{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:BT6CZ32FKKAVFIETLBDPHTR3SC","short_pith_number":"pith:BT6CZ32F","schema_version":"1.0","canonical_sha256":"0cfc2cef45528152a0935846f3ce3b908bbc8ecc6808c50dd0b6b0511a3f03f5","source":{"kind":"arxiv","id":"2403.04759","version":1},"attestation_state":"computed","paper":{"title":"Lifelong Intelligence Beyond the Edge using Hyperdimensional Computing","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.LG","authors_text":"Anthony Thomas, Ivannia Gomez Moreno, Louis Gutierrez, Tajana Rosing, Xiaofan Yu","submitted_at":"2024-03-07T18:56:33Z","abstract_excerpt":"On-device learning has emerged as a prevailing trend that avoids the slow response time and costly communication of cloud-based learning. The ability to learn continuously and indefinitely in a changing environment, and with resource constraints, is critical for real sensor deployments. However, existing designs are inadequate for practical scenarios with (i) streaming data input, (ii) lack of supervision and (iii) limited on-board resources. In this paper, we design and deploy the first on-device lifelong learning system called LifeHD for general IoT applications with limited supervision. Lif"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2403.04759","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-07T18:56:33Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"3705b17be9e1fdb89237bc224cdb4a4690c000679246f790699e1a3ce716b040","abstract_canon_sha256":"698dfadfcdb15db24dc61385dc4b9ba4e2ae5c343af7cc1f6595764ad1962e15"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:53:27.592522Z","signature_b64":"jZOzXuIeXkL9vxRA4ocAwA/u5LVRmgpfZk/AEBeVACZq6uVuLNzRQyUkmoidUiVxNm/HGvCcKGWuriqbQJFuCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0cfc2cef45528152a0935846f3ce3b908bbc8ecc6808c50dd0b6b0511a3f03f5","last_reissued_at":"2026-07-05T07:53:27.592069Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:53:27.592069Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Lifelong Intelligence Beyond the Edge using Hyperdimensional Computing","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.LG","authors_text":"Anthony Thomas, Ivannia Gomez Moreno, Louis Gutierrez, Tajana Rosing, Xiaofan Yu","submitted_at":"2024-03-07T18:56:33Z","abstract_excerpt":"On-device learning has emerged as a prevailing trend that avoids the slow response time and costly communication of cloud-based learning. The ability to learn continuously and indefinitely in a changing environment, and with resource constraints, is critical for real sensor deployments. However, existing designs are inadequate for practical scenarios with (i) streaming data input, (ii) lack of supervision and (iii) limited on-board resources. In this paper, we design and deploy the first on-device lifelong learning system called LifeHD for general IoT applications with limited supervision. Lif"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.04759","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/2403.04759/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2403.04759","created_at":"2026-07-05T07:53:27.592123+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.04759v1","created_at":"2026-07-05T07:53:27.592123+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.04759","created_at":"2026-07-05T07:53:27.592123+00:00"},{"alias_kind":"pith_short_12","alias_value":"BT6CZ32FKKAV","created_at":"2026-07-05T07:53:27.592123+00:00"},{"alias_kind":"pith_short_16","alias_value":"BT6CZ32FKKAVFIET","created_at":"2026-07-05T07:53:27.592123+00:00"},{"alias_kind":"pith_short_8","alias_value":"BT6CZ32F","created_at":"2026-07-05T07:53:27.592123+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.12331","citing_title":"HyperLiDAR: Adaptive Post-Deployment LiDAR Segmentation via Hyperdimensional Computing","ref_index":41,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BT6CZ32FKKAVFIETLBDPHTR3SC","json":"https://pith.science/pith/BT6CZ32FKKAVFIETLBDPHTR3SC.json","graph_json":"https://pith.science/api/pith-number/BT6CZ32FKKAVFIETLBDPHTR3SC/graph.json","events_json":"https://pith.science/api/pith-number/BT6CZ32FKKAVFIETLBDPHTR3SC/events.json","paper":"https://pith.science/paper/BT6CZ32F"},"agent_actions":{"view_html":"https://pith.science/pith/BT6CZ32FKKAVFIETLBDPHTR3SC","download_json":"https://pith.science/pith/BT6CZ32FKKAVFIETLBDPHTR3SC.json","view_paper":"https://pith.science/paper/BT6CZ32F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.04759&json=true","fetch_graph":"https://pith.science/api/pith-number/BT6CZ32FKKAVFIETLBDPHTR3SC/graph.json","fetch_events":"https://pith.science/api/pith-number/BT6CZ32FKKAVFIETLBDPHTR3SC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BT6CZ32FKKAVFIETLBDPHTR3SC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BT6CZ32FKKAVFIETLBDPHTR3SC/action/storage_attestation","attest_author":"https://pith.science/pith/BT6CZ32FKKAVFIETLBDPHTR3SC/action/author_attestation","sign_citation":"https://pith.science/pith/BT6CZ32FKKAVFIETLBDPHTR3SC/action/citation_signature","submit_replication":"https://pith.science/pith/BT6CZ32FKKAVFIETLBDPHTR3SC/action/replication_record"}},"created_at":"2026-07-05T07:53:27.592123+00:00","updated_at":"2026-07-05T07:53:27.592123+00:00"}