{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:HXTPRVTEELJW4Y24QQDWSARFX7","short_pith_number":"pith:HXTPRVTE","canonical_record":{"source":{"id":"2506.04015","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2025-06-04T14:46:18Z","cross_cats_sorted":[],"title_canon_sha256":"30b51d3b969da4ab8d46f019a586ceb974a228bbc52169e41274e451e0e2a501","abstract_canon_sha256":"643ad906ea5b83d13dc25a5110bc1d097d3f2f4b1c3360708b21a2c67794b0ae"},"schema_version":"1.0"},"canonical_sha256":"3de6f8d66422d36e635c8407690225bff32c84c1ccf0e6791d062702b61c2852","source":{"kind":"arxiv","id":"2506.04015","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.04015","created_at":"2026-07-05T11:22:18Z"},{"alias_kind":"arxiv_version","alias_value":"2506.04015v1","created_at":"2026-07-05T11:22:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.04015","created_at":"2026-07-05T11:22:18Z"},{"alias_kind":"pith_short_12","alias_value":"HXTPRVTEELJW","created_at":"2026-07-05T11:22:18Z"},{"alias_kind":"pith_short_16","alias_value":"HXTPRVTEELJW4Y24","created_at":"2026-07-05T11:22:18Z"},{"alias_kind":"pith_short_8","alias_value":"HXTPRVTE","created_at":"2026-07-05T11:22:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:HXTPRVTEELJW4Y24QQDWSARFX7","target":"record","payload":{"canonical_record":{"source":{"id":"2506.04015","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2025-06-04T14:46:18Z","cross_cats_sorted":[],"title_canon_sha256":"30b51d3b969da4ab8d46f019a586ceb974a228bbc52169e41274e451e0e2a501","abstract_canon_sha256":"643ad906ea5b83d13dc25a5110bc1d097d3f2f4b1c3360708b21a2c67794b0ae"},"schema_version":"1.0"},"canonical_sha256":"3de6f8d66422d36e635c8407690225bff32c84c1ccf0e6791d062702b61c2852","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:22:18.901020Z","signature_b64":"S33jZv3U0KfFUI0JBul8jev/LxTzYCTSooHyn9okzNdELSb+VXYtRt6F8ujg4uDiJUxS1hNMPzcHgD5REZ8xCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3de6f8d66422d36e635c8407690225bff32c84c1ccf0e6791d062702b61c2852","last_reissued_at":"2026-07-05T11:22:18.900500Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:22:18.900500Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.04015","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":"2026-07-05T11:22:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VXgVqIV7a9nuN6W9h/n+mMoD73g00s+KzBRSKHJ0BJRgx9kcV8LvSgC3AX+5k/WGFI2UFGYW5T1ZVQwqtV+wBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T21:28:04.218181Z"},"content_sha256":"ae345702a13a473005082de73fd1dc8b15a9636d4f5cd8ac7c00f30f0df1a2ba","schema_version":"1.0","event_id":"sha256:ae345702a13a473005082de73fd1dc8b15a9636d4f5cd8ac7c00f30f0df1a2ba"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:HXTPRVTEELJW4Y24QQDWSARFX7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GORACS: Group-level Optimal Transport-guided Coreset Selection for LLM-based Recommender Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Deqing Yang, Hengrui Chen, Jiaqing Liang, Peng Yu, Tiehua Mei","submitted_at":"2025-06-04T14:46:18Z","abstract_excerpt":"Although large language models (LLMs) have shown great potential in recommender systems, the prohibitive computational costs for fine-tuning LLMs on entire datasets hinder their successful deployment in real-world scenarios. To develop affordable and effective LLM-based recommender systems, we focus on the task of coreset selection which identifies a small subset of fine-tuning data to optimize the test loss, thereby facilitating efficient LLMs' fine-tuning. Although there exist some intuitive solutions of subset selection, including distribution-based and importance-based approaches, they oft"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.04015","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/2506.04015/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":"2026-07-05T11:22:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UU0qZj2J0Ota3ayEHxkNgvvWf+dZx7/kvdCAXFBNu+PSfd7rY+bbx1Gj5ZfARDCqSjPCbxLrN0EzA2/Z1ls+CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T21:28:04.219117Z"},"content_sha256":"73948650a0d372c94659c1a4e1bf1597c1736a1a474bb92758173a5ad2528854","schema_version":"1.0","event_id":"sha256:73948650a0d372c94659c1a4e1bf1597c1736a1a474bb92758173a5ad2528854"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HXTPRVTEELJW4Y24QQDWSARFX7/bundle.json","state_url":"https://pith.science/pith/HXTPRVTEELJW4Y24QQDWSARFX7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HXTPRVTEELJW4Y24QQDWSARFX7/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":"2026-08-07T21:28:04Z","links":{"resolver":"https://pith.science/pith/HXTPRVTEELJW4Y24QQDWSARFX7","bundle":"https://pith.science/pith/HXTPRVTEELJW4Y24QQDWSARFX7/bundle.json","state":"https://pith.science/pith/HXTPRVTEELJW4Y24QQDWSARFX7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HXTPRVTEELJW4Y24QQDWSARFX7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:HXTPRVTEELJW4Y24QQDWSARFX7","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":"643ad906ea5b83d13dc25a5110bc1d097d3f2f4b1c3360708b21a2c67794b0ae","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2025-06-04T14:46:18Z","title_canon_sha256":"30b51d3b969da4ab8d46f019a586ceb974a228bbc52169e41274e451e0e2a501"},"schema_version":"1.0","source":{"id":"2506.04015","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.04015","created_at":"2026-07-05T11:22:18Z"},{"alias_kind":"arxiv_version","alias_value":"2506.04015v1","created_at":"2026-07-05T11:22:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.04015","created_at":"2026-07-05T11:22:18Z"},{"alias_kind":"pith_short_12","alias_value":"HXTPRVTEELJW","created_at":"2026-07-05T11:22:18Z"},{"alias_kind":"pith_short_16","alias_value":"HXTPRVTEELJW4Y24","created_at":"2026-07-05T11:22:18Z"},{"alias_kind":"pith_short_8","alias_value":"HXTPRVTE","created_at":"2026-07-05T11:22:18Z"}],"graph_snapshots":[{"event_id":"sha256:73948650a0d372c94659c1a4e1bf1597c1736a1a474bb92758173a5ad2528854","target":"graph","created_at":"2026-07-05T11:22:18Z","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/2506.04015/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Although large language models (LLMs) have shown great potential in recommender systems, the prohibitive computational costs for fine-tuning LLMs on entire datasets hinder their successful deployment in real-world scenarios. To develop affordable and effective LLM-based recommender systems, we focus on the task of coreset selection which identifies a small subset of fine-tuning data to optimize the test loss, thereby facilitating efficient LLMs' fine-tuning. Although there exist some intuitive solutions of subset selection, including distribution-based and importance-based approaches, they oft","authors_text":"Deqing Yang, Hengrui Chen, Jiaqing Liang, Peng Yu, Tiehua Mei","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2025-06-04T14:46:18Z","title":"GORACS: Group-level Optimal Transport-guided Coreset Selection for LLM-based Recommender Systems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.04015","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:ae345702a13a473005082de73fd1dc8b15a9636d4f5cd8ac7c00f30f0df1a2ba","target":"record","created_at":"2026-07-05T11:22:18Z","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":"643ad906ea5b83d13dc25a5110bc1d097d3f2f4b1c3360708b21a2c67794b0ae","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2025-06-04T14:46:18Z","title_canon_sha256":"30b51d3b969da4ab8d46f019a586ceb974a228bbc52169e41274e451e0e2a501"},"schema_version":"1.0","source":{"id":"2506.04015","kind":"arxiv","version":1}},"canonical_sha256":"3de6f8d66422d36e635c8407690225bff32c84c1ccf0e6791d062702b61c2852","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3de6f8d66422d36e635c8407690225bff32c84c1ccf0e6791d062702b61c2852","first_computed_at":"2026-07-05T11:22:18.900500Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:22:18.900500Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"S33jZv3U0KfFUI0JBul8jev/LxTzYCTSooHyn9okzNdELSb+VXYtRt6F8ujg4uDiJUxS1hNMPzcHgD5REZ8xCg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:22:18.901020Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.04015","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ae345702a13a473005082de73fd1dc8b15a9636d4f5cd8ac7c00f30f0df1a2ba","sha256:73948650a0d372c94659c1a4e1bf1597c1736a1a474bb92758173a5ad2528854"],"state_sha256":"19f8a250f3f301840ccefe3d8d41251644b950947f27034a586d475676845766"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ye5irxADHHULZk9cs7RLhJhmvQL59KTUmeT34J+/XjkrDfou/4xJBc8GWrMBYPuCRze7QrQFqC1sLzho6vWABA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T21:28:04.229326Z","bundle_sha256":"986572114cefb901ace58929c457c80c720139b3ef38080aadc82f36df31d686"}}