{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:L2R4HGDUTQC22OOIEA24JWTXNS","short_pith_number":"pith:L2R4HGDU","canonical_record":{"source":{"id":"2501.03276","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-05T09:57:03Z","cross_cats_sorted":["cs.AI","cs.IR","cs.LG"],"title_canon_sha256":"770fd061ab0a6bb242cf4857cf77066c0a89794b0e687a91b60196df911f1b18","abstract_canon_sha256":"4ccbbddedcb5eda76158f5ce5bbaf25b35a254d02be5f30fea4272e05e4bec77"},"schema_version":"1.0"},"canonical_sha256":"5ea3c398749c05ad39c82035c4da776cbd3066587140aa9d9d6cf82a80b41632","source":{"kind":"arxiv","id":"2501.03276","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.03276","created_at":"2026-07-05T09:57:51Z"},{"alias_kind":"arxiv_version","alias_value":"2501.03276v1","created_at":"2026-07-05T09:57:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.03276","created_at":"2026-07-05T09:57:51Z"},{"alias_kind":"pith_short_12","alias_value":"L2R4HGDUTQC2","created_at":"2026-07-05T09:57:51Z"},{"alias_kind":"pith_short_16","alias_value":"L2R4HGDUTQC22OOI","created_at":"2026-07-05T09:57:51Z"},{"alias_kind":"pith_short_8","alias_value":"L2R4HGDU","created_at":"2026-07-05T09:57:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:L2R4HGDUTQC22OOIEA24JWTXNS","target":"record","payload":{"canonical_record":{"source":{"id":"2501.03276","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-05T09:57:03Z","cross_cats_sorted":["cs.AI","cs.IR","cs.LG"],"title_canon_sha256":"770fd061ab0a6bb242cf4857cf77066c0a89794b0e687a91b60196df911f1b18","abstract_canon_sha256":"4ccbbddedcb5eda76158f5ce5bbaf25b35a254d02be5f30fea4272e05e4bec77"},"schema_version":"1.0"},"canonical_sha256":"5ea3c398749c05ad39c82035c4da776cbd3066587140aa9d9d6cf82a80b41632","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:57:51.671575Z","signature_b64":"VkgwRobiukuuLFOTqDpJNn79fJtQjzb5+BdP5RZaVtfrz43h5bGKS0AOyjnlnlUVAolXZcrWS4cogj1pMFYcCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ea3c398749c05ad39c82035c4da776cbd3066587140aa9d9d6cf82a80b41632","last_reissued_at":"2026-07-05T09:57:51.671143Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:57:51.671143Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.03276","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":"2026-07-05T09:57:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"723D3PRoj1LGtDxxhl++3rAvfCby4kmICitZuATuX7D6uRGRgL/yy5SSMM2H+2Eps1c+leFfNwBBFFRNLurhDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T20:38:09.381430Z"},"content_sha256":"45d1847d91e5b57035de1d98ad1de0020a3fb3840b6c2e955f0878c8bb127ef8","schema_version":"1.0","event_id":"sha256:45d1847d91e5b57035de1d98ad1de0020a3fb3840b6c2e955f0878c8bb127ef8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:L2R4HGDUTQC22OOIEA24JWTXNS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ComMer: a Framework for Compressing and Merging User Data for Personalization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.IR","cs.LG"],"primary_cat":"cs.CL","authors_text":"Amir Zait, Danny Karmon, Efrat Farkash, Ilia Labzovsky, Yoel Zeldes","submitted_at":"2025-01-05T09:57:03Z","abstract_excerpt":"Large Language Models (LLMs) excel at a wide range of tasks, but adapting them to new data, particularly for personalized applications, poses significant challenges due to resource and computational constraints. Existing methods either rely on exposing fresh data to the model through the prompt, which is limited by context size and computationally expensive at inference time, or fine-tuning, which incurs substantial training and update costs. In this paper, we introduce ComMer - Compress and Merge - a novel framework that efficiently personalizes LLMs by compressing users' documents into compa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.03276","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/2501.03276/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":"2026-07-05T09:57:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XkrbPFmrilvVpLdF7SGbzfJMThM3/zTgAn7r1onjupo0TCXMdl9FCuGd/TkvCyCkwH+RKKCHDe6xQZBydEJKBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T20:38:09.381971Z"},"content_sha256":"a8b9367fba63ef2981585c94a1ba62757692e2aedae4154fa2060cb90f837596","schema_version":"1.0","event_id":"sha256:a8b9367fba63ef2981585c94a1ba62757692e2aedae4154fa2060cb90f837596"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/L2R4HGDUTQC22OOIEA24JWTXNS/bundle.json","state_url":"https://pith.science/pith/L2R4HGDUTQC22OOIEA24JWTXNS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/L2R4HGDUTQC22OOIEA24JWTXNS/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":"2026-08-11T20:38:09Z","links":{"resolver":"https://pith.science/pith/L2R4HGDUTQC22OOIEA24JWTXNS","bundle":"https://pith.science/pith/L2R4HGDUTQC22OOIEA24JWTXNS/bundle.json","state":"https://pith.science/pith/L2R4HGDUTQC22OOIEA24JWTXNS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/L2R4HGDUTQC22OOIEA24JWTXNS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:L2R4HGDUTQC22OOIEA24JWTXNS","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":"4ccbbddedcb5eda76158f5ce5bbaf25b35a254d02be5f30fea4272e05e4bec77","cross_cats_sorted":["cs.AI","cs.IR","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-05T09:57:03Z","title_canon_sha256":"770fd061ab0a6bb242cf4857cf77066c0a89794b0e687a91b60196df911f1b18"},"schema_version":"1.0","source":{"id":"2501.03276","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.03276","created_at":"2026-07-05T09:57:51Z"},{"alias_kind":"arxiv_version","alias_value":"2501.03276v1","created_at":"2026-07-05T09:57:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.03276","created_at":"2026-07-05T09:57:51Z"},{"alias_kind":"pith_short_12","alias_value":"L2R4HGDUTQC2","created_at":"2026-07-05T09:57:51Z"},{"alias_kind":"pith_short_16","alias_value":"L2R4HGDUTQC22OOI","created_at":"2026-07-05T09:57:51Z"},{"alias_kind":"pith_short_8","alias_value":"L2R4HGDU","created_at":"2026-07-05T09:57:51Z"}],"graph_snapshots":[{"event_id":"sha256:a8b9367fba63ef2981585c94a1ba62757692e2aedae4154fa2060cb90f837596","target":"graph","created_at":"2026-07-05T09:57:51Z","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/2501.03276/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) excel at a wide range of tasks, but adapting them to new data, particularly for personalized applications, poses significant challenges due to resource and computational constraints. Existing methods either rely on exposing fresh data to the model through the prompt, which is limited by context size and computationally expensive at inference time, or fine-tuning, which incurs substantial training and update costs. In this paper, we introduce ComMer - Compress and Merge - a novel framework that efficiently personalizes LLMs by compressing users' documents into compa","authors_text":"Amir Zait, Danny Karmon, Efrat Farkash, Ilia Labzovsky, Yoel Zeldes","cross_cats":["cs.AI","cs.IR","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-05T09:57:03Z","title":"ComMer: a Framework for Compressing and Merging User Data for Personalization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.03276","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":"sha256:45d1847d91e5b57035de1d98ad1de0020a3fb3840b6c2e955f0878c8bb127ef8","target":"record","created_at":"2026-07-05T09:57:51Z","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":"4ccbbddedcb5eda76158f5ce5bbaf25b35a254d02be5f30fea4272e05e4bec77","cross_cats_sorted":["cs.AI","cs.IR","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-05T09:57:03Z","title_canon_sha256":"770fd061ab0a6bb242cf4857cf77066c0a89794b0e687a91b60196df911f1b18"},"schema_version":"1.0","source":{"id":"2501.03276","kind":"arxiv","version":1}},"canonical_sha256":"5ea3c398749c05ad39c82035c4da776cbd3066587140aa9d9d6cf82a80b41632","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5ea3c398749c05ad39c82035c4da776cbd3066587140aa9d9d6cf82a80b41632","first_computed_at":"2026-07-05T09:57:51.671143Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:57:51.671143Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VkgwRobiukuuLFOTqDpJNn79fJtQjzb5+BdP5RZaVtfrz43h5bGKS0AOyjnlnlUVAolXZcrWS4cogj1pMFYcCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:57:51.671575Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.03276","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:45d1847d91e5b57035de1d98ad1de0020a3fb3840b6c2e955f0878c8bb127ef8","sha256:a8b9367fba63ef2981585c94a1ba62757692e2aedae4154fa2060cb90f837596"],"state_sha256":"61fd42ed55185de369c2a800b9017dca4681b8123ce5455839f05e772ec2b8ee"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"spFg+5dB4J6+UkLoUymZHoHKTTEFz0gpqaFE27JrL55/DX+HD2jYCPbK97iyhNyfaqyKnOl38cHGQcRQ+8nPBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T20:38:09.387510Z","bundle_sha256":"d4e9c67d328fcec3e0f9cd38c25984bffd02144c38e483ace4515607302bc290"}}