{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:PI3GNZP6T47ZZO7FOEY7VJAYV3","short_pith_number":"pith:PI3GNZP6","canonical_record":{"source":{"id":"2403.19857","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2024-03-28T22:06:04Z","cross_cats_sorted":[],"title_canon_sha256":"25d46b6392601eb502f592bf5940dcf711900a5d6002755b62a6fde344250664","abstract_canon_sha256":"953849fa696312ed3e47e2ac15f0772b2a7a56dd4c1f91ce664675394648c56d"},"schema_version":"1.0"},"canonical_sha256":"7a3666e5fe9f3f9cbbe57131faa418aed11f10a7fda0557daa10db6872f06ea8","source":{"kind":"arxiv","id":"2403.19857","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.19857","created_at":"2026-07-05T08:02:09Z"},{"alias_kind":"arxiv_version","alias_value":"2403.19857v1","created_at":"2026-07-05T08:02:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.19857","created_at":"2026-07-05T08:02:09Z"},{"alias_kind":"pith_short_12","alias_value":"PI3GNZP6T47Z","created_at":"2026-07-05T08:02:09Z"},{"alias_kind":"pith_short_16","alias_value":"PI3GNZP6T47ZZO7F","created_at":"2026-07-05T08:02:09Z"},{"alias_kind":"pith_short_8","alias_value":"PI3GNZP6","created_at":"2026-07-05T08:02:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:PI3GNZP6T47ZZO7FOEY7VJAYV3","target":"record","payload":{"canonical_record":{"source":{"id":"2403.19857","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2024-03-28T22:06:04Z","cross_cats_sorted":[],"title_canon_sha256":"25d46b6392601eb502f592bf5940dcf711900a5d6002755b62a6fde344250664","abstract_canon_sha256":"953849fa696312ed3e47e2ac15f0772b2a7a56dd4c1f91ce664675394648c56d"},"schema_version":"1.0"},"canonical_sha256":"7a3666e5fe9f3f9cbbe57131faa418aed11f10a7fda0557daa10db6872f06ea8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:02:09.902526Z","signature_b64":"8uVbt0Jd+XJSws5DW3j7Q35JEBfx3t4O2kcm4eHPu3DHz41coU31gBYDIW9mlBdn3FZ5pKltqx1gmV2U5dZWCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a3666e5fe9f3f9cbbe57131faa418aed11f10a7fda0557daa10db6872f06ea8","last_reissued_at":"2026-07-05T08:02:09.902051Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:02:09.902051Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.19857","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":"2026-07-05T08:02:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Hw9VvQzCurmvNthzvbKvjT8SyzY8anWB3WMEW3yL2ucdrBOm1XdGA/AxYUnxpqDB5n+HGNv73yj9iN9PrArfDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T18:27:43.024655Z"},"content_sha256":"c018142d9e628748f4bb182670a88f42fa737594df4fdd83bdb32268dbfc6686","schema_version":"1.0","event_id":"sha256:c018142d9e628748f4bb182670a88f42fa737594df4fdd83bdb32268dbfc6686"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:PI3GNZP6T47ZZO7FOEY7VJAYV3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LLMSense: Harnessing LLMs for High-level Reasoning Over Spatiotemporal Sensor Traces","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Mani Srivastava, Xiaomin Ouyang","submitted_at":"2024-03-28T22:06:04Z","abstract_excerpt":"Most studies on machine learning in sensing systems focus on low-level perception tasks that process raw sensory data within a short time window. However, many practical applications, such as human routine modeling and occupancy tracking, require high-level reasoning abilities to comprehend concepts and make inferences based on long-term sensor traces. Existing machine learning-based approaches for handling such complex tasks struggle to generalize due to the limited training samples and the high dimensionality of sensor traces, necessitating the integration of human knowledge for designing fi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.19857","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/2403.19857/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":"2026-07-05T08:02:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"L1Pl0D316uk/zS4viJ2YcCK7K6hpdsILQJC9fcdgRVVJ1uNViqdsrXD69nwsFajAKE5ZDp83u0gZnfHG3Ap+CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T18:27:43.025206Z"},"content_sha256":"4ebd3c308cf6c35b3880086e7b2b2f20d91f7b9e55320bdfb0a0c8d790696b3b","schema_version":"1.0","event_id":"sha256:4ebd3c308cf6c35b3880086e7b2b2f20d91f7b9e55320bdfb0a0c8d790696b3b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PI3GNZP6T47ZZO7FOEY7VJAYV3/bundle.json","state_url":"https://pith.science/pith/PI3GNZP6T47ZZO7FOEY7VJAYV3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PI3GNZP6T47ZZO7FOEY7VJAYV3/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":"2026-08-07T18:27:43Z","links":{"resolver":"https://pith.science/pith/PI3GNZP6T47ZZO7FOEY7VJAYV3","bundle":"https://pith.science/pith/PI3GNZP6T47ZZO7FOEY7VJAYV3/bundle.json","state":"https://pith.science/pith/PI3GNZP6T47ZZO7FOEY7VJAYV3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PI3GNZP6T47ZZO7FOEY7VJAYV3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:PI3GNZP6T47ZZO7FOEY7VJAYV3","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":"953849fa696312ed3e47e2ac15f0772b2a7a56dd4c1f91ce664675394648c56d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2024-03-28T22:06:04Z","title_canon_sha256":"25d46b6392601eb502f592bf5940dcf711900a5d6002755b62a6fde344250664"},"schema_version":"1.0","source":{"id":"2403.19857","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.19857","created_at":"2026-07-05T08:02:09Z"},{"alias_kind":"arxiv_version","alias_value":"2403.19857v1","created_at":"2026-07-05T08:02:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.19857","created_at":"2026-07-05T08:02:09Z"},{"alias_kind":"pith_short_12","alias_value":"PI3GNZP6T47Z","created_at":"2026-07-05T08:02:09Z"},{"alias_kind":"pith_short_16","alias_value":"PI3GNZP6T47ZZO7F","created_at":"2026-07-05T08:02:09Z"},{"alias_kind":"pith_short_8","alias_value":"PI3GNZP6","created_at":"2026-07-05T08:02:09Z"}],"graph_snapshots":[{"event_id":"sha256:4ebd3c308cf6c35b3880086e7b2b2f20d91f7b9e55320bdfb0a0c8d790696b3b","target":"graph","created_at":"2026-07-05T08:02:09Z","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/2403.19857/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Most studies on machine learning in sensing systems focus on low-level perception tasks that process raw sensory data within a short time window. However, many practical applications, such as human routine modeling and occupancy tracking, require high-level reasoning abilities to comprehend concepts and make inferences based on long-term sensor traces. Existing machine learning-based approaches for handling such complex tasks struggle to generalize due to the limited training samples and the high dimensionality of sensor traces, necessitating the integration of human knowledge for designing fi","authors_text":"Mani Srivastava, Xiaomin Ouyang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2024-03-28T22:06:04Z","title":"LLMSense: Harnessing LLMs for High-level Reasoning Over Spatiotemporal Sensor Traces"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.19857","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:c018142d9e628748f4bb182670a88f42fa737594df4fdd83bdb32268dbfc6686","target":"record","created_at":"2026-07-05T08:02:09Z","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":"953849fa696312ed3e47e2ac15f0772b2a7a56dd4c1f91ce664675394648c56d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2024-03-28T22:06:04Z","title_canon_sha256":"25d46b6392601eb502f592bf5940dcf711900a5d6002755b62a6fde344250664"},"schema_version":"1.0","source":{"id":"2403.19857","kind":"arxiv","version":1}},"canonical_sha256":"7a3666e5fe9f3f9cbbe57131faa418aed11f10a7fda0557daa10db6872f06ea8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7a3666e5fe9f3f9cbbe57131faa418aed11f10a7fda0557daa10db6872f06ea8","first_computed_at":"2026-07-05T08:02:09.902051Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:02:09.902051Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8uVbt0Jd+XJSws5DW3j7Q35JEBfx3t4O2kcm4eHPu3DHz41coU31gBYDIW9mlBdn3FZ5pKltqx1gmV2U5dZWCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:02:09.902526Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.19857","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c018142d9e628748f4bb182670a88f42fa737594df4fdd83bdb32268dbfc6686","sha256:4ebd3c308cf6c35b3880086e7b2b2f20d91f7b9e55320bdfb0a0c8d790696b3b"],"state_sha256":"a0d22efbf7d788a0047a85a6b171fa5f457d300718798cf2c171a27ce4e5d023"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XdRmYmlFXGE0F0iqETfVs8Ub0sO9BqLVQgR9cULth6Vr81xbTppzxW0AlSXG0gwt+gGe8CHLEP6NBeQQcJ8uCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T18:27:43.030098Z","bundle_sha256":"2cb7199ffbe7368f69362a91b8c803bc61c938bac4815103ad6c62c43d4cf909"}}