16 directions in LLM agents and tool use. Builds on 20 of its 23 references that are in the map. First posted on arXiv 21 Nov 2025.

Builds on

20 of 23 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025SCUBA: Salesforce Computer Use Benchmark30 Sep
  2. 2025MMBench-GUI: Hierarchical Multi-Platform Evaluation Framework for GUI Agents25 Jul
  3. 2025OSWorld-Human: Benchmarking the Efficiency of Computer-Use Agents19 Jun
  4. 2025macOSWorld: A Multilingual Interactive Benchmark for GUI Agents4 Jun
  5. 2025UI-TARS: Pioneering Automated GUI Interaction with Native Agents21 Jan
  6. 2024Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale12 Sep

Other references · 14

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,796
  4. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  5. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 2,006
  6. 2023Mind2Web: Towards a Generalist Agent for the Webcited by 1,242
  7. 2024AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agentscited by 468
  8. 2024WebVoyager: Building an End-to-End Web Agent with Large Multimodal Modelscited by 438
  9. 2024Agent Workflow Memorycited by 383
  10. 2024TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Taskscited by 326
  11. 2018Reinforcement Learning on Web Interfaces Using Workflow-Guided Explorationcited by 284
  12. 2024OfficeBench: Benchmarking Language Agents across Multiple Applications for Office Automationcited by 70
2 more
  1. 2025CRMArena-Pro: Holistic Assessment of LLM Agents Across Diverse Business Scenarios and Interactionscited by 47
  2. 2025REAL: Benchmarking Autonomous Agents on Deterministic Simulations of Real Websitescited by 45

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026cua-speedrun: Standardized Benchmarking of the Speed of Computer-Use Agents
  2. 2026How Benchmarks Mis-Score Computer-Use Agents
  3. 2026ComponentBench: Diagnosing Component-Level Failures in Computer-Use Agents
  4. 2026OSWorld 2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks
  5. 2026WindowsWorld: A Process-Centric Benchmark of Autonomous GUI Agents in Professional Cross-Application Environments
  6. 2026MacAgentBench: Benchmarking AI Agents on Real-World macOS Desktop
  7. 2026Desktop-Delta Bench: Do Computer-Use Models Understand Desktop GUI Transitions?
  8. 2025OS-MAP: How Far Can Computer-Using Agents Go in Breadth and Depth?