{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:YIA7IVJXX64NHGBESFNQM3AT4S","short_pith_number":"pith:YIA7IVJX","canonical_record":{"source":{"id":"2507.16524","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-22T12:32:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"68f30818d63f4d1647c435549bfcc59b4f036430cf30aff76e23a7c2f06490db","abstract_canon_sha256":"95f55471751cd622d563bbe2ecc9f5e5a57c258e030ae42b568114367d607912"},"schema_version":"1.0"},"canonical_sha256":"c201f45537bfb8d39824915b066c13e48393e1c23633ef79ebe5050fb387c956","source":{"kind":"arxiv","id":"2507.16524","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.16524","created_at":"2026-07-05T11:41:11Z"},{"alias_kind":"arxiv_version","alias_value":"2507.16524v1","created_at":"2026-07-05T11:41:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.16524","created_at":"2026-07-05T11:41:11Z"},{"alias_kind":"pith_short_12","alias_value":"YIA7IVJXX64N","created_at":"2026-07-05T11:41:11Z"},{"alias_kind":"pith_short_16","alias_value":"YIA7IVJXX64NHGBE","created_at":"2026-07-05T11:41:11Z"},{"alias_kind":"pith_short_8","alias_value":"YIA7IVJX","created_at":"2026-07-05T11:41:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:YIA7IVJXX64NHGBESFNQM3AT4S","target":"record","payload":{"canonical_record":{"source":{"id":"2507.16524","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-22T12:32:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"68f30818d63f4d1647c435549bfcc59b4f036430cf30aff76e23a7c2f06490db","abstract_canon_sha256":"95f55471751cd622d563bbe2ecc9f5e5a57c258e030ae42b568114367d607912"},"schema_version":"1.0"},"canonical_sha256":"c201f45537bfb8d39824915b066c13e48393e1c23633ef79ebe5050fb387c956","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:41:11.474972Z","signature_b64":"Y8LrUcyVHsgPUybbaqWqxUdqF8d45RSNlFiWhd24MrBDd90qyEj8UB4b42DQVc7LEIjgzCXEw0UwbpYcnzNGAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c201f45537bfb8d39824915b066c13e48393e1c23633ef79ebe5050fb387c956","last_reissued_at":"2026-07-05T11:41:11.474404Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:41:11.474404Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.16524","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":"2026-07-05T11:41:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rzsuVZmeIHB/4LV7gg2h4ohZYcHLu6UbRjt3TC0yzG3/g5jrrE2Y+HPXXUuMkQy36FziRiQRG+dE8KGGebpNAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-24T20:07:34.092150Z"},"content_sha256":"ad1254525345cf9e0ac466b4eb70c2d2406896fc160da0795d27e3b74ae9ded2","schema_version":"1.0","event_id":"sha256:ad1254525345cf9e0ac466b4eb70c2d2406896fc160da0795d27e3b74ae9ded2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:YIA7IVJXX64NHGBESFNQM3AT4S","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Spatial 3D-LLM: Exploring Spatial Awareness in 3D Vision-Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Chao Zhang, Jiaxing Qi, Ruifei Ma, Xiangde Liu, Xiaoyan Wang, Yifan Xu, Zeju Li, Zhifei Yang","submitted_at":"2025-07-22T12:32:35Z","abstract_excerpt":"New era has unlocked exciting possibilities for extending Large Language Models (LLMs) to tackle 3D vision-language tasks. However, most existing 3D multimodal LLMs (MLLMs) rely on compressing holistic 3D scene information or segmenting independent objects to perform these tasks, which limits their spatial awareness due to insufficient representation of the richness inherent in 3D scenes. To overcome these limitations, we propose Spatial 3D-LLM, a 3D MLLM specifically designed to enhance spatial awareness for 3D vision-language tasks by enriching the spatial embeddings of 3D scenes. Spatial 3D"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.16524","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/2507.16524/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":"2026-07-05T11:41:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"i7OuMfRvcXoF1LU9fRuCNKvgVJZaL9ey+CUr+C75C03InHYXUq6bGRM5RLKbB+tIb5b9Jw3J1rhkBSrONyGRCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-24T20:07:34.092537Z"},"content_sha256":"510c2b4b1cee6e645da5d1a09eb20ac07f24b78679b14b8421ca14c3be98eaa8","schema_version":"1.0","event_id":"sha256:510c2b4b1cee6e645da5d1a09eb20ac07f24b78679b14b8421ca14c3be98eaa8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YIA7IVJXX64NHGBESFNQM3AT4S/bundle.json","state_url":"https://pith.science/pith/YIA7IVJXX64NHGBESFNQM3AT4S/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YIA7IVJXX64NHGBESFNQM3AT4S/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":"2026-07-24T20:07:34Z","links":{"resolver":"https://pith.science/pith/YIA7IVJXX64NHGBESFNQM3AT4S","bundle":"https://pith.science/pith/YIA7IVJXX64NHGBESFNQM3AT4S/bundle.json","state":"https://pith.science/pith/YIA7IVJXX64NHGBESFNQM3AT4S/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YIA7IVJXX64NHGBESFNQM3AT4S/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:YIA7IVJXX64NHGBESFNQM3AT4S","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":"95f55471751cd622d563bbe2ecc9f5e5a57c258e030ae42b568114367d607912","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-22T12:32:35Z","title_canon_sha256":"68f30818d63f4d1647c435549bfcc59b4f036430cf30aff76e23a7c2f06490db"},"schema_version":"1.0","source":{"id":"2507.16524","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.16524","created_at":"2026-07-05T11:41:11Z"},{"alias_kind":"arxiv_version","alias_value":"2507.16524v1","created_at":"2026-07-05T11:41:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.16524","created_at":"2026-07-05T11:41:11Z"},{"alias_kind":"pith_short_12","alias_value":"YIA7IVJXX64N","created_at":"2026-07-05T11:41:11Z"},{"alias_kind":"pith_short_16","alias_value":"YIA7IVJXX64NHGBE","created_at":"2026-07-05T11:41:11Z"},{"alias_kind":"pith_short_8","alias_value":"YIA7IVJX","created_at":"2026-07-05T11:41:11Z"}],"graph_snapshots":[{"event_id":"sha256:510c2b4b1cee6e645da5d1a09eb20ac07f24b78679b14b8421ca14c3be98eaa8","target":"graph","created_at":"2026-07-05T11:41:11Z","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/2507.16524/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"New era has unlocked exciting possibilities for extending Large Language Models (LLMs) to tackle 3D vision-language tasks. However, most existing 3D multimodal LLMs (MLLMs) rely on compressing holistic 3D scene information or segmenting independent objects to perform these tasks, which limits their spatial awareness due to insufficient representation of the richness inherent in 3D scenes. To overcome these limitations, we propose Spatial 3D-LLM, a 3D MLLM specifically designed to enhance spatial awareness for 3D vision-language tasks by enriching the spatial embeddings of 3D scenes. Spatial 3D","authors_text":"Chao Zhang, Jiaxing Qi, Ruifei Ma, Xiangde Liu, Xiaoyan Wang, Yifan Xu, Zeju Li, Zhifei Yang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-22T12:32:35Z","title":"Spatial 3D-LLM: Exploring Spatial Awareness in 3D Vision-Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.16524","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:ad1254525345cf9e0ac466b4eb70c2d2406896fc160da0795d27e3b74ae9ded2","target":"record","created_at":"2026-07-05T11:41:11Z","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":"95f55471751cd622d563bbe2ecc9f5e5a57c258e030ae42b568114367d607912","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-22T12:32:35Z","title_canon_sha256":"68f30818d63f4d1647c435549bfcc59b4f036430cf30aff76e23a7c2f06490db"},"schema_version":"1.0","source":{"id":"2507.16524","kind":"arxiv","version":1}},"canonical_sha256":"c201f45537bfb8d39824915b066c13e48393e1c23633ef79ebe5050fb387c956","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c201f45537bfb8d39824915b066c13e48393e1c23633ef79ebe5050fb387c956","first_computed_at":"2026-07-05T11:41:11.474404Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:41:11.474404Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Y8LrUcyVHsgPUybbaqWqxUdqF8d45RSNlFiWhd24MrBDd90qyEj8UB4b42DQVc7LEIjgzCXEw0UwbpYcnzNGAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:41:11.474972Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.16524","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ad1254525345cf9e0ac466b4eb70c2d2406896fc160da0795d27e3b74ae9ded2","sha256:510c2b4b1cee6e645da5d1a09eb20ac07f24b78679b14b8421ca14c3be98eaa8"],"state_sha256":"89f09f96a31a9c63c26672fb5230bf232e584da56082232232a8d85b84a95692"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/T4qLI9D2aFYuyWVPOIx+QNZUNmHyIChC0zAnU1vVQLs6QDHQhOMeUDOoCgLhxBQh/Odx6+91ax3iXThoJoDCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-24T20:07:34.097409Z","bundle_sha256":"71dca67f338867ec14f3950c0ffc17838f0c3a161b69dd41fb8c7731a1825618"}}