{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:HEENF6SKPPV6S374GD3ZWGTFL3","short_pith_number":"pith:HEENF6SK","canonical_record":{"source":{"id":"2408.00960","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-02T00:24:22Z","cross_cats_sorted":["cs.AI","cs.IR"],"title_canon_sha256":"55fed668a9987e422d993e3d8b3dc9eb0c980ac4e03a74486947f26fe8f641ae","abstract_canon_sha256":"6c29490c8c3d63eb9293fa37b2c6ed1aaddf1f9c75a692e498ea9eca6507655d"},"schema_version":"1.0"},"canonical_sha256":"3908d2fa4a7bebe96ffc30f79b1a655ee8763d36cdfd68daa634c10dbe0d2a53","source":{"kind":"arxiv","id":"2408.00960","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.00960","created_at":"2026-07-05T08:51:24Z"},{"alias_kind":"arxiv_version","alias_value":"2408.00960v1","created_at":"2026-07-05T08:51:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.00960","created_at":"2026-07-05T08:51:24Z"},{"alias_kind":"pith_short_12","alias_value":"HEENF6SKPPV6","created_at":"2026-07-05T08:51:24Z"},{"alias_kind":"pith_short_16","alias_value":"HEENF6SKPPV6S374","created_at":"2026-07-05T08:51:24Z"},{"alias_kind":"pith_short_8","alias_value":"HEENF6SK","created_at":"2026-07-05T08:51:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:HEENF6SKPPV6S374GD3ZWGTFL3","target":"record","payload":{"canonical_record":{"source":{"id":"2408.00960","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-02T00:24:22Z","cross_cats_sorted":["cs.AI","cs.IR"],"title_canon_sha256":"55fed668a9987e422d993e3d8b3dc9eb0c980ac4e03a74486947f26fe8f641ae","abstract_canon_sha256":"6c29490c8c3d63eb9293fa37b2c6ed1aaddf1f9c75a692e498ea9eca6507655d"},"schema_version":"1.0"},"canonical_sha256":"3908d2fa4a7bebe96ffc30f79b1a655ee8763d36cdfd68daa634c10dbe0d2a53","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:51:24.736015Z","signature_b64":"/PQvRFYmZtmCaeD8Og/m/2nwqNxnsK0+reQkOWwzXf0X3Er9v4GNTg81d+j2hwLbFjPYOIi2pq31mF0rRJpHBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3908d2fa4a7bebe96ffc30f79b1a655ee8763d36cdfd68daa634c10dbe0d2a53","last_reissued_at":"2026-07-05T08:51:24.735538Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:51:24.735538Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2408.00960","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":"2026-07-05T08:51:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YcsteLuuDQCcF4y5xiZRSG7031kF4w2P5x1Xu9jCNUsykCUnnLmVuZBcyROYrsYiouPfQZF2AZp2G0LO+A2CBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T10:20:09.461538Z"},"content_sha256":"88307202d99721dcda656ffe4e8a492443eac4789b90fd7d201610af39934c3e","schema_version":"1.0","event_id":"sha256:88307202d99721dcda656ffe4e8a492443eac4789b90fd7d201610af39934c3e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:HEENF6SKPPV6S374GD3ZWGTFL3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"PERSOMA: PERsonalized SOft ProMpt Adapter Architecture for Personalized Language Prompting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.IR"],"primary_cat":"cs.CL","authors_text":"Alexandros Karatzoglou, Ambarish Jash, Dima Kuzmin, Krishna Sayana, Liam Hebert, Sukhdeep Sodhi, Sumanth Doddapaneni, Yanli Cai","submitted_at":"2024-08-02T00:24:22Z","abstract_excerpt":"Understanding the nuances of a user's extensive interaction history is key to building accurate and personalized natural language systems that can adapt to evolving user preferences. To address this, we introduce PERSOMA, Personalized Soft Prompt Adapter architecture. Unlike previous personalized prompting methods for large language models, PERSOMA offers a novel approach to efficiently capture user history. It achieves this by resampling and compressing interactions as free form text into expressive soft prompt embeddings, building upon recent research utilizing embedding representations as i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.00960","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/2408.00960/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":"2026-07-05T08:51:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"b0ceZ7+clWx2RDK+VAM/rHJ9Fshp7Ma615qlDxE0/GOlfHSiCNKygWPywLJXP92AF4pboQLrwEyqxxaw/47OBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T10:20:09.461950Z"},"content_sha256":"8846d98563147b793ffb1302ff1517141ea9b6a10a6067055f8e1f5e06b81d55","schema_version":"1.0","event_id":"sha256:8846d98563147b793ffb1302ff1517141ea9b6a10a6067055f8e1f5e06b81d55"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HEENF6SKPPV6S374GD3ZWGTFL3/bundle.json","state_url":"https://pith.science/pith/HEENF6SKPPV6S374GD3ZWGTFL3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HEENF6SKPPV6S374GD3ZWGTFL3/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":"2026-08-01T10:20:09Z","links":{"resolver":"https://pith.science/pith/HEENF6SKPPV6S374GD3ZWGTFL3","bundle":"https://pith.science/pith/HEENF6SKPPV6S374GD3ZWGTFL3/bundle.json","state":"https://pith.science/pith/HEENF6SKPPV6S374GD3ZWGTFL3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HEENF6SKPPV6S374GD3ZWGTFL3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:HEENF6SKPPV6S374GD3ZWGTFL3","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":"6c29490c8c3d63eb9293fa37b2c6ed1aaddf1f9c75a692e498ea9eca6507655d","cross_cats_sorted":["cs.AI","cs.IR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-02T00:24:22Z","title_canon_sha256":"55fed668a9987e422d993e3d8b3dc9eb0c980ac4e03a74486947f26fe8f641ae"},"schema_version":"1.0","source":{"id":"2408.00960","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.00960","created_at":"2026-07-05T08:51:24Z"},{"alias_kind":"arxiv_version","alias_value":"2408.00960v1","created_at":"2026-07-05T08:51:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.00960","created_at":"2026-07-05T08:51:24Z"},{"alias_kind":"pith_short_12","alias_value":"HEENF6SKPPV6","created_at":"2026-07-05T08:51:24Z"},{"alias_kind":"pith_short_16","alias_value":"HEENF6SKPPV6S374","created_at":"2026-07-05T08:51:24Z"},{"alias_kind":"pith_short_8","alias_value":"HEENF6SK","created_at":"2026-07-05T08:51:24Z"}],"graph_snapshots":[{"event_id":"sha256:8846d98563147b793ffb1302ff1517141ea9b6a10a6067055f8e1f5e06b81d55","target":"graph","created_at":"2026-07-05T08:51:24Z","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/2408.00960/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Understanding the nuances of a user's extensive interaction history is key to building accurate and personalized natural language systems that can adapt to evolving user preferences. To address this, we introduce PERSOMA, Personalized Soft Prompt Adapter architecture. Unlike previous personalized prompting methods for large language models, PERSOMA offers a novel approach to efficiently capture user history. It achieves this by resampling and compressing interactions as free form text into expressive soft prompt embeddings, building upon recent research utilizing embedding representations as i","authors_text":"Alexandros Karatzoglou, Ambarish Jash, Dima Kuzmin, Krishna Sayana, Liam Hebert, Sukhdeep Sodhi, Sumanth Doddapaneni, Yanli Cai","cross_cats":["cs.AI","cs.IR"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-02T00:24:22Z","title":"PERSOMA: PERsonalized SOft ProMpt Adapter Architecture for Personalized Language Prompting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.00960","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":"sha256:88307202d99721dcda656ffe4e8a492443eac4789b90fd7d201610af39934c3e","target":"record","created_at":"2026-07-05T08:51:24Z","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":"6c29490c8c3d63eb9293fa37b2c6ed1aaddf1f9c75a692e498ea9eca6507655d","cross_cats_sorted":["cs.AI","cs.IR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-02T00:24:22Z","title_canon_sha256":"55fed668a9987e422d993e3d8b3dc9eb0c980ac4e03a74486947f26fe8f641ae"},"schema_version":"1.0","source":{"id":"2408.00960","kind":"arxiv","version":1}},"canonical_sha256":"3908d2fa4a7bebe96ffc30f79b1a655ee8763d36cdfd68daa634c10dbe0d2a53","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3908d2fa4a7bebe96ffc30f79b1a655ee8763d36cdfd68daa634c10dbe0d2a53","first_computed_at":"2026-07-05T08:51:24.735538Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:51:24.735538Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/PQvRFYmZtmCaeD8Og/m/2nwqNxnsK0+reQkOWwzXf0X3Er9v4GNTg81d+j2hwLbFjPYOIi2pq31mF0rRJpHBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:51:24.736015Z","signed_message":"canonical_sha256_bytes"},"source_id":"2408.00960","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:88307202d99721dcda656ffe4e8a492443eac4789b90fd7d201610af39934c3e","sha256:8846d98563147b793ffb1302ff1517141ea9b6a10a6067055f8e1f5e06b81d55"],"state_sha256":"5e32915b29d555938e9799bde1650fa00761fec72ff6c8606dbd0d8cc41194e3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+4VN4pXcSd9qCYl5V1MBH48CGjmhdQ3EYSYKTgs9fat+e3H4OGxG5kQflXKZnWdLADy/QVGHaqD0HQqQExKcCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-01T10:20:09.464676Z","bundle_sha256":"811996e7a5bf6daef7f34c1fa0b660506d13ec5576b64e16272dbbb32eb86a9b"}}