{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:GJDUKKCYCCHFPKXETYXM24BIBY","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":"11e0475ea1baabd99309efd3dea3f479b9cb872a8148c97a7985e48da9578703","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-19T15:28:29Z","title_canon_sha256":"2dc3cbae2482376c9ca497cded0ee6e949ccb6c187c0b5409d060ae9b201ac81"},"schema_version":"1.0","source":{"id":"2406.13632","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.13632","created_at":"2026-07-05T11:43:55Z"},{"alias_kind":"arxiv_version","alias_value":"2406.13632v4","created_at":"2026-07-05T11:43:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.13632","created_at":"2026-07-05T11:43:55Z"},{"alias_kind":"pith_short_12","alias_value":"GJDUKKCYCCHF","created_at":"2026-07-05T11:43:55Z"},{"alias_kind":"pith_short_16","alias_value":"GJDUKKCYCCHFPKXE","created_at":"2026-07-05T11:43:55Z"},{"alias_kind":"pith_short_8","alias_value":"GJDUKKCY","created_at":"2026-07-05T11:43:55Z"}],"graph_snapshots":[{"event_id":"sha256:f47a9b29bf33523f5c65ce8585e7d348ca92b726e43134feafa2218b44d0ee4c","target":"graph","created_at":"2026-07-05T11:43:55Z","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/2406.13632/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Despite recent advancements in Large Language Models (LLMs), their performance on tasks involving long contexts remains sub-optimal. In this work, we propose DoubleDipper, a novel In-Context-Learning method that automatically generates few-shot examples for long context QA tasks by recycling contexts. Specifically, given a long input context (1-3k tokens) and a query, we generate additional query-output pairs from the given context as few-shot examples, while introducing the context only once. This ensures that the demonstrations are leveraging the same context as the target query while only a","authors_text":"Alex Fabrikant, Alon Jacovi, Arie Cattan, Avi Caciularu, Avinatan Hassidim, Dror Marcus, Hannah Rashkin, Idan Szpektor, Jonathan Herzig, Roee Aharoni, Yossi Matias","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-19T15:28:29Z","title":"DoubleDipper: Improving Long-Context LLMs via Context Recycling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.13632","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":"sha256:249421f0062d3fa35a60829375d20a1b8005e1b48eab5ffbf1872d74fff810cd","target":"record","created_at":"2026-07-05T11:43:55Z","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":"11e0475ea1baabd99309efd3dea3f479b9cb872a8148c97a7985e48da9578703","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-19T15:28:29Z","title_canon_sha256":"2dc3cbae2482376c9ca497cded0ee6e949ccb6c187c0b5409d060ae9b201ac81"},"schema_version":"1.0","source":{"id":"2406.13632","kind":"arxiv","version":4}},"canonical_sha256":"3247452858108e57aae49e2ecd70280e31717fa74125f7309cd9ce987aa67ce2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3247452858108e57aae49e2ecd70280e31717fa74125f7309cd9ce987aa67ce2","first_computed_at":"2026-07-05T11:43:55.703553Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:43:55.703553Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LjY2Eqy97uI9mbDeRMHXZBCrjsOGaHYCD2oaL5YwlnDahVZiBg3yJVKBHP46J5vSECJDo+Dp8JoHEC2uLXZ7Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:43:55.704042Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.13632","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:249421f0062d3fa35a60829375d20a1b8005e1b48eab5ffbf1872d74fff810cd","sha256:f47a9b29bf33523f5c65ce8585e7d348ca92b726e43134feafa2218b44d0ee4c"],"state_sha256":"e2de28164a52f8d9f6dc7eca452560a97e99438933b14c7326283f7e0a9ba18a"}