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

---
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
