{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:NWZYT5L3JWHJJIUEQ4MYHTKWOZ","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":"891f8ba802fee08f5d547b7f6754c73a045341def375c244c3685f2f8e4a3e7b","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-04-27T16:10:17Z","title_canon_sha256":"99a3807a571ed5beba4d5630bab5666728330d5f7525dff64a399745626c4647"},"schema_version":"1.0","source":{"id":"2504.20109","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.20109","created_at":"2026-07-05T10:55:36Z"},{"alias_kind":"arxiv_version","alias_value":"2504.20109v1","created_at":"2026-07-05T10:55:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.20109","created_at":"2026-07-05T10:55:36Z"},{"alias_kind":"pith_short_12","alias_value":"NWZYT5L3JWHJ","created_at":"2026-07-05T10:55:36Z"},{"alias_kind":"pith_short_16","alias_value":"NWZYT5L3JWHJJIUE","created_at":"2026-07-05T10:55:36Z"},{"alias_kind":"pith_short_8","alias_value":"NWZYT5L3","created_at":"2026-07-05T10:55:36Z"}],"graph_snapshots":[{"event_id":"sha256:5a21c437fcafe207ff99e3df88381eb383ff1e2bd588f559ea7732c3bf32ae79","target":"graph","created_at":"2026-07-05T10:55:36Z","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/2504.20109/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Artificial Intelligence has made remarkable advancements in recent years, primarily driven by increasingly large deep learning models. However, achieving true Artificial General Intelligence (AGI) demands fundamentally new architectures rather than merely scaling up existing models. Current approaches largely depend on expanding model parameters, which improves task-specific performance but falls short in enabling continuous, adaptable, and generalized learning. Achieving AGI capable of continuous learning and personalization on resource-constrained edge devices is an even bigger challenge.\n  ","authors_text":"Amir Javaheri, Divya Gupta, Jairaj Singh Shaktawat, Rajeev Gupta, Ronak Parikh, Suhani Gupta","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-04-27T16:10:17Z","title":"Personalized Artificial General Intelligence (AGI) via Neuroscience-Inspired Continuous Learning Systems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.20109","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":"sha256:8b7b74b882ce1fc6395c19086bac7e26ae556ef253ac6a3cb4676cffa5dc63a3","target":"record","created_at":"2026-07-05T10:55:36Z","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":"891f8ba802fee08f5d547b7f6754c73a045341def375c244c3685f2f8e4a3e7b","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-04-27T16:10:17Z","title_canon_sha256":"99a3807a571ed5beba4d5630bab5666728330d5f7525dff64a399745626c4647"},"schema_version":"1.0","source":{"id":"2504.20109","kind":"arxiv","version":1}},"canonical_sha256":"6db389f57b4d8e94a284871983cd567641f7be764254260e4384018df6f8606a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6db389f57b4d8e94a284871983cd567641f7be764254260e4384018df6f8606a","first_computed_at":"2026-07-05T10:55:36.690964Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:55:36.690964Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qoMexAWcRDtUL0/aoLu55AB2F94O3eut8LOPfI0l2Tt/4NylHp43PEkiT7QUcSmYwEzrAPH9pAR+iQmAoNzhCg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:55:36.691452Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.20109","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8b7b74b882ce1fc6395c19086bac7e26ae556ef253ac6a3cb4676cffa5dc63a3","sha256:5a21c437fcafe207ff99e3df88381eb383ff1e2bd588f559ea7732c3bf32ae79"],"state_sha256":"2554b97973db3714c7854df0c6705ad3576cd758b2e5c1a5487d51ac23ba583d"}