{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:MH2SB2Z7Q5ZTT5CKKWSOIGEONL","short_pith_number":"pith:MH2SB2Z7","canonical_record":{"source":{"id":"2605.31164","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-29T11:13:43Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"fb5188543ad85291868e19b98d264c72257a86ce9deec9ea14ab766acae874f4","abstract_canon_sha256":"438c9b0f380ee9c3d3babcf131068cb79b2f855c2708d6b5546d51bae1b00c24"},"schema_version":"1.0"},"canonical_sha256":"61f520eb3f877339f44a55a4e4188e6af9331d5df642c573eb96ed6ef752b498","source":{"kind":"arxiv","id":"2605.31164","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.31164","created_at":"2026-06-01T01:04:01Z"},{"alias_kind":"arxiv_version","alias_value":"2605.31164v1","created_at":"2026-06-01T01:04:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.31164","created_at":"2026-06-01T01:04:01Z"},{"alias_kind":"pith_short_12","alias_value":"MH2SB2Z7Q5ZT","created_at":"2026-06-01T01:04:01Z"},{"alias_kind":"pith_short_16","alias_value":"MH2SB2Z7Q5ZTT5CK","created_at":"2026-06-01T01:04:01Z"},{"alias_kind":"pith_short_8","alias_value":"MH2SB2Z7","created_at":"2026-06-01T01:04:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:MH2SB2Z7Q5ZTT5CKKWSOIGEONL","target":"record","payload":{"canonical_record":{"source":{"id":"2605.31164","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-29T11:13:43Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"fb5188543ad85291868e19b98d264c72257a86ce9deec9ea14ab766acae874f4","abstract_canon_sha256":"438c9b0f380ee9c3d3babcf131068cb79b2f855c2708d6b5546d51bae1b00c24"},"schema_version":"1.0"},"canonical_sha256":"61f520eb3f877339f44a55a4e4188e6af9331d5df642c573eb96ed6ef752b498","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-01T01:04:01.979161Z","signature_b64":"tvNEaBXOyEztavkKyBtwurgiz0ZYLwwmlD8KnszVg4cFcaPRouigLxKBCnWJ/Q2ilAtVP3EeiG3sRFQaF9LhDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"61f520eb3f877339f44a55a4e4188e6af9331d5df642c573eb96ed6ef752b498","last_reissued_at":"2026-06-01T01:04:01.978616Z","signature_status":"signed_v1","first_computed_at":"2026-06-01T01:04:01.978616Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.31164","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":"2026-06-01T01:04:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g3FewPWFNDAk8kRK0j/HSTDdaobtlsdVT/TMqclS2bmWJv6eXuD0BoPdZTcoCHHcyUVC8NqhXOUYDPIkmzoYBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T12:08:55.987067Z"},"content_sha256":"93fe9bfd0a439365e4e1505ae3ed37654a4d371aaef378500959adfcfd6b5210","schema_version":"1.0","event_id":"sha256:93fe9bfd0a439365e4e1505ae3ed37654a4d371aaef378500959adfcfd6b5210"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:MH2SB2Z7Q5ZTT5CKKWSOIGEONL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"D$^3$: Dynamic Directional Graph-Constrained Data Scheduling for LLM Training","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Guang Zhang, Jianing Hao, Yuanjian Xu, Zhong Li","submitted_at":"2026-05-29T11:13:43Z","abstract_excerpt":"Training data plays a central role in large language models (LLMs) optimization, motivating extensive research on data scheduling strategies. Most existing approaches concentrate on adjusting the overall data distribution but neglect the underlying interactions between samples during training. However, we argue that such interactions cannot be overlooked, as real-world data samples frequently exhibit directional influences on each other, making the training order crucial. Intuitively, we can prioritize train-units with greater influence to improves learning efficiency. In this work, we propose"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.31164","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/2605.31164/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":"2026-06-01T01:04:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aZnDWrn6CXCuS0hU39/yib0t+9MGSLwzSQaoFIcMAxoU3CNWfUd0g/BiUBgpX5niTsfymxAIblwrPwPBeFj3Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T12:08:55.987446Z"},"content_sha256":"990f51e636a5c1ef3ea5f04b76ed22fc13e4dd860a9f73159fc8004f9f08182e","schema_version":"1.0","event_id":"sha256:990f51e636a5c1ef3ea5f04b76ed22fc13e4dd860a9f73159fc8004f9f08182e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MH2SB2Z7Q5ZTT5CKKWSOIGEONL/bundle.json","state_url":"https://pith.science/pith/MH2SB2Z7Q5ZTT5CKKWSOIGEONL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MH2SB2Z7Q5ZTT5CKKWSOIGEONL/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":"2026-06-29T12:08:55Z","links":{"resolver":"https://pith.science/pith/MH2SB2Z7Q5ZTT5CKKWSOIGEONL","bundle":"https://pith.science/pith/MH2SB2Z7Q5ZTT5CKKWSOIGEONL/bundle.json","state":"https://pith.science/pith/MH2SB2Z7Q5ZTT5CKKWSOIGEONL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MH2SB2Z7Q5ZTT5CKKWSOIGEONL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:MH2SB2Z7Q5ZTT5CKKWSOIGEONL","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":"438c9b0f380ee9c3d3babcf131068cb79b2f855c2708d6b5546d51bae1b00c24","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-29T11:13:43Z","title_canon_sha256":"fb5188543ad85291868e19b98d264c72257a86ce9deec9ea14ab766acae874f4"},"schema_version":"1.0","source":{"id":"2605.31164","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.31164","created_at":"2026-06-01T01:04:01Z"},{"alias_kind":"arxiv_version","alias_value":"2605.31164v1","created_at":"2026-06-01T01:04:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.31164","created_at":"2026-06-01T01:04:01Z"},{"alias_kind":"pith_short_12","alias_value":"MH2SB2Z7Q5ZT","created_at":"2026-06-01T01:04:01Z"},{"alias_kind":"pith_short_16","alias_value":"MH2SB2Z7Q5ZTT5CK","created_at":"2026-06-01T01:04:01Z"},{"alias_kind":"pith_short_8","alias_value":"MH2SB2Z7","created_at":"2026-06-01T01:04:01Z"}],"graph_snapshots":[{"event_id":"sha256:990f51e636a5c1ef3ea5f04b76ed22fc13e4dd860a9f73159fc8004f9f08182e","target":"graph","created_at":"2026-06-01T01:04:01Z","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/2605.31164/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Training data plays a central role in large language models (LLMs) optimization, motivating extensive research on data scheduling strategies. Most existing approaches concentrate on adjusting the overall data distribution but neglect the underlying interactions between samples during training. However, we argue that such interactions cannot be overlooked, as real-world data samples frequently exhibit directional influences on each other, making the training order crucial. Intuitively, we can prioritize train-units with greater influence to improves learning efficiency. In this work, we propose","authors_text":"Guang Zhang, Jianing Hao, Yuanjian Xu, Zhong Li","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-29T11:13:43Z","title":"D$^3$: Dynamic Directional Graph-Constrained Data Scheduling for LLM Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.31164","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":"sha256:93fe9bfd0a439365e4e1505ae3ed37654a4d371aaef378500959adfcfd6b5210","target":"record","created_at":"2026-06-01T01:04:01Z","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":"438c9b0f380ee9c3d3babcf131068cb79b2f855c2708d6b5546d51bae1b00c24","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-29T11:13:43Z","title_canon_sha256":"fb5188543ad85291868e19b98d264c72257a86ce9deec9ea14ab766acae874f4"},"schema_version":"1.0","source":{"id":"2605.31164","kind":"arxiv","version":1}},"canonical_sha256":"61f520eb3f877339f44a55a4e4188e6af9331d5df642c573eb96ed6ef752b498","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"61f520eb3f877339f44a55a4e4188e6af9331d5df642c573eb96ed6ef752b498","first_computed_at":"2026-06-01T01:04:01.978616Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-01T01:04:01.978616Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tvNEaBXOyEztavkKyBtwurgiz0ZYLwwmlD8KnszVg4cFcaPRouigLxKBCnWJ/Q2ilAtVP3EeiG3sRFQaF9LhDg==","signature_status":"signed_v1","signed_at":"2026-06-01T01:04:01.979161Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.31164","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:93fe9bfd0a439365e4e1505ae3ed37654a4d371aaef378500959adfcfd6b5210","sha256:990f51e636a5c1ef3ea5f04b76ed22fc13e4dd860a9f73159fc8004f9f08182e"],"state_sha256":"0003b2147355d0c2c057ab38a3bc0ce0752ced3db1b051d030d13bee05706770"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6zY43ikiPbzakBHqJYHIuwZs8FU6BRTK3SIooSCgaV+gd76ELxLHcyOhWa5rnG7Ugx3tKVJuhZbix2nipmU4Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-29T12:08:55.989511Z","bundle_sha256":"b5934267bfbdff5b19c4c223e599886ecf15926094c92344cfc7b4534c15464e"}}