{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:4TYU5EDZNWS4R5PRCO75X7YFBT","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":"593c5c45cd2b430bca374dfdfc878e452e1c335d2e92515a66f44195da2aabb6","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2026-06-23T20:20:37Z","title_canon_sha256":"a9e657d27b9223411a76443d4f58735c592bd2a9accb1c76805b8c41b4fc9722"},"schema_version":"1.0","source":{"id":"2606.25147","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.25147","created_at":"2026-06-25T00:18:19Z"},{"alias_kind":"arxiv_version","alias_value":"2606.25147v1","created_at":"2026-06-25T00:18:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.25147","created_at":"2026-06-25T00:18:19Z"},{"alias_kind":"pith_short_12","alias_value":"4TYU5EDZNWS4","created_at":"2026-06-25T00:18:19Z"},{"alias_kind":"pith_short_16","alias_value":"4TYU5EDZNWS4R5PR","created_at":"2026-06-25T00:18:19Z"},{"alias_kind":"pith_short_8","alias_value":"4TYU5EDZ","created_at":"2026-06-25T00:18:19Z"}],"graph_snapshots":[{"event_id":"sha256:4c932ca9f5b271bf6e73c64630c9101188a262fbf19e92c9b57d1a0a691fa04f","target":"graph","created_at":"2026-06-25T00:18:19Z","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/2606.25147/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"User modeling in industrial recommender systems typically produces dense embeddings, which suffer from representational constraints inherent to fixed-dimensional vectors. An emerging alternative for discrete user representation -- using LLMs to generate text-based user tokens -- captures topical co-occurrences rather than deep sequential behavior dynamics and produces outputs that are difficult to ground to item attributes. Meanwhile, Semantic ID (SID) based item tokenization has proven effective for improving generalization in generative recommendation, yet discrete SID-based representations ","authors_text":"Bo Yan, Diego Uribe, Ekansh Sharma, Emma Olowo, Lichan Hong, Likang Yin, Li Wei, Lukasz Heldt, Min-Hsuan Tsai, Qingyun Liu, Saksham Aggarwal, Siqi Wu, Vikas Kedigehalli, Xinyang Yi, Yang Liu, Yuan Hao, Yuji Roh, Yuxuan Li","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2026-06-23T20:20:37Z","title":"TokenMinds: Pretrained User Tokens and Embeddings for User Understanding in Large Recommender Systems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.25147","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":"sha256:848aeabcd78f979d11f1de3a0aa864a08dd5d0dbb572c2270558e968edb8d790","target":"record","created_at":"2026-06-25T00:18:19Z","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":"593c5c45cd2b430bca374dfdfc878e452e1c335d2e92515a66f44195da2aabb6","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2026-06-23T20:20:37Z","title_canon_sha256":"a9e657d27b9223411a76443d4f58735c592bd2a9accb1c76805b8c41b4fc9722"},"schema_version":"1.0","source":{"id":"2606.25147","kind":"arxiv","version":1}},"canonical_sha256":"e4f14e90796da5c8f5f113bfdbff050cf83073b5314ad85c24ea0bfd10f66127","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e4f14e90796da5c8f5f113bfdbff050cf83073b5314ad85c24ea0bfd10f66127","first_computed_at":"2026-06-25T00:18:19.341366Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-25T00:18:19.341366Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8PKPF4/L8nxSllpc+FnHTiwvt6/QvIjYmgV8N3vXWkEh+GvDK2lvpXJDx0vGQJzmUqNpeSFmaOw4aD5XXxz9Bg==","signature_status":"signed_v1","signed_at":"2026-06-25T00:18:19.341776Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.25147","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:848aeabcd78f979d11f1de3a0aa864a08dd5d0dbb572c2270558e968edb8d790","sha256:4c932ca9f5b271bf6e73c64630c9101188a262fbf19e92c9b57d1a0a691fa04f"],"state_sha256":"c4379fa7c1c063141501c3d5233250796ddae43e31ec54ea1f73c1ea0afd44c6"}