{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:CUDQZ73M4K37T4XLYYDUCVAHQO","short_pith_number":"pith:CUDQZ73M","canonical_record":{"source":{"id":"2410.23136","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-10-30T15:48:36Z","cross_cats_sorted":[],"title_canon_sha256":"9340dc525bd2ef2411ca210134b83d5408f47d174a975702538292d41b5b563f","abstract_canon_sha256":"c44071dcd9014b5efddd612f9db098ddeb0fdd50444107e3908250d40b090503"},"schema_version":"1.0"},"canonical_sha256":"15070cff6ce2b7f9f2ebc60741540783a5cfbaf64325576ef158dd296ee4fc47","source":{"kind":"arxiv","id":"2410.23136","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.23136","created_at":"2026-07-05T09:28:41Z"},{"alias_kind":"arxiv_version","alias_value":"2410.23136v1","created_at":"2026-07-05T09:28:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.23136","created_at":"2026-07-05T09:28:41Z"},{"alias_kind":"pith_short_12","alias_value":"CUDQZ73M4K37","created_at":"2026-07-05T09:28:41Z"},{"alias_kind":"pith_short_16","alias_value":"CUDQZ73M4K37T4XL","created_at":"2026-07-05T09:28:41Z"},{"alias_kind":"pith_short_8","alias_value":"CUDQZ73M","created_at":"2026-07-05T09:28:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:CUDQZ73M4K37T4XLYYDUCVAHQO","target":"record","payload":{"canonical_record":{"source":{"id":"2410.23136","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-10-30T15:48:36Z","cross_cats_sorted":[],"title_canon_sha256":"9340dc525bd2ef2411ca210134b83d5408f47d174a975702538292d41b5b563f","abstract_canon_sha256":"c44071dcd9014b5efddd612f9db098ddeb0fdd50444107e3908250d40b090503"},"schema_version":"1.0"},"canonical_sha256":"15070cff6ce2b7f9f2ebc60741540783a5cfbaf64325576ef158dd296ee4fc47","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:28:41.451980Z","signature_b64":"qEaSollaGbr3zPQJxc1eNGPXO7fwdxg/G1zk8tcInxTW2CdoKJr6bv8iNGoEvEfgnwgphqZmYIIjkMsIx7vYDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"15070cff6ce2b7f9f2ebc60741540783a5cfbaf64325576ef158dd296ee4fc47","last_reissued_at":"2026-07-05T09:28:41.451564Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:28:41.451564Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.23136","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":"2026-07-05T09:28:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sVrYCigrTaTWC9P9ox/EOlK6yiF8WRukpuCoaM7t70M0nE1BM4uzABV3JnJqsWnw5GkpPzitdfkJ4nbnZtuKBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T23:56:37.431513Z"},"content_sha256":"0356b42f92813e4547b60a31513b15ea02c0a0a4c3cf35586d56f2b544ff0633","schema_version":"1.0","event_id":"sha256:0356b42f92813e4547b60a31513b15ea02c0a0a4c3cf35586d56f2b544ff0633"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:CUDQZ73M4K37T4XLYYDUCVAHQO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Real-Time Personalization for LLM-based Recommendation with Customized In-Context Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Fuli Feng, Jizhi Zhang, Keqin Bao, Ming Yan, Wenjie Wang, Xiangnan He, Yang Zhang","submitted_at":"2024-10-30T15:48:36Z","abstract_excerpt":"Frequently updating Large Language Model (LLM)-based recommender systems to adapt to new user interests -- as done for traditional ones -- is impractical due to high training costs, even with acceleration methods. This work explores adapting to dynamic user interests without any model updates by leveraging In-Context Learning (ICL), which allows LLMs to learn new tasks from few-shot examples provided in the input. Using new-interest examples as the ICL few-shot examples, LLMs may learn real-time interest directly, avoiding the need for model updates. However, existing LLM-based recommenders of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.23136","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/2410.23136/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":"2026-07-05T09:28:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IlGmT/YfJXzA2e6KL0Mvr0ZQL6KWLcT/V2OFwtJqeTvjHkxcesSPFANG0vq4kKNR4Y5YM3CEKfpnrLHT+LtsAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T23:56:37.431882Z"},"content_sha256":"b4f0978c6523cc98f846ae485f42a6ad56210891e1b59a1d43d222b6cabccc79","schema_version":"1.0","event_id":"sha256:b4f0978c6523cc98f846ae485f42a6ad56210891e1b59a1d43d222b6cabccc79"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CUDQZ73M4K37T4XLYYDUCVAHQO/bundle.json","state_url":"https://pith.science/pith/CUDQZ73M4K37T4XLYYDUCVAHQO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CUDQZ73M4K37T4XLYYDUCVAHQO/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":"2026-08-22T23:56:37Z","links":{"resolver":"https://pith.science/pith/CUDQZ73M4K37T4XLYYDUCVAHQO","bundle":"https://pith.science/pith/CUDQZ73M4K37T4XLYYDUCVAHQO/bundle.json","state":"https://pith.science/pith/CUDQZ73M4K37T4XLYYDUCVAHQO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CUDQZ73M4K37T4XLYYDUCVAHQO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:CUDQZ73M4K37T4XLYYDUCVAHQO","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":"c44071dcd9014b5efddd612f9db098ddeb0fdd50444107e3908250d40b090503","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-10-30T15:48:36Z","title_canon_sha256":"9340dc525bd2ef2411ca210134b83d5408f47d174a975702538292d41b5b563f"},"schema_version":"1.0","source":{"id":"2410.23136","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.23136","created_at":"2026-07-05T09:28:41Z"},{"alias_kind":"arxiv_version","alias_value":"2410.23136v1","created_at":"2026-07-05T09:28:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.23136","created_at":"2026-07-05T09:28:41Z"},{"alias_kind":"pith_short_12","alias_value":"CUDQZ73M4K37","created_at":"2026-07-05T09:28:41Z"},{"alias_kind":"pith_short_16","alias_value":"CUDQZ73M4K37T4XL","created_at":"2026-07-05T09:28:41Z"},{"alias_kind":"pith_short_8","alias_value":"CUDQZ73M","created_at":"2026-07-05T09:28:41Z"}],"graph_snapshots":[{"event_id":"sha256:b4f0978c6523cc98f846ae485f42a6ad56210891e1b59a1d43d222b6cabccc79","target":"graph","created_at":"2026-07-05T09:28:41Z","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/2410.23136/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Frequently updating Large Language Model (LLM)-based recommender systems to adapt to new user interests -- as done for traditional ones -- is impractical due to high training costs, even with acceleration methods. This work explores adapting to dynamic user interests without any model updates by leveraging In-Context Learning (ICL), which allows LLMs to learn new tasks from few-shot examples provided in the input. Using new-interest examples as the ICL few-shot examples, LLMs may learn real-time interest directly, avoiding the need for model updates. However, existing LLM-based recommenders of","authors_text":"Fuli Feng, Jizhi Zhang, Keqin Bao, Ming Yan, Wenjie Wang, Xiangnan He, Yang Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-10-30T15:48:36Z","title":"Real-Time Personalization for LLM-based Recommendation with Customized In-Context Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.23136","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":"sha256:0356b42f92813e4547b60a31513b15ea02c0a0a4c3cf35586d56f2b544ff0633","target":"record","created_at":"2026-07-05T09:28:41Z","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":"c44071dcd9014b5efddd612f9db098ddeb0fdd50444107e3908250d40b090503","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-10-30T15:48:36Z","title_canon_sha256":"9340dc525bd2ef2411ca210134b83d5408f47d174a975702538292d41b5b563f"},"schema_version":"1.0","source":{"id":"2410.23136","kind":"arxiv","version":1}},"canonical_sha256":"15070cff6ce2b7f9f2ebc60741540783a5cfbaf64325576ef158dd296ee4fc47","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"15070cff6ce2b7f9f2ebc60741540783a5cfbaf64325576ef158dd296ee4fc47","first_computed_at":"2026-07-05T09:28:41.451564Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:28:41.451564Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qEaSollaGbr3zPQJxc1eNGPXO7fwdxg/G1zk8tcInxTW2CdoKJr6bv8iNGoEvEfgnwgphqZmYIIjkMsIx7vYDw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:28:41.451980Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.23136","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0356b42f92813e4547b60a31513b15ea02c0a0a4c3cf35586d56f2b544ff0633","sha256:b4f0978c6523cc98f846ae485f42a6ad56210891e1b59a1d43d222b6cabccc79"],"state_sha256":"3d256354b4c61e7f55dec46070969745de7681fca398507a0a4273f20959e76d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zkAfcni7zMwyeEN92GPyAX5ahAwYv3I/7ax5fDgui7yrizM8NbcTqaM2NfqtIHRePMY/FObN1F8sU5WwPzFgAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T23:56:37.434688Z","bundle_sha256":"cabfeeebd1d043e6c1b25d061cacb8301783f80863a195dfb8321241f3a2af4c"}}