{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:FSO44ZQGNZXSXNQRSFD3UDBCOD","short_pith_number":"pith:FSO44ZQG","canonical_record":{"source":{"id":"2510.16282","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-10-18T00:41:25Z","cross_cats_sorted":[],"title_canon_sha256":"01c5d9de9d69e5d72fe41922a9db41f9677284f580544314e14f838641de7f81","abstract_canon_sha256":"0222fa8bc6a5d7a0f8798fb4923f40d5a8d18174acccbc98392e723e86def5e9"},"schema_version":"1.0"},"canonical_sha256":"2c9dce66066e6f2bb6119147ba0c2270da0c9279cc7c0eebba0ca4b733382670","source":{"kind":"arxiv","id":"2510.16282","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.16282","created_at":"2026-06-03T01:05:06Z"},{"alias_kind":"arxiv_version","alias_value":"2510.16282v2","created_at":"2026-06-03T01:05:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.16282","created_at":"2026-06-03T01:05:06Z"},{"alias_kind":"pith_short_12","alias_value":"FSO44ZQGNZXS","created_at":"2026-06-03T01:05:06Z"},{"alias_kind":"pith_short_16","alias_value":"FSO44ZQGNZXSXNQR","created_at":"2026-06-03T01:05:06Z"},{"alias_kind":"pith_short_8","alias_value":"FSO44ZQG","created_at":"2026-06-03T01:05:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:FSO44ZQGNZXSXNQRSFD3UDBCOD","target":"record","payload":{"canonical_record":{"source":{"id":"2510.16282","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-10-18T00:41:25Z","cross_cats_sorted":[],"title_canon_sha256":"01c5d9de9d69e5d72fe41922a9db41f9677284f580544314e14f838641de7f81","abstract_canon_sha256":"0222fa8bc6a5d7a0f8798fb4923f40d5a8d18174acccbc98392e723e86def5e9"},"schema_version":"1.0"},"canonical_sha256":"2c9dce66066e6f2bb6119147ba0c2270da0c9279cc7c0eebba0ca4b733382670","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T01:05:06.594843Z","signature_b64":"fU+H8JuHQXYRpPyqu34Uo6pY+0KbHr//xbLPVOUZ9W6Mrr8b/55+Tm2LHaJ28eXM8EtroiV5/Wfd6zVuKP3/CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2c9dce66066e6f2bb6119147ba0c2270da0c9279cc7c0eebba0ca4b733382670","last_reissued_at":"2026-06-03T01:05:06.594345Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T01:05:06.594345Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2510.16282","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":"2026-06-03T01:05:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eiclSf+Mm1huUmunzn3/VqN9s1xHPSklRxnDf30TK0y2wwPLBvl8FjYD1OGSdeu87UxaL1ZPQ80+hGBp+YSMDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T22:57:41.822641Z"},"content_sha256":"486b770f2f0a7cbae919c73fc85e634cb035661603db5177d8342e0d47737a8e","schema_version":"1.0","event_id":"sha256:486b770f2f0a7cbae919c73fc85e634cb035661603db5177d8342e0d47737a8e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:FSO44ZQGNZXSXNQRSFD3UDBCOD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Instant Personalized Large Language Model Adaptation via Hypernetwork","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Fengran Mo, Haoyang Wen, Meng Jiang, Pei Chen, Qingkai Zeng, Qingyu Yin, Rongzhi Zhang, Zhaoxuan Tan, Zheng Li, Zheyuan Liu, Zixuan Zhang","submitted_at":"2025-10-18T00:41:25Z","abstract_excerpt":"Personalized large language models (LLMs) tailor content to individual preferences using user profiles or histories. However, existing parameter-efficient fine-tuning (PEFT) methods, such as the ``One-PEFT-Per-User'' (OPPU) paradigm, require training a separate adapter for each user, making them computationally expensive and impractical for real-time updates. We introduce Profile-to-PEFT, a scalable framework that employs a hypernetwork, trained end-to-end, to map a user's encoded profile directly to a full set of adapter parameters (e.g., LoRA), eliminating per-user training at deployment. Th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.16282","kind":"arxiv","version":2},"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/2510.16282/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":"2026-06-03T01:05:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gFysBbeBZTjuL2D5cTSurIA9lLYwH+f1F5G3+vERbLft7L3jlnDlpggN+QdiKLP7KwTWyAQRIomNDiER7GpUBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T22:57:41.823263Z"},"content_sha256":"4b3fab3c18c9aa8e1ad568b82d8e73fa89fcffe43b4bba3322e636c22b492ffd","schema_version":"1.0","event_id":"sha256:4b3fab3c18c9aa8e1ad568b82d8e73fa89fcffe43b4bba3322e636c22b492ffd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FSO44ZQGNZXSXNQRSFD3UDBCOD/bundle.json","state_url":"https://pith.science/pith/FSO44ZQGNZXSXNQRSFD3UDBCOD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FSO44ZQGNZXSXNQRSFD3UDBCOD/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":"2026-06-04T22:57:41Z","links":{"resolver":"https://pith.science/pith/FSO44ZQGNZXSXNQRSFD3UDBCOD","bundle":"https://pith.science/pith/FSO44ZQGNZXSXNQRSFD3UDBCOD/bundle.json","state":"https://pith.science/pith/FSO44ZQGNZXSXNQRSFD3UDBCOD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FSO44ZQGNZXSXNQRSFD3UDBCOD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:FSO44ZQGNZXSXNQRSFD3UDBCOD","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":"0222fa8bc6a5d7a0f8798fb4923f40d5a8d18174acccbc98392e723e86def5e9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-10-18T00:41:25Z","title_canon_sha256":"01c5d9de9d69e5d72fe41922a9db41f9677284f580544314e14f838641de7f81"},"schema_version":"1.0","source":{"id":"2510.16282","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.16282","created_at":"2026-06-03T01:05:06Z"},{"alias_kind":"arxiv_version","alias_value":"2510.16282v2","created_at":"2026-06-03T01:05:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.16282","created_at":"2026-06-03T01:05:06Z"},{"alias_kind":"pith_short_12","alias_value":"FSO44ZQGNZXS","created_at":"2026-06-03T01:05:06Z"},{"alias_kind":"pith_short_16","alias_value":"FSO44ZQGNZXSXNQR","created_at":"2026-06-03T01:05:06Z"},{"alias_kind":"pith_short_8","alias_value":"FSO44ZQG","created_at":"2026-06-03T01:05:06Z"}],"graph_snapshots":[{"event_id":"sha256:4b3fab3c18c9aa8e1ad568b82d8e73fa89fcffe43b4bba3322e636c22b492ffd","target":"graph","created_at":"2026-06-03T01:05:06Z","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/2510.16282/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Personalized large language models (LLMs) tailor content to individual preferences using user profiles or histories. However, existing parameter-efficient fine-tuning (PEFT) methods, such as the ``One-PEFT-Per-User'' (OPPU) paradigm, require training a separate adapter for each user, making them computationally expensive and impractical for real-time updates. We introduce Profile-to-PEFT, a scalable framework that employs a hypernetwork, trained end-to-end, to map a user's encoded profile directly to a full set of adapter parameters (e.g., LoRA), eliminating per-user training at deployment. Th","authors_text":"Fengran Mo, Haoyang Wen, Meng Jiang, Pei Chen, Qingkai Zeng, Qingyu Yin, Rongzhi Zhang, Zhaoxuan Tan, Zheng Li, Zheyuan Liu, Zixuan Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-10-18T00:41:25Z","title":"Instant Personalized Large Language Model Adaptation via Hypernetwork"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.16282","kind":"arxiv","version":2},"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":"sha256:486b770f2f0a7cbae919c73fc85e634cb035661603db5177d8342e0d47737a8e","target":"record","created_at":"2026-06-03T01:05:06Z","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":"0222fa8bc6a5d7a0f8798fb4923f40d5a8d18174acccbc98392e723e86def5e9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-10-18T00:41:25Z","title_canon_sha256":"01c5d9de9d69e5d72fe41922a9db41f9677284f580544314e14f838641de7f81"},"schema_version":"1.0","source":{"id":"2510.16282","kind":"arxiv","version":2}},"canonical_sha256":"2c9dce66066e6f2bb6119147ba0c2270da0c9279cc7c0eebba0ca4b733382670","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2c9dce66066e6f2bb6119147ba0c2270da0c9279cc7c0eebba0ca4b733382670","first_computed_at":"2026-06-03T01:05:06.594345Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-03T01:05:06.594345Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fU+H8JuHQXYRpPyqu34Uo6pY+0KbHr//xbLPVOUZ9W6Mrr8b/55+Tm2LHaJ28eXM8EtroiV5/Wfd6zVuKP3/CA==","signature_status":"signed_v1","signed_at":"2026-06-03T01:05:06.594843Z","signed_message":"canonical_sha256_bytes"},"source_id":"2510.16282","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:486b770f2f0a7cbae919c73fc85e634cb035661603db5177d8342e0d47737a8e","sha256:4b3fab3c18c9aa8e1ad568b82d8e73fa89fcffe43b4bba3322e636c22b492ffd"],"state_sha256":"82f369019a09727562d3df58e74b46d1c3ede1fd47ea58b4e580c72aa34a1073"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0Ns8+pZVZn3oZmiufDGF6d20VRopNDitqdHoM2dGS58ai/bqt7QPdO7QYpQ/bDSPMXYtf+Kbtq8kEt5HDgYBDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T22:57:41.826871Z","bundle_sha256":"aab3fe9e596d177ccfb153563eff1e1606375a1cbfadf8e750d1f5aa2083a0d7"}}