agent/papers/items/2026-2606-25760-uncertainty-quantification-for-computer-use-agents-a-benchmark-across-vision-lan.md

Paper: Uncertainty Quantification for Computer-Use Agents: A Benchmark across Vision-Language Models and GUI Grounding Datasets

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type: paper title: "Uncertainty Quantification for Computer-Use Agents: A Benchmark across Vision-Language Models and GUI Grounding Datasets" authors: Divake Kumar, Sina Tayebati, Devashri Naik, Amanda Sofie Rios, Nilesh Ahuja, Omesh Tickoo, Ranganath Krishnan, Amit Ranjan Trivedi year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.25760 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-06-24 updated_at: 2026-06-24 status: queued relevance: high topics:

• agent-evaluation
• agent-safety
• computer-use
• rag methods:

benchmarks:

models:

datasets:

• cs.LG
• cs.AI
• cs.CL
• cs.CV related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 16 collection_queries: agent-evaluation, web-gui-agent

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: agent-evaluation, web-gui-agent
• inferred topics: agent-evaluation, agent-safety, computer-use, rag
• arXiv categories: cs.LG, cs.AI, cs.CL, cs.CV
• collection score: 16

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2606.25760