AndroidArena
Android environment for evaluating language-model-driven agents.
AI-assisted editorial review against a primary source. This is a catalog annotation, not an independent reproduction or endorsement.
What is this resource?
- Resource type
- Benchmarks
- Environment
- android
- Topics
- evaluation, mobile-navigation
- Record publication date
- Not recorded
- Evidence retrieved
- 2026-10-16T06:35:58Z
- Resource ID
- github:androidarenaagent/androidarena
What licenses are recorded?
Code, training data, weights, and documents can have different terms. A blank declaration means unknown, not unrestricted use.
- Code
- Not recorded
- Data
- Not recorded
- Document
- Not recorded
- Weights
- Not recorded
Evaluation protocol
- Focus
- multi-step Android tasks, cross-app workflows and instruction constraints
- Setting
- interactive
- Observations
- UI hierarchy text, screenshots, combined text/image observations
- Actions / outputs
- app launch/stop, click/double-click/long-click, text input, swipe/drag and Android navigation, finish
- Evaluation
- The released evaluator compares trajectories with reference actions using longest-common-subsequence-based task reward and completion. Its reversed redundancy metric is the reference/predicted action-length ratio (with 1e-6 denominator smoothing), multiplied by the LLM success flag. LLM success judgments and instruction-constraint checks are separate metrics. Full reference-dependent evaluation coverage cannot be assumed because complete annotated action sequences are not released.
Scale and counting units
Not recordedREADME states that task instructions are released but annotated action sequences, apart from the calendar example, will come later. Environment setup targets Android API 33 with uiautomator2 and manual Google-account setup. Public task YAML and evaluator code exist, but benchmark execution and full reference coverage were not verified. No task total is derived from app YAML file counts. Code and data licenses remain unknown.
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Explore related topics
Related resources
- documented by: Understanding the Weakness of Large Language Model Agents within a Complex Android Environment
Primary evidence and verification
Source retrieval is separate from experiment reproduction. Use the original publication or publisher page when citing claims about the resource.
- editorial primary review
Retrieved 2026-10-16T06:35:58Z. Supports: name, kind, summary, environments, topics, scope, relations, details.benchmark_protocol.
Content fingerprint
206bd0dc5220cf754cffc97767cce6a3cd27660ef050a569366bef2331241c60 - editorial primary review
Retrieved 2026-10-17T14:21:10Z. Supports: details.benchmark_protocol.
Content fingerprint
a1e7dc0c5facae030c2cf19aad6511996e7e1198d8d6619268d10c29a1b57bcf - editorial primary review
Retrieved 2026-10-17T14:14:05Z. Supports: details.benchmark_protocol.
Content fingerprint
edc03388b67143644db4a412e4c888f8c796068daff29f63615f0b6a3a04262a - editorial primary review
Retrieved 2026-10-17T14:24:52Z. Supports: details.benchmark_protocol.
Content fingerprint
cfa9d272c85398f54c18d6e7445d680009e8c16d128962235d899592d3094c2f - editorial primary review
Retrieved 2026-10-17T14:24:53Z. Supports: details.benchmark_protocol.
Content fingerprint
d25f2edf9202f59615341b62fc6046ac23a6f4cb072a6038ac562506cbdc891c - editorial primary review
Retrieved 2026-10-17T14:21:10Z. Supports: details.benchmark_protocol.
Content fingerprint
257c42e980a0b14bd74bfcc381a0ecbef2b538c0f1cc1a94d22925efe354fb8e
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