{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:VVIQCSUPVAPTBTB4YBP3W573WH","short_pith_number":"pith:VVIQCSUP","canonical_record":{"source":{"id":"2605.23191","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-22T03:17:29Z","cross_cats_sorted":["cs.IR","cs.NA","math.NA"],"title_canon_sha256":"7278d30625b74511c2fd27c2c7a4cc0ec06ba57d57d635a5013fd4d5db56f661","abstract_canon_sha256":"8f31f56dc228df2bfabebabf1ce7912a4314f2ee631d57480fa9429d229fe1a9"},"schema_version":"1.0"},"canonical_sha256":"ad51014a8fa81f30cc3cc05fbb77fbb1c674b58392871ea204b5f0270fe11597","source":{"kind":"arxiv","id":"2605.23191","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.23191","created_at":"2026-05-25T02:01:42Z"},{"alias_kind":"arxiv_version","alias_value":"2605.23191v1","created_at":"2026-05-25T02:01:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.23191","created_at":"2026-05-25T02:01:42Z"},{"alias_kind":"pith_short_12","alias_value":"VVIQCSUPVAPT","created_at":"2026-05-25T02:01:42Z"},{"alias_kind":"pith_short_16","alias_value":"VVIQCSUPVAPTBTB4","created_at":"2026-05-25T02:01:42Z"},{"alias_kind":"pith_short_8","alias_value":"VVIQCSUP","created_at":"2026-05-25T02:01:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:VVIQCSUPVAPTBTB4YBP3W573WH","target":"record","payload":{"canonical_record":{"source":{"id":"2605.23191","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-22T03:17:29Z","cross_cats_sorted":["cs.IR","cs.NA","math.NA"],"title_canon_sha256":"7278d30625b74511c2fd27c2c7a4cc0ec06ba57d57d635a5013fd4d5db56f661","abstract_canon_sha256":"8f31f56dc228df2bfabebabf1ce7912a4314f2ee631d57480fa9429d229fe1a9"},"schema_version":"1.0"},"canonical_sha256":"ad51014a8fa81f30cc3cc05fbb77fbb1c674b58392871ea204b5f0270fe11597","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-25T02:01:42.384155Z","signature_b64":"lDPGS1BVWr501TQVMjFYH3mZlPRUsW4qQg0RhTvg36dtfnz4uLZUM8FSzMrjI30bIhS8JJ5gzWLP6wyaRj7IDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ad51014a8fa81f30cc3cc05fbb77fbb1c674b58392871ea204b5f0270fe11597","last_reissued_at":"2026-05-25T02:01:42.383583Z","signature_status":"signed_v1","first_computed_at":"2026-05-25T02:01:42.383583Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.23191","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":"2026-05-25T02:01:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wkCJSxhpXfZLnknc6ahwZHt0hsWXAxZrW4fChUWpoBX9wTANYdhH2r5hoHSDiwowbU0Ypypq3dy0PyV7Fz65Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T05:52:47.710947Z"},"content_sha256":"a00593e42f5c9213bbe819310bc659e99ef24283ce5c90aaac9cf906fdecb561","schema_version":"1.0","event_id":"sha256:a00593e42f5c9213bbe819310bc659e99ef24283ce5c90aaac9cf906fdecb561"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:VVIQCSUPVAPTBTB4YBP3W573WH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR","cs.NA","math.NA"],"primary_cat":"cs.LG","authors_text":"Chao Zhou, Gengsheng Xue, Guoming Li, Haijie Gu, Jin Chen, Junwei Pan, Menglin Yang, Shangyu Zhang, Shudong Huang, Wentao Ning","submitted_at":"2026-05-22T03:17:29Z","abstract_excerpt":"Scaling recommendation models is a central challenge in recommender systems. Recently, RankMixer has emerged as an effective solution, operating on a unified token representation and alternating between token mixing and per-token feedforward networks (P-FFNs) to achieve scalable performance. However, RankMixer suffers from \\textit{embedding collapse}, where learned representations have low effective rank, limiting expressivity and underutilizing the expanded representation space. Through empirical analysis and theoretical insights, we identify rigid token mixing and P-FFN modules as the primar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.23191","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/2605.23191/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":"2026-05-25T02:01:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vwaRoF5eZON509Yz4bkJQE2Llo05HCGA8TAzk8P2GskQ4xA/G7ZH4fQAgmBW6MLWg+LlWZ6TflfZcKS/bbusDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T05:52:47.711758Z"},"content_sha256":"0722bfbdec17bf1bf2d5902e5a61c698e6384d2f130486a21931e4b143bc1bf8","schema_version":"1.0","event_id":"sha256:0722bfbdec17bf1bf2d5902e5a61c698e6384d2f130486a21931e4b143bc1bf8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VVIQCSUPVAPTBTB4YBP3W573WH/bundle.json","state_url":"https://pith.science/pith/VVIQCSUPVAPTBTB4YBP3W573WH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VVIQCSUPVAPTBTB4YBP3W573WH/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":"2026-05-26T05:52:47Z","links":{"resolver":"https://pith.science/pith/VVIQCSUPVAPTBTB4YBP3W573WH","bundle":"https://pith.science/pith/VVIQCSUPVAPTBTB4YBP3W573WH/bundle.json","state":"https://pith.science/pith/VVIQCSUPVAPTBTB4YBP3W573WH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VVIQCSUPVAPTBTB4YBP3W573WH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:VVIQCSUPVAPTBTB4YBP3W573WH","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":"8f31f56dc228df2bfabebabf1ce7912a4314f2ee631d57480fa9429d229fe1a9","cross_cats_sorted":["cs.IR","cs.NA","math.NA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-22T03:17:29Z","title_canon_sha256":"7278d30625b74511c2fd27c2c7a4cc0ec06ba57d57d635a5013fd4d5db56f661"},"schema_version":"1.0","source":{"id":"2605.23191","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.23191","created_at":"2026-05-25T02:01:42Z"},{"alias_kind":"arxiv_version","alias_value":"2605.23191v1","created_at":"2026-05-25T02:01:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.23191","created_at":"2026-05-25T02:01:42Z"},{"alias_kind":"pith_short_12","alias_value":"VVIQCSUPVAPT","created_at":"2026-05-25T02:01:42Z"},{"alias_kind":"pith_short_16","alias_value":"VVIQCSUPVAPTBTB4","created_at":"2026-05-25T02:01:42Z"},{"alias_kind":"pith_short_8","alias_value":"VVIQCSUP","created_at":"2026-05-25T02:01:42Z"}],"graph_snapshots":[{"event_id":"sha256:0722bfbdec17bf1bf2d5902e5a61c698e6384d2f130486a21931e4b143bc1bf8","target":"graph","created_at":"2026-05-25T02:01:42Z","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/2605.23191/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Scaling recommendation models is a central challenge in recommender systems. Recently, RankMixer has emerged as an effective solution, operating on a unified token representation and alternating between token mixing and per-token feedforward networks (P-FFNs) to achieve scalable performance. However, RankMixer suffers from \\textit{embedding collapse}, where learned representations have low effective rank, limiting expressivity and underutilizing the expanded representation space. Through empirical analysis and theoretical insights, we identify rigid token mixing and P-FFN modules as the primar","authors_text":"Chao Zhou, Gengsheng Xue, Guoming Li, Haijie Gu, Jin Chen, Junwei Pan, Menglin Yang, Shangyu Zhang, Shudong Huang, Wentao Ning","cross_cats":["cs.IR","cs.NA","math.NA"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-22T03:17:29Z","title":"Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.23191","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:a00593e42f5c9213bbe819310bc659e99ef24283ce5c90aaac9cf906fdecb561","target":"record","created_at":"2026-05-25T02:01:42Z","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":"8f31f56dc228df2bfabebabf1ce7912a4314f2ee631d57480fa9429d229fe1a9","cross_cats_sorted":["cs.IR","cs.NA","math.NA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-22T03:17:29Z","title_canon_sha256":"7278d30625b74511c2fd27c2c7a4cc0ec06ba57d57d635a5013fd4d5db56f661"},"schema_version":"1.0","source":{"id":"2605.23191","kind":"arxiv","version":1}},"canonical_sha256":"ad51014a8fa81f30cc3cc05fbb77fbb1c674b58392871ea204b5f0270fe11597","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ad51014a8fa81f30cc3cc05fbb77fbb1c674b58392871ea204b5f0270fe11597","first_computed_at":"2026-05-25T02:01:42.383583Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-25T02:01:42.383583Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lDPGS1BVWr501TQVMjFYH3mZlPRUsW4qQg0RhTvg36dtfnz4uLZUM8FSzMrjI30bIhS8JJ5gzWLP6wyaRj7IDw==","signature_status":"signed_v1","signed_at":"2026-05-25T02:01:42.384155Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.23191","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a00593e42f5c9213bbe819310bc659e99ef24283ce5c90aaac9cf906fdecb561","sha256:0722bfbdec17bf1bf2d5902e5a61c698e6384d2f130486a21931e4b143bc1bf8"],"state_sha256":"55171acfe1d5adf9102d04c7a921a5f813af374b5fe4b5cf49c0778af021ae67"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YHbpmJFmQ5JIFN1JFXsPAbyrZlxaECOwpoxp5NQqFobmgDbGSmFGSTbJZC7U9UmedhaSfQQAu/0ufNP9ELCGBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T05:52:47.715656Z","bundle_sha256":"382d0c8c0912a4e67d8099768287a62d86cec8804c64a4774d17f315a3814e2d"}}