{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:YT2S6HR7NBEG6TX54E4PSO4NSF","short_pith_number":"pith:YT2S6HR7","schema_version":"1.0","canonical_sha256":"c4f52f1e3f68486f4efde138f93b8d916e57a2bae1709a3f239b744ce8197917","source":{"kind":"arxiv","id":"2310.10437","version":1},"attestation_state":"computed","paper":{"title":"Physical learning of power-efficient solutions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.soft","cond-mat.stat-mech"],"primary_cat":"cond-mat.dis-nn","authors_text":"Andrea J. Liu, Dinesh Jayaraman, Douglas J. Durian, Menachem Stern, Sam Dillavou","submitted_at":"2023-10-16T14:20:16Z","abstract_excerpt":"As the size and ubiquity of artificial intelligence and computational machine learning (ML) models grow, their energy consumption for training and use is rapidly becoming economically and environmentally unsustainable. Neuromorphic computing, or the implementation of ML in hardware, has the potential to reduce this cost. In particular, recent laboratory prototypes of self-learning electronic circuits, examples of ``physical learning machines,\" open the door to analog hardware that directly employs physics to learn desired functions from examples. In this work, we show that this hardware platfo"},"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":"2310.10437","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.dis-nn","submitted_at":"2023-10-16T14:20:16Z","cross_cats_sorted":["cond-mat.soft","cond-mat.stat-mech"],"title_canon_sha256":"a432325d2ffd0709f029cdedd97322b56c853863e55ce1eb5bbeffb527973660","abstract_canon_sha256":"04f566d8903eea71011f83705ddf046abb37834ba5ba042366632405a5b57573"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:01:20.062825Z","signature_b64":"v5eQIFDBlbQ8X33EgGsYK8diAMK+4Oc58PLiMWeLzuTxYKNRjPSar4xOTZHAivnopN/IscaGEyfmSZJBKnZVAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c4f52f1e3f68486f4efde138f93b8d916e57a2bae1709a3f239b744ce8197917","last_reissued_at":"2026-07-05T07:01:20.062379Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:01:20.062379Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Physical learning of power-efficient solutions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.soft","cond-mat.stat-mech"],"primary_cat":"cond-mat.dis-nn","authors_text":"Andrea J. Liu, Dinesh Jayaraman, Douglas J. Durian, Menachem Stern, Sam Dillavou","submitted_at":"2023-10-16T14:20:16Z","abstract_excerpt":"As the size and ubiquity of artificial intelligence and computational machine learning (ML) models grow, their energy consumption for training and use is rapidly becoming economically and environmentally unsustainable. Neuromorphic computing, or the implementation of ML in hardware, has the potential to reduce this cost. In particular, recent laboratory prototypes of self-learning electronic circuits, examples of ``physical learning machines,\" open the door to analog hardware that directly employs physics to learn desired functions from examples. In this work, we show that this hardware platfo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.10437","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/2310.10437/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":"2310.10437","created_at":"2026-07-05T07:01:20.062439+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.10437v1","created_at":"2026-07-05T07:01:20.062439+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.10437","created_at":"2026-07-05T07:01:20.062439+00:00"},{"alias_kind":"pith_short_12","alias_value":"YT2S6HR7NBEG","created_at":"2026-07-05T07:01:20.062439+00:00"},{"alias_kind":"pith_short_16","alias_value":"YT2S6HR7NBEG6TX5","created_at":"2026-07-05T07:01:20.062439+00:00"},{"alias_kind":"pith_short_8","alias_value":"YT2S6HR7","created_at":"2026-07-05T07:01:20.062439+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.22017","citing_title":"Ecosystems as adaptive living circuits","ref_index":56,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YT2S6HR7NBEG6TX54E4PSO4NSF","json":"https://pith.science/pith/YT2S6HR7NBEG6TX54E4PSO4NSF.json","graph_json":"https://pith.science/api/pith-number/YT2S6HR7NBEG6TX54E4PSO4NSF/graph.json","events_json":"https://pith.science/api/pith-number/YT2S6HR7NBEG6TX54E4PSO4NSF/events.json","paper":"https://pith.science/paper/YT2S6HR7"},"agent_actions":{"view_html":"https://pith.science/pith/YT2S6HR7NBEG6TX54E4PSO4NSF","download_json":"https://pith.science/pith/YT2S6HR7NBEG6TX54E4PSO4NSF.json","view_paper":"https://pith.science/paper/YT2S6HR7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.10437&json=true","fetch_graph":"https://pith.science/api/pith-number/YT2S6HR7NBEG6TX54E4PSO4NSF/graph.json","fetch_events":"https://pith.science/api/pith-number/YT2S6HR7NBEG6TX54E4PSO4NSF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YT2S6HR7NBEG6TX54E4PSO4NSF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YT2S6HR7NBEG6TX54E4PSO4NSF/action/storage_attestation","attest_author":"https://pith.science/pith/YT2S6HR7NBEG6TX54E4PSO4NSF/action/author_attestation","sign_citation":"https://pith.science/pith/YT2S6HR7NBEG6TX54E4PSO4NSF/action/citation_signature","submit_replication":"https://pith.science/pith/YT2S6HR7NBEG6TX54E4PSO4NSF/action/replication_record"}},"created_at":"2026-07-05T07:01:20.062439+00:00","updated_at":"2026-07-05T07:01:20.062439+00:00"}