{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:NNJYAASEE3DAATRCVECDOR7UVL","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":"bf8dec2b14e99a0b50e2341227928d0eac3fdf4f7f613f493b6a28d68badc263","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-03-20T06:09:30Z","title_canon_sha256":"4ba527f988b60c3e411cc4a59315f643018c4922abfff51de20b3688dd463f7e"},"schema_version":"1.0","source":{"id":"2403.13325","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.13325","created_at":"2026-07-05T07:58:34Z"},{"alias_kind":"arxiv_version","alias_value":"2403.13325v1","created_at":"2026-07-05T07:58:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.13325","created_at":"2026-07-05T07:58:34Z"},{"alias_kind":"pith_short_12","alias_value":"NNJYAASEE3DA","created_at":"2026-07-05T07:58:34Z"},{"alias_kind":"pith_short_16","alias_value":"NNJYAASEE3DAATRC","created_at":"2026-07-05T07:58:34Z"},{"alias_kind":"pith_short_8","alias_value":"NNJYAASE","created_at":"2026-07-05T07:58:34Z"}],"graph_snapshots":[{"event_id":"sha256:e134eb8a434e80a8c2fec5878e68778d03ab838de61b544e9d4f550c4d835635","target":"graph","created_at":"2026-07-05T07:58:34Z","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/2403.13325/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advances in Large Language Models (LLMs) have been changing the paradigm of Recommender Systems (RS). However, when items in the recommendation scenarios contain rich textual information, such as product descriptions in online shopping or news headlines on social media, LLMs require longer texts to comprehensively depict the historical user behavior sequence. This poses significant challenges to LLM-based recommenders, such as over-length limitations, extensive time and space overheads, and suboptimal model performance. To this end, in this paper, we design a novel framework for harness","authors_text":"Hengshu Zhu, Hui Xiong, Wenshuo Chao, Zhaopeng Qiu, Zhi Zheng","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-03-20T06:09:30Z","title":"Harnessing Large Language Models for Text-Rich Sequential Recommendation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.13325","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:fe52334e61c45326258e45432f5b56571e2e8aa0d3bc3d68e4264dbecafbfb29","target":"record","created_at":"2026-07-05T07:58:34Z","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":"bf8dec2b14e99a0b50e2341227928d0eac3fdf4f7f613f493b6a28d68badc263","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-03-20T06:09:30Z","title_canon_sha256":"4ba527f988b60c3e411cc4a59315f643018c4922abfff51de20b3688dd463f7e"},"schema_version":"1.0","source":{"id":"2403.13325","kind":"arxiv","version":1}},"canonical_sha256":"6b5380024426c6004e22a9043747f4aacb45a5239c45fb58f5c40c63e1cae0fd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6b5380024426c6004e22a9043747f4aacb45a5239c45fb58f5c40c63e1cae0fd","first_computed_at":"2026-07-05T07:58:34.782374Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:58:34.782374Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UCgbh9i8r6NTgUI7Kaf0vSsFtYyZrB+T0TJFUjdJqdiQvnS6AelO5oPqmnsB41BGxqdVg4oI2gZsHQQtFkGoCw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:58:34.782823Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.13325","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fe52334e61c45326258e45432f5b56571e2e8aa0d3bc3d68e4264dbecafbfb29","sha256:e134eb8a434e80a8c2fec5878e68778d03ab838de61b544e9d4f550c4d835635"],"state_sha256":"28e6b31676ef7f93d7bbf17696807b3de4effe5c18af4337bf9803acd762b729"}