{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:2GEEXPGGNC5Y7YEGNIBA7AF2SN","short_pith_number":"pith:2GEEXPGG","canonical_record":{"source":{"id":"2506.07440","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-09T05:33:28Z","cross_cats_sorted":[],"title_canon_sha256":"b4144bdf1920b53992ff0b293c39d9a93d5cedfa0edafe8003e10b7a1986c2d2","abstract_canon_sha256":"648c3f014a5aa87e124fe363b9c0c19ca896c73ab13ec6826481986243bd0bec"},"schema_version":"1.0"},"canonical_sha256":"d1884bbcc668bb8fe0866a020f80ba935a9643a6f27ade20fc730bc89131871b","source":{"kind":"arxiv","id":"2506.07440","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.07440","created_at":"2026-07-05T11:18:27Z"},{"alias_kind":"arxiv_version","alias_value":"2506.07440v1","created_at":"2026-07-05T11:18:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.07440","created_at":"2026-07-05T11:18:27Z"},{"alias_kind":"pith_short_12","alias_value":"2GEEXPGGNC5Y","created_at":"2026-07-05T11:18:27Z"},{"alias_kind":"pith_short_16","alias_value":"2GEEXPGGNC5Y7YEG","created_at":"2026-07-05T11:18:27Z"},{"alias_kind":"pith_short_8","alias_value":"2GEEXPGG","created_at":"2026-07-05T11:18:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:2GEEXPGGNC5Y7YEGNIBA7AF2SN","target":"record","payload":{"canonical_record":{"source":{"id":"2506.07440","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-09T05:33:28Z","cross_cats_sorted":[],"title_canon_sha256":"b4144bdf1920b53992ff0b293c39d9a93d5cedfa0edafe8003e10b7a1986c2d2","abstract_canon_sha256":"648c3f014a5aa87e124fe363b9c0c19ca896c73ab13ec6826481986243bd0bec"},"schema_version":"1.0"},"canonical_sha256":"d1884bbcc668bb8fe0866a020f80ba935a9643a6f27ade20fc730bc89131871b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:18:27.266413Z","signature_b64":"GxsEC4iNoodCpbQKjVvjxPBiT7N0skXLXKeebKgVYRsIBL8bQPpMo8JGWUvv4KtkzOq2mAN3SO6AHg9Y0cvpAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d1884bbcc668bb8fe0866a020f80ba935a9643a6f27ade20fc730bc89131871b","last_reissued_at":"2026-07-05T11:18:27.265812Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:18:27.265812Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.07440","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":"2026-07-05T11:18:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IWjcOD598sLStzsigYwpdvawVGIq9Ji0hYjP1O36XLMp2Qy9aj7KfCuEiHcc4rF25nRs5BT4/GUsx3pNWsO5BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T13:06:03.539630Z"},"content_sha256":"4974e5b1bf00d19a818e0567cf8b7dcba663816313cc60fc15a932cf9d379d5f","schema_version":"1.0","event_id":"sha256:4974e5b1bf00d19a818e0567cf8b7dcba663816313cc60fc15a932cf9d379d5f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:2GEEXPGGNC5Y7YEGNIBA7AF2SN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Federated In-Context Learning: Iterative Refinement for Improved Answer Quality","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chengkai Huang, Dongruo Zhou, John C.S. Lui, Lina Yao, Ruhan Wang, Rui Wang, Tong Yu, Zhiyong Wang","submitted_at":"2025-06-09T05:33:28Z","abstract_excerpt":"For question-answering (QA) tasks, in-context learning (ICL) enables language models to generate responses without modifying their parameters by leveraging examples provided in the input. However, the effectiveness of ICL heavily depends on the availability of high-quality examples, which are often scarce due to data privacy constraints, annotation costs, and distribution disparities. A natural solution is to utilize examples stored on client devices, but existing approaches either require transmitting model parameters - incurring significant communication overhead - or fail to fully exploit l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.07440","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/2506.07440/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":"2026-07-05T11:18:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jwNauafJNU/1DsCnihCY2kCTuJNyQvaqCtjphmsmZV089QXyRefpP02tKxoxemhHfHkp+et08MzcHsriJPuaCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T13:06:03.540416Z"},"content_sha256":"cf66cedf87b9304e90fce3e0df93a4853de5215a8e0c560c760651fe84c5701b","schema_version":"1.0","event_id":"sha256:cf66cedf87b9304e90fce3e0df93a4853de5215a8e0c560c760651fe84c5701b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2GEEXPGGNC5Y7YEGNIBA7AF2SN/bundle.json","state_url":"https://pith.science/pith/2GEEXPGGNC5Y7YEGNIBA7AF2SN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2GEEXPGGNC5Y7YEGNIBA7AF2SN/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":"2026-07-07T13:06:03Z","links":{"resolver":"https://pith.science/pith/2GEEXPGGNC5Y7YEGNIBA7AF2SN","bundle":"https://pith.science/pith/2GEEXPGGNC5Y7YEGNIBA7AF2SN/bundle.json","state":"https://pith.science/pith/2GEEXPGGNC5Y7YEGNIBA7AF2SN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2GEEXPGGNC5Y7YEGNIBA7AF2SN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:2GEEXPGGNC5Y7YEGNIBA7AF2SN","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":"648c3f014a5aa87e124fe363b9c0c19ca896c73ab13ec6826481986243bd0bec","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-09T05:33:28Z","title_canon_sha256":"b4144bdf1920b53992ff0b293c39d9a93d5cedfa0edafe8003e10b7a1986c2d2"},"schema_version":"1.0","source":{"id":"2506.07440","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.07440","created_at":"2026-07-05T11:18:27Z"},{"alias_kind":"arxiv_version","alias_value":"2506.07440v1","created_at":"2026-07-05T11:18:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.07440","created_at":"2026-07-05T11:18:27Z"},{"alias_kind":"pith_short_12","alias_value":"2GEEXPGGNC5Y","created_at":"2026-07-05T11:18:27Z"},{"alias_kind":"pith_short_16","alias_value":"2GEEXPGGNC5Y7YEG","created_at":"2026-07-05T11:18:27Z"},{"alias_kind":"pith_short_8","alias_value":"2GEEXPGG","created_at":"2026-07-05T11:18:27Z"}],"graph_snapshots":[{"event_id":"sha256:cf66cedf87b9304e90fce3e0df93a4853de5215a8e0c560c760651fe84c5701b","target":"graph","created_at":"2026-07-05T11:18:27Z","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/2506.07440/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"For question-answering (QA) tasks, in-context learning (ICL) enables language models to generate responses without modifying their parameters by leveraging examples provided in the input. However, the effectiveness of ICL heavily depends on the availability of high-quality examples, which are often scarce due to data privacy constraints, annotation costs, and distribution disparities. A natural solution is to utilize examples stored on client devices, but existing approaches either require transmitting model parameters - incurring significant communication overhead - or fail to fully exploit l","authors_text":"Chengkai Huang, Dongruo Zhou, John C.S. Lui, Lina Yao, Ruhan Wang, Rui Wang, Tong Yu, Zhiyong Wang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-09T05:33:28Z","title":"Federated In-Context Learning: Iterative Refinement for Improved Answer Quality"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.07440","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":"sha256:4974e5b1bf00d19a818e0567cf8b7dcba663816313cc60fc15a932cf9d379d5f","target":"record","created_at":"2026-07-05T11:18:27Z","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":"648c3f014a5aa87e124fe363b9c0c19ca896c73ab13ec6826481986243bd0bec","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-09T05:33:28Z","title_canon_sha256":"b4144bdf1920b53992ff0b293c39d9a93d5cedfa0edafe8003e10b7a1986c2d2"},"schema_version":"1.0","source":{"id":"2506.07440","kind":"arxiv","version":1}},"canonical_sha256":"d1884bbcc668bb8fe0866a020f80ba935a9643a6f27ade20fc730bc89131871b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d1884bbcc668bb8fe0866a020f80ba935a9643a6f27ade20fc730bc89131871b","first_computed_at":"2026-07-05T11:18:27.265812Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:18:27.265812Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GxsEC4iNoodCpbQKjVvjxPBiT7N0skXLXKeebKgVYRsIBL8bQPpMo8JGWUvv4KtkzOq2mAN3SO6AHg9Y0cvpAA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:18:27.266413Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.07440","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4974e5b1bf00d19a818e0567cf8b7dcba663816313cc60fc15a932cf9d379d5f","sha256:cf66cedf87b9304e90fce3e0df93a4853de5215a8e0c560c760651fe84c5701b"],"state_sha256":"c45d990a81bff0b459cb43d6e8806d2297e302daf7aeb3e77444d0f6288c50a5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"66W9pKk2Dx9LGWkj5IFs1p1z+jB9eXnWDBZ0uG+2Vfy0IGv5cKtJIgMK1tNCOM1kWPpkrUIHqUDYqODFECIqCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T13:06:03.544824Z","bundle_sha256":"1b373b6e211ce98c0c3dafc5209e51b11f8370ae73d677117ef55f8f0187cb1a"}}