{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:R6MQHHIPISJL3JC64JHR7HTPBR","short_pith_number":"pith:R6MQHHIP","canonical_record":{"source":{"id":"2605.02759","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-05-04T15:58:08Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"7e3dc8a35b453715b51bda6a4918e6228c68093c8147a2c946761a861f08f745","abstract_canon_sha256":"8b0a5f494dedf0ba93776d167f0efae9abc808abafcc9651e2bcae4715194169"},"schema_version":"1.0"},"canonical_sha256":"8f99039d0f4492bda45ee24f1f9e6f0c4a361f678398a211d2dadfc5c0bfbcf2","source":{"kind":"arxiv","id":"2605.02759","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.02759","created_at":"2026-05-20T00:03:13Z"},{"alias_kind":"arxiv_version","alias_value":"2605.02759v2","created_at":"2026-05-20T00:03:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.02759","created_at":"2026-05-20T00:03:13Z"},{"alias_kind":"pith_short_12","alias_value":"R6MQHHIPISJL","created_at":"2026-05-20T00:03:13Z"},{"alias_kind":"pith_short_16","alias_value":"R6MQHHIPISJL3JC6","created_at":"2026-05-20T00:03:13Z"},{"alias_kind":"pith_short_8","alias_value":"R6MQHHIP","created_at":"2026-05-20T00:03:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:R6MQHHIPISJL3JC64JHR7HTPBR","target":"record","payload":{"canonical_record":{"source":{"id":"2605.02759","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-05-04T15:58:08Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"7e3dc8a35b453715b51bda6a4918e6228c68093c8147a2c946761a861f08f745","abstract_canon_sha256":"8b0a5f494dedf0ba93776d167f0efae9abc808abafcc9651e2bcae4715194169"},"schema_version":"1.0"},"canonical_sha256":"8f99039d0f4492bda45ee24f1f9e6f0c4a361f678398a211d2dadfc5c0bfbcf2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:03:13.762873Z","signature_b64":"m2BT01bDokQMrBTgqsgoHxFOFGWf2FzAamOR+XXQ0fe6i4U7jeeTgIpapVEdDvczjQIudiUdfsTnzqXjkN3nBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f99039d0f4492bda45ee24f1f9e6f0c4a361f678398a211d2dadfc5c0bfbcf2","last_reissued_at":"2026-05-20T00:03:13.762028Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:03:13.762028Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.02759","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:03:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kWPgGZ7FiAOM0R8+itdZo7GeFKbnSFLSxQ1faialXAusW+6peceQTJnuI/oROf7inPczwsOrvV0vPL4v4HBgBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T16:37:34.019500Z"},"content_sha256":"cd645938db417f29b5df2cc8154f880b51eaff274931c96f7e2a779b7b0485d1","schema_version":"1.0","event_id":"sha256:cd645938db417f29b5df2cc8154f880b51eaff274931c96f7e2a779b7b0485d1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:R6MQHHIPISJL3JC64JHR7HTPBR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DynoSLAM: Dynamic SLAM with Generative Graph Neural Networks for Real-World Social Navigation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"DynoSLAM uses Monte Carlo rollouts from graph neural networks to capture pedestrian uncertainty and embed it into SLAM optimization.","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Danil Tokhchukov, Gonzalo Ferrer, Veronika Morozova","submitted_at":"2026-05-04T15:58:08Z","abstract_excerpt":"Traditional Simultaneous Localization and Mapping (SLAM) algorithms rely heavily on the static environment assumption, which severely limits their applicability in real-world spaces populated by moving entities, such as pedestrians. In this work, we propose DynoSLAM, a tightly-coupled Dynamic GraphSLAM architecture that integrates socially-aware Graph Neural Networks (GNNs) directly into the factor graph optimization. Unlike conventional approaches that use rigid constant-velocity heuristics or deterministic single-agent neural priors, our framework formulates pedestrian motion forecasting as "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"By utilizing Monte Carlo rollouts from a trained GNN, we capture the multimodal epistemic uncertainty of human interactions and embed it into the SLAM graph via a dynamic Mahalanobis distance factor.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That Monte Carlo samples from the pre-trained GNN will produce uncertainty estimates that integrate stably into the factor-graph optimizer without introducing new failure modes or requiring post-hoc tuning.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"DynoSLAM embeds stochastic pedestrian motion predictions from generative GNNs into a dynamic GraphSLAM factor graph to enable safer robot navigation in human environments.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"DynoSLAM uses Monte Carlo rollouts from graph neural networks to capture pedestrian uncertainty and embed it into SLAM optimization.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"8dc13203bd341c8606f0bf74fd11e1b6edb3e2b89d6e4a3309d27182da49c2cb"},"source":{"id":"2605.02759","kind":"arxiv","version":2},"verdict":{"id":"29e17ef0-44f2-471c-92b0-d6d3bdbcd15e","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-08T17:38:25.901378Z","strongest_claim":"By utilizing Monte Carlo rollouts from a trained GNN, we capture the multimodal epistemic uncertainty of human interactions and embed it into the SLAM graph via a dynamic Mahalanobis distance factor.","one_line_summary":"DynoSLAM embeds stochastic pedestrian motion predictions from generative GNNs into a dynamic GraphSLAM factor graph to enable safer robot navigation in human environments.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That Monte Carlo samples from the pre-trained GNN will produce uncertainty estimates that integrate stably into the factor-graph optimizer without introducing new failure modes or requiring post-hoc tuning.","pith_extraction_headline":"DynoSLAM uses Monte Carlo rollouts from graph neural networks to capture pedestrian uncertainty and embed it into SLAM optimization."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.02759/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_compliance","ran_at":"2026-05-19T16:01:38.021742Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"8c92a413ac3ef3309b11c04503c95b572578234e1ddbde9145a9f94f6bfc557a"},"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":"29e17ef0-44f2-471c-92b0-d6d3bdbcd15e"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:03:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yH7hsARMbaBbxyk1AjT1nI4rbc3PQ+7DvKlQPxYFcEqxughR8BRiArdslmRAqYnA4cELCXLzNJhGHRcoRzSqDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T16:37:34.020387Z"},"content_sha256":"57a04002a269bc62092f29659dd8ba2ff45b2901e350f36da148bc67ee4c7c8d","schema_version":"1.0","event_id":"sha256:57a04002a269bc62092f29659dd8ba2ff45b2901e350f36da148bc67ee4c7c8d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/R6MQHHIPISJL3JC64JHR7HTPBR/bundle.json","state_url":"https://pith.science/pith/R6MQHHIPISJL3JC64JHR7HTPBR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/R6MQHHIPISJL3JC64JHR7HTPBR/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":"2026-06-07T16:37:34Z","links":{"resolver":"https://pith.science/pith/R6MQHHIPISJL3JC64JHR7HTPBR","bundle":"https://pith.science/pith/R6MQHHIPISJL3JC64JHR7HTPBR/bundle.json","state":"https://pith.science/pith/R6MQHHIPISJL3JC64JHR7HTPBR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/R6MQHHIPISJL3JC64JHR7HTPBR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:R6MQHHIPISJL3JC64JHR7HTPBR","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":"8b0a5f494dedf0ba93776d167f0efae9abc808abafcc9651e2bcae4715194169","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-05-04T15:58:08Z","title_canon_sha256":"7e3dc8a35b453715b51bda6a4918e6228c68093c8147a2c946761a861f08f745"},"schema_version":"1.0","source":{"id":"2605.02759","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.02759","created_at":"2026-05-20T00:03:13Z"},{"alias_kind":"arxiv_version","alias_value":"2605.02759v2","created_at":"2026-05-20T00:03:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.02759","created_at":"2026-05-20T00:03:13Z"},{"alias_kind":"pith_short_12","alias_value":"R6MQHHIPISJL","created_at":"2026-05-20T00:03:13Z"},{"alias_kind":"pith_short_16","alias_value":"R6MQHHIPISJL3JC6","created_at":"2026-05-20T00:03:13Z"},{"alias_kind":"pith_short_8","alias_value":"R6MQHHIP","created_at":"2026-05-20T00:03:13Z"}],"graph_snapshots":[{"event_id":"sha256:57a04002a269bc62092f29659dd8ba2ff45b2901e350f36da148bc67ee4c7c8d","target":"graph","created_at":"2026-05-20T00:03:13Z","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":"By utilizing Monte Carlo rollouts from a trained GNN, we capture the multimodal epistemic uncertainty of human interactions and embed it into the SLAM graph via a dynamic Mahalanobis distance factor."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That Monte Carlo samples from the pre-trained GNN will produce uncertainty estimates that integrate stably into the factor-graph optimizer without introducing new failure modes or requiring post-hoc tuning."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"DynoSLAM embeds stochastic pedestrian motion predictions from generative GNNs into a dynamic GraphSLAM factor graph to enable safer robot navigation in human environments."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"DynoSLAM uses Monte Carlo rollouts from graph neural networks to capture pedestrian uncertainty and embed it into SLAM optimization."}],"snapshot_sha256":"8dc13203bd341c8606f0bf74fd11e1b6edb3e2b89d6e4a3309d27182da49c2cb"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T16:01:38.021742Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.02759/integrity.json","findings":[],"snapshot_sha256":"8c92a413ac3ef3309b11c04503c95b572578234e1ddbde9145a9f94f6bfc557a","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Traditional Simultaneous Localization and Mapping (SLAM) algorithms rely heavily on the static environment assumption, which severely limits their applicability in real-world spaces populated by moving entities, such as pedestrians. In this work, we propose DynoSLAM, a tightly-coupled Dynamic GraphSLAM architecture that integrates socially-aware Graph Neural Networks (GNNs) directly into the factor graph optimization. Unlike conventional approaches that use rigid constant-velocity heuristics or deterministic single-agent neural priors, our framework formulates pedestrian motion forecasting as ","authors_text":"Danil Tokhchukov, Gonzalo Ferrer, Veronika Morozova","cross_cats":["cs.CV"],"headline":"DynoSLAM uses Monte Carlo rollouts from graph neural networks to capture pedestrian uncertainty and embed it into SLAM optimization.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-05-04T15:58:08Z","title":"DynoSLAM: Dynamic SLAM with Generative Graph Neural Networks for Real-World Social Navigation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.02759","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-08T17:38:25.901378Z","id":"29e17ef0-44f2-471c-92b0-d6d3bdbcd15e","model_set":{"reader":"grok-4.3"},"one_line_summary":"DynoSLAM embeds stochastic pedestrian motion predictions from generative GNNs into a dynamic GraphSLAM factor graph to enable safer robot navigation in human environments.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"DynoSLAM uses Monte Carlo rollouts from graph neural networks to capture pedestrian uncertainty and embed it into SLAM optimization.","strongest_claim":"By utilizing Monte Carlo rollouts from a trained GNN, we capture the multimodal epistemic uncertainty of human interactions and embed it into the SLAM graph via a dynamic Mahalanobis distance factor.","weakest_assumption":"That Monte Carlo samples from the pre-trained GNN will produce uncertainty estimates that integrate stably into the factor-graph optimizer without introducing new failure modes or requiring post-hoc tuning."}},"verdict_id":"29e17ef0-44f2-471c-92b0-d6d3bdbcd15e"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:cd645938db417f29b5df2cc8154f880b51eaff274931c96f7e2a779b7b0485d1","target":"record","created_at":"2026-05-20T00:03:13Z","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":"8b0a5f494dedf0ba93776d167f0efae9abc808abafcc9651e2bcae4715194169","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-05-04T15:58:08Z","title_canon_sha256":"7e3dc8a35b453715b51bda6a4918e6228c68093c8147a2c946761a861f08f745"},"schema_version":"1.0","source":{"id":"2605.02759","kind":"arxiv","version":2}},"canonical_sha256":"8f99039d0f4492bda45ee24f1f9e6f0c4a361f678398a211d2dadfc5c0bfbcf2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8f99039d0f4492bda45ee24f1f9e6f0c4a361f678398a211d2dadfc5c0bfbcf2","first_computed_at":"2026-05-20T00:03:13.762028Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:03:13.762028Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"m2BT01bDokQMrBTgqsgoHxFOFGWf2FzAamOR+XXQ0fe6i4U7jeeTgIpapVEdDvczjQIudiUdfsTnzqXjkN3nBw==","signature_status":"signed_v1","signed_at":"2026-05-20T00:03:13.762873Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.02759","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cd645938db417f29b5df2cc8154f880b51eaff274931c96f7e2a779b7b0485d1","sha256:57a04002a269bc62092f29659dd8ba2ff45b2901e350f36da148bc67ee4c7c8d"],"state_sha256":"7443c30256635e6de73851c48ab9b0fd68ed3a8dba989d2aecc2c584e8af3740"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HjP8Q/QewcisHoNbteCOhDhK7lNe8LPzfjhvej+7jtylW0RTcrYqVJs142DWiN/4BrxFTGnloB+Z+PcGr/fwDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T16:37:34.024478Z","bundle_sha256":"34ae8ad7f62cb321f5be48fe397f4751b26ec2a9b4793a1c5ab0467f51c463b7"}}