| --- |
| 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 |
|
|
| ```python |
| 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. |
|
|
