{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:R3IUAPTGAQ7QUMN2CU4QOUIV4S","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"73dd4d14ae3d01ac795555185351db4c4795ff3c775b8761a59e0693d96e0f5e","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-13T00:44:09Z","title_canon_sha256":"44a7f5f981d0f95b7127ee4198c83b4b214772c9524f9c46a9a3a8fd3f2a109c"},"schema_version":"1.0","source":{"id":"2605.12845","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.12845","created_at":"2026-05-18T03:09:11Z"},{"alias_kind":"arxiv_version","alias_value":"2605.12845v1","created_at":"2026-05-18T03:09:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.12845","created_at":"2026-05-18T03:09:11Z"},{"alias_kind":"pith_short_12","alias_value":"R3IUAPTGAQ7Q","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"R3IUAPTGAQ7QUMN2","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"R3IUAPTG","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:5c50a9fb72aba32dfd58e26907bddc7bc4a08ae96fb730dcd50cd36bbccaa3aa","target":"graph","created_at":"2026-05-18T03:09:11Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"AssemblyDyno outperforms prior works in both assembly pose estimation and trajectory feasibility, where the latter is evaluated by our physics-based simulations."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The synthetic dataset and physics simulations sufficiently capture the shape complexities and physical constraints of real industrial assembly tasks."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"AssemblyBench dataset and AssemblyDyno transformer model enable physics-aware prediction of assembly sequences and trajectories for complex industrial objects from multimodal instructions and 3D shapes."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"AssemblyBench dataset and AssemblyDyno model advance physics-aware assembly planning for industrial objects."}],"snapshot_sha256":"8275c42eec6565200a058571bdaab48ea4123def78bd96115a3472208c21a75c"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"e8f580d1f748c6c7d6292abf42d8dec8525bd5e3f5441ae9d0a35fa09bc7d7d2"},"paper":{"abstract_excerpt":"Assembling objects from parts requires understanding multimodal instructions, linking them to 3D components, and predicting physically plausible 6-DoF motions for each assembly step. Existing datasets focus on simplified scenarios, overlooking shape complexities and assembly trajectories in industrial assemblies. We introduce AssemblyBench, a synthetic dataset of 2,789 industrial objects with multimodal instruction manuals, corresponding 3D part models, and part assembly trajectories. We also propose a transformer-based model, AssemblyDyno, which uses the instructional manual and the 3D shape ","authors_text":"Anoop Cherian, Bernhard Egger, Danrui Li, Jiahao Zhang, Moitreya Chatterjee, Suhas Lohit, Tim K. Marks","cross_cats":["cs.AI"],"headline":"AssemblyBench dataset and AssemblyDyno model advance physics-aware assembly planning for industrial objects.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-13T00:44:09Z","title":"AssemblyBench: Physics-Aware Assembly of Complex Industrial Objects"},"references":{"count":48,"internal_anchors":4,"resolved_work":48,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"The ikea asm dataset: Understanding people assem- bling furniture through actions, objects and pose","work_id":"e9284f93-742f-4aee-a531-9298e546d4c8","year":2021},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Qi Charles, Hao Su, Mo Kaichun, and Leonidas J","work_id":"1b9ea2e7-59d1-46fd-abc3-d0377d84f449","year":2017},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"A point set generation network for 3D object reconstruction from a sin- gle image","work_id":"ee80af9d-b2a5-4b12-a189-ed86388c32d1","year":2017},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"R. Hadsell, S. Chopra, and Y . LeCun. Dimensionality reduc- tion by learning an invariant mapping. InIEEE Computer Society Conference on Computer Vision and Pattern Recog- nition (CVPR), pages 1735–17","work_id":"8f49669a-d31d-4bbc-8ad7-0fd63686d4fd","year":2006},{"cited_arxiv_id":"2509.02949","doi":"","is_internal_anchor":true,"ref_index":5,"title":"ProMQA-Assembly: Multimodal Procedural QA Dataset on Assembly","work_id":"2f39c333-ec37-43c2-8462-733d3943e109","year":null}],"snapshot_sha256":"e4347d286d4707ad44098b6dd5c089f9d58a4e27bc8d071ef36a62b422b95fdf"},"source":{"id":"2605.12845","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-14T20:37:05.590411Z","id":"147799eb-2964-4020-b824-67d8c271ed76","model_set":{"reader":"grok-4.3"},"one_line_summary":"AssemblyBench dataset and AssemblyDyno transformer model enable physics-aware prediction of assembly sequences and trajectories for complex industrial objects from multimodal instructions and 3D shapes.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"AssemblyBench dataset and AssemblyDyno model advance physics-aware assembly planning for industrial objects.","strongest_claim":"AssemblyDyno outperforms prior works in both assembly pose estimation and trajectory feasibility, where the latter is evaluated by our physics-based simulations.","weakest_assumption":"The synthetic dataset and physics simulations sufficiently capture the shape complexities and physical constraints of real industrial assembly tasks."}},"verdict_id":"147799eb-2964-4020-b824-67d8c271ed76"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:0ea73ec95c7671313265508b1fe311455e9a646c4d296d58377fd20be10c5ea2","target":"record","created_at":"2026-05-18T03:09:11Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"73dd4d14ae3d01ac795555185351db4c4795ff3c775b8761a59e0693d96e0f5e","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-13T00:44:09Z","title_canon_sha256":"44a7f5f981d0f95b7127ee4198c83b4b214772c9524f9c46a9a3a8fd3f2a109c"},"schema_version":"1.0","source":{"id":"2605.12845","kind":"arxiv","version":1}},"canonical_sha256":"8ed1403e66043f0a31ba1539075115e49b4e169a555a97d7f5194f8402d124da","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8ed1403e66043f0a31ba1539075115e49b4e169a555a97d7f5194f8402d124da","first_computed_at":"2026-05-18T03:09:11.908830Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:09:11.908830Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gXDhGIxvrod2gQXoAs75bwH7GYpepG9E7Zu7AhJRuKL4K43wFJi3O5YCvK2BZLjD+EcpWusyx9QTjnsufqLwAg==","signature_status":"signed_v1","signed_at":"2026-05-18T03:09:11.909489Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.12845","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0ea73ec95c7671313265508b1fe311455e9a646c4d296d58377fd20be10c5ea2","sha256:5c50a9fb72aba32dfd58e26907bddc7bc4a08ae96fb730dcd50cd36bbccaa3aa"],"state_sha256":"2d0f900809580e6df31bae0fa092d11bdc3095cc485c5e3eb90430dfa513b185"}