--- pretty_name: AssemblyWorldBench license: other license_name: per-source-terms license_link: LICENSE.md tags: - 3d - assembly - agents - benchmark - webmcp size_categories: - n<1K configs: - config_name: default data_files: - split: test path: index/samples.parquet extra_gated_prompt: >- AssemblyWorldBench embeds part geometry, manual pages and reference images from PartNet (via Manual-PA), IKEA-Manual, AssemblyBench (Fusion 360 Gallery) and Fantastic Breaks. These assets keep their original rights. Access is granted for non-commercial research only, and you must comply with the terms of every source dataset. extra_gated_fields: Name: text Affiliation: text I will use this dataset for non-commercial research only: checkbox I will comply with the terms of each source dataset listed in the card: checkbox --- # AssemblyWorldBench AssemblyWorldBench is the benchmark of *AssemblyWorld: Rethinking 3D Assembly with General-Purpose Agents*. An agent receives the rigid parts of one object, scattered in an interactive 3D scene, and must assemble them by inspecting rendered views and manipulating parts through the environment's WebMCP tools, optionally guided by a reference image or a full assembly manual. The finished scene is scored geometrically against the ground-truth assembly. The benchmark has **100 evaluations over 80 shapes** from four sources, organized into five blocks of 20. Agent results for the systems in the paper are released separately in [`AssemblyWorld/AssemblyWorldBench-Results`](https://huggingface.co/datasets/AssemblyWorld/AssemblyWorldBench-Results). | Block | Source dataset | Reference given to the agent | Objects | Parts per object | |---|---|---|---|---| | `partnet-none` | PartNet (Manual-PA split) | none | 7 chairs, 7 tables, 6 storage | 5–15 | | `partnet-final-image` | PartNet (Manual-PA split) | final assembly image | same 20 objects | 5–15 | | `ikea-manualbook` | IKEA-Manual | full manual, original page order | 20 furniture objects | 3–19 | | `assemblybench-manualbook` | AssemblyBench | full step diagrams, in order | 20 industrial objects | 3–18 | | `fantastic-breaks-none` | Fantastic Breaks | none | 20 broken objects | 2 | Objects were selected from task properties only (category and part-count bands, fixed seed), never from model results. The selection inputs are in `selection/`. ## Layout ``` benchmark.json frozen definition: blocks, samples, protocol, aggregation, provenance launch-commands.txt one run command per block index/samples.parquet one row per evaluation (block, source, sample, category, band, parts, paths) selection/ spec, manifest, exclusions, exclusion candidates, overrides /task.txt the task text of the block /// config.json preparation identity and per-sample initial episode records .episode.zip initial episode: the only input an agent receives cache//evaluation.json ground truth for scoring: part point clouds, GT poses, equivalence groups cache//reference// reference pages attached to the prompt (final-image, manualbook) ``` An initial episode is a [3DWebAgent](https://github.com/AssemblyWorld/3DWebAgent) episode archive (`3dwebagent-episode` v1, runtime contract `3dwebagent-runtime-1`): MuJoCo world, part meshes and the scattered initial state. The `cache/` directories hold ground truth and must never be given to an agent; the runner reads only the reference pages from them, and the evaluator reads `evaluation.json`. ## Running and scoring The environment is served at `https://assemblyworld.github.io/3DWebAgent/`. Runs and scoring use [`assembly-world-agent`](https://github.com/AssemblyWorld/assembly-world-agent): ```sh # after downloading this repository to data/assemblyworldbench/ uv run --locked --extra episodes --group browser assembly-world-agent run \ --dataset partnet-manualpa --config-id prep-v1-s0-i0-987553cbcd58 \ --data data/assemblyworldbench/partnet-none --reference-mode none \ --prompt-file data/assemblyworldbench/partnet-none/task.txt \ --agent codex --model MODEL --headless # ... one command per block, see launch-commands.txt uv run --locked --extra episodes python scripts/evaluate_run.py \ --benchmark data/assemblyworldbench/benchmark.json ``` Scoring uses protocol `assembly-evaluation-v2`: the final assembly is aligned to the ground truth with one global SE(3) transform, and parts are matched to ground-truth parts within geometry equivalence groups. **SCD** is the whole-shape Chamfer distance (sum of bidirectional mean squared nearest-neighbor distances, ×1000); **PA** is the fraction of parts whose Chamfer distance to their matched part is at most 0.01; **SR** is 1 when every part is correct. The overall score is the mean over sources of the mean over that source's blocks; a sample without a scored episode counts SR = PA = 0, and a model refusal counts as a failed evaluation. ## Sources and terms Every block pins a source dataset revision in `benchmark.json`. The initial episodes embed the source part geometry, and the reference pages are the source images. These assets keep their original rights; see `LICENSE.md` and the source cards. | Source | Hugging Face dataset | Terms of the underlying assets | |---|---|---| | PartNet (Manual-PA) | [`AssemblyWorld/partnet-manualpa`](https://huggingface.co/datasets/AssemblyWorld/partnet-manualpa) | PartNet and ImagePA / Manual-PA source rights; no blanket redistribution grant verified | | IKEA-Manual | [`AssemblyWorld/ikea-manual`](https://huggingface.co/datasets/AssemblyWorld/ikea-manual) | annotations CC BY-NC-SA 4.0; manuals and meshes keep their creators' rights | | AssemblyBench | [`AssemblyWorld/assemblybench`](https://huggingface.co/datasets/AssemblyWorld/assemblybench) | modifications CC BY-SA 4.0 (MERL); Fusion 360 Gallery assets non-commercial research only under the Fusion 360 Gallery Dataset License | | Fantastic Breaks | [`AssemblyWorld/fantastic-breaks`](https://huggingface.co/datasets/AssemblyWorld/fantastic-breaks) | no dataset license specified; source assets keep their original rights | Please also cite the source datasets: Mo et al., *PartNet*, 2019; Zhang et al., *Manual-PA*, 2025; Wang et al., *IKEA-Manual*, 2022; Li et al., *AssemblyBench*, 2026; Lamb et al., *Fantastic Breaks*, 2023.