README.md

metadata

license: cc-by-4.0
language:
  - zh
  - en
size_categories:
  - 1K<n<10K
task_categories:
  - other
tags:
  - agent-evaluation
  - llm-as-judge
  - conformal-prediction
  - benchmark
  - mobile-agent
  - browser-agent
  - desktop-agent
pretty_name: GeneralAgentBench
configs:
  - config_name: default
    data_files:
      - split: test
        path: general_agent_bench.jsonl

GeneralAgentBench

GeneralAgentBench is a 1,400+ task benchmark for evaluating whether general-purpose AI agents have genuinely completed a task, spanning Mobile / Browser / Desktop environments. It is the evaluation resource accompanying an anonymous NeurIPS 2026 Evaluations & Datasets Track submission (Submission 173, AgentJudge). This release is fully anonymized for double-blind review.

Each task provides a natural-language instruction plus a list of verification checkpoints. This release is a task catalog: it does not include agent trajectories, independent human verdicts, checkpoint-level labels, or the experimental calibration/test assignment used for the paper's reported evaluator metrics.

Dataset at a glance

Property	Value
Total tasks	1,448
Environments	desktop_linux (1,023), mobile_android (403), unspecified (22)
Difficulty	easy (739), medium (512), hard (197)
Priority	P0 (487), P1 (693), P2 (197), P3 (71)
Avg. checkpoints / task	4.23 (every task has ≥1)
Tracks	skill (963), mobile (306), mobile_agent (97), general (82)
Language	Chinese instructions with some English; task targets reference public websites
License	CC BY 4.0

Note on anonymization: all references to specific commercial products, services, and internal codenames have been replaced with neutral placeholders (e.g. paymentapp, mapapp, cloudplatform, doccollab, teamapp, shoppingapp, skillkit). Task logic and difficulty are preserved. Publicly known third-party websites used as task targets are retained, as they do not identify the authors.

Data fields

Each line in general_agent_bench.jsonl is one task:

Field	Type	Description
uid	string	Globally unique id (<track>/<id>)
track	string	Source track: skill / mobile / mobile_agent / general
category	string	Top-level category (anonymized)
subcategory	string	Full category path (anonymized)
id	string	Original task id within its track
question	string	The task instruction given to the agent
reference_answer	string	Short reference / expected-outcome note (may be empty)
checkpoints	list[string]	Human-defined verification conditions for success
priority	string	P0–P3
difficulty	string	easy / medium / hard
environment	string	desktop_linux / mobile_android / empty
execution_mode	string	e.g. one_shot, multi-turn variants
timeout	int	Suggested wall-clock budget (seconds)
step_budget	int	Suggested max agent steps
token_budget	int	Suggested token budget
precondition_files	string (JSON)	Pre-staged input files under assets/ (JSON-encoded list)
tags	list[string]	Optional tags
extra_json	string (JSON)	Any additional track-specific fields, JSON-encoded

Pre-staged input files referenced by precondition_files are provided in the assets/ directory (synthetic CSVs, sample documents, generic test images — no personal data).

Usage

from datasets import load_dataset

ds = load_dataset("agentjudge-anon/GeneralAgentBench", split="test")
print(ds[0]["question"])
print(ds[0]["checkpoints"])

# nested fields are JSON-encoded strings:
import json
pre = json.loads(ds[0]["precondition_files"])
extra = json.loads(ds[0]["extra_json"])

Intended use and evidence scope

The released task catalog can be used to construct agent or evaluator studies, inspect task/checkpoint coverage, and test data-loading code. Reproducing the paper's human-agreement, error-rate, conformal-coverage, or routing results additionally requires trajectories, independent reference labels, label nonconformity scores, and the original experimental split assignment; those artifacts are not included in this release. Task availability must not be mistaken for evaluator validation.

Hugging Face exposes the complete JSONL as one test configuration. The file does not contain a split field and therefore is not a development/calibration/test split manifest.

Repository-evidenced composition

The 1,448 released rows are aggregated from four tracks and normalized to the schema documented above:

Track	# tasks	Origin	Environment
skill	963	Skill/capability regression suites	desktop_linux
mobile	306	Mobile UI task suite	mobile_android
mobile_agent	97	Mobile agent task suite	mobile_android
general	82	General cross-environment tasks	mixed

The file directly supports exact row, track, environment, difficulty, priority, and checkpoint counts; uid uniqueness; field-type checks; and file hashes.

The task-level priority field contains P0–P3; it is not a per-checkpoint MUST/NICE/OPT label. The difficulty field contains easy/medium/hard labels, but the component values V, K, and S are not distributed, so the log-linear difficulty formula described in the paper cannot be recomputed from this release alone.

Construction and annotation records

The accompanying paper describes a three-stage construction protocol. This public task-only release does not contain per-annotator votes, adjudication records, reference-agent dry-run traces, or contamination-comparison outputs. Consequently, annotator counts, agreement statistics, human-vs-LLM responsibility, and contamination rates are historical manuscript claims rather than measurements independently auditable from the released JSONL.

Release validation

RELEASE_MANIFEST.json records machine-checkable counts, field names, and limitations. checksums.sha256 contains SHA-256 hashes for the distributed task file, Croissant metadata, and manifest. These files validate the release contents; they do not reconstruct the paper's experimental splits or labels.

Limitations

• Instructions are predominantly in Chinese.
• Agent trajectories, screenshots, independent human labels, calibration scores, and original experimental split assignments are not released here.
• Anonymized product placeholders mean some instructions read generically (e.g. "the payment app") rather than naming a specific service.
• Task-level priority is not checkpoint-level priority annotation.
• The release does not by itself reproduce the paper's evaluator metrics.

Citation

Anonymous. AgentJudge: Conformal Evaluation of General-Purpose AI Agents via Multi-Modal Evidence Fusion, Six-Dimensional Scoring, and Dual-Track Adaptive Routing. NeurIPS 2026 Evaluations & Datasets Track, Submission 173 (under review). Citation details will be completed upon acceptance.