{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:NAGQUN6DETSZH7X6TVXXVS6AMJ","short_pith_number":"pith:NAGQUN6D","canonical_record":{"source":{"id":"2312.10763","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-17T16:53:30Z","cross_cats_sorted":[],"title_canon_sha256":"02c7f2b25dd49789796cbe9f0e54c460e6416efebff150437bc0115b318461fc","abstract_canon_sha256":"6fef97adba673ea4d0966c83b7fbd6281db1edfb43c221957026290cd614d03e"},"schema_version":"1.0"},"canonical_sha256":"680d0a37c324e593fefe9d6f7acbc0627f69bc8e3080e20212bb0989635cc6c1","source":{"kind":"arxiv","id":"2312.10763","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.10763","created_at":"2026-07-05T07:25:11Z"},{"alias_kind":"arxiv_version","alias_value":"2312.10763v1","created_at":"2026-07-05T07:25:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.10763","created_at":"2026-07-05T07:25:11Z"},{"alias_kind":"pith_short_12","alias_value":"NAGQUN6DETSZ","created_at":"2026-07-05T07:25:11Z"},{"alias_kind":"pith_short_16","alias_value":"NAGQUN6DETSZH7X6","created_at":"2026-07-05T07:25:11Z"},{"alias_kind":"pith_short_8","alias_value":"NAGQUN6D","created_at":"2026-07-05T07:25:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:NAGQUN6DETSZH7X6TVXXVS6AMJ","target":"record","payload":{"canonical_record":{"source":{"id":"2312.10763","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-17T16:53:30Z","cross_cats_sorted":[],"title_canon_sha256":"02c7f2b25dd49789796cbe9f0e54c460e6416efebff150437bc0115b318461fc","abstract_canon_sha256":"6fef97adba673ea4d0966c83b7fbd6281db1edfb43c221957026290cd614d03e"},"schema_version":"1.0"},"canonical_sha256":"680d0a37c324e593fefe9d6f7acbc0627f69bc8e3080e20212bb0989635cc6c1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:25:11.934906Z","signature_b64":"Sef2lE1t1Ygo7bRiYzqobXQrkNcYScL9oIyItKqHTPKRb5H3P8ZZ5T2uUiIY8ejJM75kylOyw10T4X2KD9yGAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"680d0a37c324e593fefe9d6f7acbc0627f69bc8e3080e20212bb0989635cc6c1","last_reissued_at":"2026-07-05T07:25:11.934379Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:25:11.934379Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2312.10763","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:25:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Sw2ys/DFeZPmshiC2iBIMIdsQz3zn7+89kHkEbokhIw9QYchLx08Po5nvSmC996GpevahRgdRlg+YVx9JodQCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T03:54:36.868034Z"},"content_sha256":"f62d9c8554f6074b46580072f15ac09aa6f865f2d56c4640282575c6f52754c0","schema_version":"1.0","event_id":"sha256:f62d9c8554f6074b46580072f15ac09aa6f865f2d56c4640282575c6f52754c0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:NAGQUN6DETSZH7X6TVXXVS6AMJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"M3DBench: Let's Instruct Large Models with Multi-modal 3D Prompts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chi Zhang, Fukun Yin, Gang Yu, Hongyuan Zhu, Mingsheng Li, Sijin Chen, Tao Chen, Xin Chen","submitted_at":"2023-12-17T16:53:30Z","abstract_excerpt":"Recently, 3D understanding has become popular to facilitate autonomous agents to perform further decisionmaking. However, existing 3D datasets and methods are often limited to specific tasks. On the other hand, recent progress in Large Language Models (LLMs) and Multimodal Language Models (MLMs) have demonstrated exceptional general language and imagery tasking performance. Therefore, it is interesting to unlock MLM's potential to be 3D generalist for wider tasks. However, current MLMs' research has been less focused on 3D tasks due to a lack of large-scale 3D instruction-following datasets. I"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.10763","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2312.10763/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:25:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"01wqSJoz5YkNRro+mBy3YCThXPqEm9QmNYITGUkNg93/61z3uGzNCxT51TXQAmIsHssjKil2nNiv0KxRF9sQBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T03:54:36.868959Z"},"content_sha256":"190ce60b6c3e9bf386b15679febba0e36d9e2fad353a5918e16056d55f6b3291","schema_version":"1.0","event_id":"sha256:190ce60b6c3e9bf386b15679febba0e36d9e2fad353a5918e16056d55f6b3291"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NAGQUN6DETSZH7X6TVXXVS6AMJ/bundle.json","state_url":"https://pith.science/pith/NAGQUN6DETSZH7X6TVXXVS6AMJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NAGQUN6DETSZH7X6TVXXVS6AMJ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-18T03:54:36Z","links":{"resolver":"https://pith.science/pith/NAGQUN6DETSZH7X6TVXXVS6AMJ","bundle":"https://pith.science/pith/NAGQUN6DETSZH7X6TVXXVS6AMJ/bundle.json","state":"https://pith.science/pith/NAGQUN6DETSZH7X6TVXXVS6AMJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NAGQUN6DETSZH7X6TVXXVS6AMJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:NAGQUN6DETSZH7X6TVXXVS6AMJ","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":"6fef97adba673ea4d0966c83b7fbd6281db1edfb43c221957026290cd614d03e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-17T16:53:30Z","title_canon_sha256":"02c7f2b25dd49789796cbe9f0e54c460e6416efebff150437bc0115b318461fc"},"schema_version":"1.0","source":{"id":"2312.10763","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.10763","created_at":"2026-07-05T07:25:11Z"},{"alias_kind":"arxiv_version","alias_value":"2312.10763v1","created_at":"2026-07-05T07:25:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.10763","created_at":"2026-07-05T07:25:11Z"},{"alias_kind":"pith_short_12","alias_value":"NAGQUN6DETSZ","created_at":"2026-07-05T07:25:11Z"},{"alias_kind":"pith_short_16","alias_value":"NAGQUN6DETSZH7X6","created_at":"2026-07-05T07:25:11Z"},{"alias_kind":"pith_short_8","alias_value":"NAGQUN6D","created_at":"2026-07-05T07:25:11Z"}],"graph_snapshots":[{"event_id":"sha256:190ce60b6c3e9bf386b15679febba0e36d9e2fad353a5918e16056d55f6b3291","target":"graph","created_at":"2026-07-05T07:25:11Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2312.10763/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently, 3D understanding has become popular to facilitate autonomous agents to perform further decisionmaking. However, existing 3D datasets and methods are often limited to specific tasks. On the other hand, recent progress in Large Language Models (LLMs) and Multimodal Language Models (MLMs) have demonstrated exceptional general language and imagery tasking performance. Therefore, it is interesting to unlock MLM's potential to be 3D generalist for wider tasks. However, current MLMs' research has been less focused on 3D tasks due to a lack of large-scale 3D instruction-following datasets. I","authors_text":"Chi Zhang, Fukun Yin, Gang Yu, Hongyuan Zhu, Mingsheng Li, Sijin Chen, Tao Chen, Xin Chen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-17T16:53:30Z","title":"M3DBench: Let's Instruct Large Models with Multi-modal 3D Prompts"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.10763","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f62d9c8554f6074b46580072f15ac09aa6f865f2d56c4640282575c6f52754c0","target":"record","created_at":"2026-07-05T07:25:11Z","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":"6fef97adba673ea4d0966c83b7fbd6281db1edfb43c221957026290cd614d03e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-17T16:53:30Z","title_canon_sha256":"02c7f2b25dd49789796cbe9f0e54c460e6416efebff150437bc0115b318461fc"},"schema_version":"1.0","source":{"id":"2312.10763","kind":"arxiv","version":1}},"canonical_sha256":"680d0a37c324e593fefe9d6f7acbc0627f69bc8e3080e20212bb0989635cc6c1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"680d0a37c324e593fefe9d6f7acbc0627f69bc8e3080e20212bb0989635cc6c1","first_computed_at":"2026-07-05T07:25:11.934379Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:25:11.934379Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Sef2lE1t1Ygo7bRiYzqobXQrkNcYScL9oIyItKqHTPKRb5H3P8ZZ5T2uUiIY8ejJM75kylOyw10T4X2KD9yGAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:25:11.934906Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.10763","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f62d9c8554f6074b46580072f15ac09aa6f865f2d56c4640282575c6f52754c0","sha256:190ce60b6c3e9bf386b15679febba0e36d9e2fad353a5918e16056d55f6b3291"],"state_sha256":"e916e46bc6f79c95e2907da54ab641941c406ec77286026ae8d8c7618a1cc560"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ynL++Ra7wpToNu0uV2rokTXyaA+HPj+CFqf7Vtaecjf0WxHShuZQ4zP8+aM5O8AFOktsMN5GjMsBC6UGIQB1BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T03:54:36.876152Z","bundle_sha256":"fdb4419e7a177e37bb7a1b25b389b41377be16b46e88dcd6eca0bce0e0365c4b"}}