{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:3Y5E44A47FHW35X6RLHSKBKC36","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":"6e8cb3e3a3e096e247413321346735d4f07835baf968f6c9b1d03bda2ea63ba1","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-03-11T01:07:57Z","title_canon_sha256":"5b574cc996a757c7f9fd63a884a88b280711829c6e83d4e0bbd912a03863f15c"},"schema_version":"1.0","source":{"id":"2503.08727","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.08727","created_at":"2026-07-05T11:50:33Z"},{"alias_kind":"arxiv_version","alias_value":"2503.08727v4","created_at":"2026-07-05T11:50:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.08727","created_at":"2026-07-05T11:50:33Z"},{"alias_kind":"pith_short_12","alias_value":"3Y5E44A47FHW","created_at":"2026-07-05T11:50:33Z"},{"alias_kind":"pith_short_16","alias_value":"3Y5E44A47FHW35X6","created_at":"2026-07-05T11:50:33Z"},{"alias_kind":"pith_short_8","alias_value":"3Y5E44A4","created_at":"2026-07-05T11:50:33Z"}],"graph_snapshots":[{"event_id":"sha256:c2f8e36ce6852a8ae9a440167b09510ba3d09f70131552779ef7e72a0ed6137b","target":"graph","created_at":"2026-07-05T11:50:33Z","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/2503.08727/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Dynamically integrating new or rapidly evolving information after (Large) Language Model pre-training remains challenging, particularly in low-data scenarios or when dealing with private and specialized documents. In-context learning and retrieval-augmented generation (RAG) face limitations, including their high inference costs and their inability to capture global document information. In this paper, we propose a way of modularizing knowledge by training document-level Knowledge Modules (KMs). KMs are lightweight components implemented as parameter-efficient LoRA modules, which are trained to","authors_text":"Alan Ansell, Alessandro Sordoni, Edoardo Ponti, Ivan Vuli\\'c, Lucas Caccia","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-03-11T01:07:57Z","title":"Training Plug-n-Play Knowledge Modules with Deep Context Distillation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.08727","kind":"arxiv","version":4},"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":"sha256:9a582ea3db794c20c65b363db5d11856cdd14d9920255b3f82c3101aa877aa5a","target":"record","created_at":"2026-07-05T11:50:33Z","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":"6e8cb3e3a3e096e247413321346735d4f07835baf968f6c9b1d03bda2ea63ba1","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-03-11T01:07:57Z","title_canon_sha256":"5b574cc996a757c7f9fd63a884a88b280711829c6e83d4e0bbd912a03863f15c"},"schema_version":"1.0","source":{"id":"2503.08727","kind":"arxiv","version":4}},"canonical_sha256":"de3a4e701cf94f6df6fe8acf250542dfad27aca310e34defa8cb2615c4bc11dc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"de3a4e701cf94f6df6fe8acf250542dfad27aca310e34defa8cb2615c4bc11dc","first_computed_at":"2026-07-05T11:50:33.869080Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:50:33.869080Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"b1emykneo2qpoF3GccnuNKim8busl9oUCWBzN7NDrwhhD+SsXeiWJTWDQK8FDKXKPuYgFYHqG4wKo01qkhIaCA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:50:33.869588Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.08727","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9a582ea3db794c20c65b363db5d11856cdd14d9920255b3f82c3101aa877aa5a","sha256:c2f8e36ce6852a8ae9a440167b09510ba3d09f70131552779ef7e72a0ed6137b"],"state_sha256":"4b83699364977f0a37a8af73e6e88c44dfe442cecd255f4bbaba03ff6d8cf98b"}