{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:ETLHFF36BXMHOE2W6UDRH363TT","short_pith_number":"pith:ETLHFF36","canonical_record":{"source":{"id":"2410.23968","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-10-31T14:22:20Z","cross_cats_sorted":[],"title_canon_sha256":"9497a92b3bebda99ebc7b9e13e748e6b789cbd78904a3b7627833d620b793072","abstract_canon_sha256":"dd492ad3e1faa86ebe1d03fca2a85e64fa74c399ccbecff043b94688f5262a26"},"schema_version":"1.0"},"canonical_sha256":"24d672977e0dd8771356f50713efdb9ce2e9252a75f2ec451eaf7456543ec187","source":{"kind":"arxiv","id":"2410.23968","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.23968","created_at":"2026-07-05T09:29:17Z"},{"alias_kind":"arxiv_version","alias_value":"2410.23968v1","created_at":"2026-07-05T09:29:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.23968","created_at":"2026-07-05T09:29:17Z"},{"alias_kind":"pith_short_12","alias_value":"ETLHFF36BXMH","created_at":"2026-07-05T09:29:17Z"},{"alias_kind":"pith_short_16","alias_value":"ETLHFF36BXMHOE2W","created_at":"2026-07-05T09:29:17Z"},{"alias_kind":"pith_short_8","alias_value":"ETLHFF36","created_at":"2026-07-05T09:29:17Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:ETLHFF36BXMHOE2W6UDRH363TT","target":"record","payload":{"canonical_record":{"source":{"id":"2410.23968","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-10-31T14:22:20Z","cross_cats_sorted":[],"title_canon_sha256":"9497a92b3bebda99ebc7b9e13e748e6b789cbd78904a3b7627833d620b793072","abstract_canon_sha256":"dd492ad3e1faa86ebe1d03fca2a85e64fa74c399ccbecff043b94688f5262a26"},"schema_version":"1.0"},"canonical_sha256":"24d672977e0dd8771356f50713efdb9ce2e9252a75f2ec451eaf7456543ec187","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:29:17.125810Z","signature_b64":"QbLMYjRC3A8dgpOuD++kG+iIaJN661L4+Qyb7OrRRlPsRtjTbRP+Y1XwieICcQpuXhc7QsL8awqpJNoGowuRAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"24d672977e0dd8771356f50713efdb9ce2e9252a75f2ec451eaf7456543ec187","last_reissued_at":"2026-07-05T09:29:17.125274Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:29:17.125274Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.23968","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":"2026-07-05T09:29:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mL2/ExUBETidqmIZvY8V08nzzOBXyotewzNnhYkQQpWJu/bQwLzZNn7jnX4f2g+XiAMY+A8okb6gH+RdF5u0AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T08:54:26.186066Z"},"content_sha256":"72d3a4246e47937f0ce163f97be2da51ff6fae433b000c06c355d21dc619a683","schema_version":"1.0","event_id":"sha256:72d3a4246e47937f0ce163f97be2da51ff6fae433b000c06c355d21dc619a683"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:ETLHFF36BXMHOE2W6UDRH363TT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"EmbodiedRAG: Dynamic 3D Scene Graph Retrieval for Efficient and Scalable Robot Task Planning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Aurora Schmidt, Bethany Kemp, Corban Rivera, Grayson Byrd, Meghan Booker","submitted_at":"2024-10-31T14:22:20Z","abstract_excerpt":"Recent advances in Large Language Models (LLMs) have helped facilitate exciting progress for robotic planning in real, open-world environments. 3D scene graphs (3DSGs) offer a promising environment representation for grounding such LLM-based planners as they are compact and semantically rich. However, as the robot's environment scales (e.g., number of entities tracked) and the complexity of scene graph information increases (e.g., maintaining more attributes), providing the 3DSG as-is to an LLM-based planner quickly becomes infeasible due to input token count limits and attentional biases pres"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.23968","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/2410.23968/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":"2026-07-05T09:29:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GlKoXf+rYk/WZ7I9KSGORBkCKC+oM6S10zKz6ryzUPwvYC3OnNh7zX9SQWcdR4leJ5RT8JChyLzfYmx9QSk1Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T08:54:26.186919Z"},"content_sha256":"ee9722a9409cd8c8feb55ed290046eeb14e6688fb862ed0286a104d4057a53b1","schema_version":"1.0","event_id":"sha256:ee9722a9409cd8c8feb55ed290046eeb14e6688fb862ed0286a104d4057a53b1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ETLHFF36BXMHOE2W6UDRH363TT/bundle.json","state_url":"https://pith.science/pith/ETLHFF36BXMHOE2W6UDRH363TT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ETLHFF36BXMHOE2W6UDRH363TT/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":"2026-08-04T08:54:26Z","links":{"resolver":"https://pith.science/pith/ETLHFF36BXMHOE2W6UDRH363TT","bundle":"https://pith.science/pith/ETLHFF36BXMHOE2W6UDRH363TT/bundle.json","state":"https://pith.science/pith/ETLHFF36BXMHOE2W6UDRH363TT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ETLHFF36BXMHOE2W6UDRH363TT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:ETLHFF36BXMHOE2W6UDRH363TT","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":"dd492ad3e1faa86ebe1d03fca2a85e64fa74c399ccbecff043b94688f5262a26","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-10-31T14:22:20Z","title_canon_sha256":"9497a92b3bebda99ebc7b9e13e748e6b789cbd78904a3b7627833d620b793072"},"schema_version":"1.0","source":{"id":"2410.23968","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.23968","created_at":"2026-07-05T09:29:17Z"},{"alias_kind":"arxiv_version","alias_value":"2410.23968v1","created_at":"2026-07-05T09:29:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.23968","created_at":"2026-07-05T09:29:17Z"},{"alias_kind":"pith_short_12","alias_value":"ETLHFF36BXMH","created_at":"2026-07-05T09:29:17Z"},{"alias_kind":"pith_short_16","alias_value":"ETLHFF36BXMHOE2W","created_at":"2026-07-05T09:29:17Z"},{"alias_kind":"pith_short_8","alias_value":"ETLHFF36","created_at":"2026-07-05T09:29:17Z"}],"graph_snapshots":[{"event_id":"sha256:ee9722a9409cd8c8feb55ed290046eeb14e6688fb862ed0286a104d4057a53b1","target":"graph","created_at":"2026-07-05T09:29:17Z","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/2410.23968/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advances in Large Language Models (LLMs) have helped facilitate exciting progress for robotic planning in real, open-world environments. 3D scene graphs (3DSGs) offer a promising environment representation for grounding such LLM-based planners as they are compact and semantically rich. However, as the robot's environment scales (e.g., number of entities tracked) and the complexity of scene graph information increases (e.g., maintaining more attributes), providing the 3DSG as-is to an LLM-based planner quickly becomes infeasible due to input token count limits and attentional biases pres","authors_text":"Aurora Schmidt, Bethany Kemp, Corban Rivera, Grayson Byrd, Meghan Booker","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-10-31T14:22:20Z","title":"EmbodiedRAG: Dynamic 3D Scene Graph Retrieval for Efficient and Scalable Robot Task Planning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.23968","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":"sha256:72d3a4246e47937f0ce163f97be2da51ff6fae433b000c06c355d21dc619a683","target":"record","created_at":"2026-07-05T09:29:17Z","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":"dd492ad3e1faa86ebe1d03fca2a85e64fa74c399ccbecff043b94688f5262a26","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-10-31T14:22:20Z","title_canon_sha256":"9497a92b3bebda99ebc7b9e13e748e6b789cbd78904a3b7627833d620b793072"},"schema_version":"1.0","source":{"id":"2410.23968","kind":"arxiv","version":1}},"canonical_sha256":"24d672977e0dd8771356f50713efdb9ce2e9252a75f2ec451eaf7456543ec187","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"24d672977e0dd8771356f50713efdb9ce2e9252a75f2ec451eaf7456543ec187","first_computed_at":"2026-07-05T09:29:17.125274Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:29:17.125274Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QbLMYjRC3A8dgpOuD++kG+iIaJN661L4+Qyb7OrRRlPsRtjTbRP+Y1XwieICcQpuXhc7QsL8awqpJNoGowuRAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:29:17.125810Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.23968","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:72d3a4246e47937f0ce163f97be2da51ff6fae433b000c06c355d21dc619a683","sha256:ee9722a9409cd8c8feb55ed290046eeb14e6688fb862ed0286a104d4057a53b1"],"state_sha256":"ff2bb051e775912b7ee60e1fcc4973de11e96b2c1a4e75fbd25bc185ec30ddce"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PTxK1tygTv0PEu9K4+COPCQtV1mVCRVGEaiFj58hI4ctX/+m/vMzkDm3rDkQMGczJCngDj3wMzmMUHc5hL4zAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T08:54:26.193421Z","bundle_sha256":"2081bea80a9e809e419ecaeb075d5395e0a666a09fbb27f7c0629d3d2e9bbba6"}}