{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:HQGQPY4BC4PMZMWJ2KMYDBCXIL","short_pith_number":"pith:HQGQPY4B","canonical_record":{"source":{"id":"2506.11120","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-10T02:24:32Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"879f5bd5b6c6f8e41c6ee1cdbd9ab9efc1c73a289aaf216bc7059b63ac095f10","abstract_canon_sha256":"3990792a1c657d0c1f8838e11a7c5604db06ed3ec48cfea6aa0c546306c6c42c"},"schema_version":"1.0"},"canonical_sha256":"3c0d07e381171eccb2c9d29981845742cf5dcf543a040030dcaa9d2a792fe6f5","source":{"kind":"arxiv","id":"2506.11120","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.11120","created_at":"2026-07-05T11:20:59Z"},{"alias_kind":"arxiv_version","alias_value":"2506.11120v1","created_at":"2026-07-05T11:20:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.11120","created_at":"2026-07-05T11:20:59Z"},{"alias_kind":"pith_short_12","alias_value":"HQGQPY4BC4PM","created_at":"2026-07-05T11:20:59Z"},{"alias_kind":"pith_short_16","alias_value":"HQGQPY4BC4PMZMWJ","created_at":"2026-07-05T11:20:59Z"},{"alias_kind":"pith_short_8","alias_value":"HQGQPY4B","created_at":"2026-07-05T11:20:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:HQGQPY4BC4PMZMWJ2KMYDBCXIL","target":"record","payload":{"canonical_record":{"source":{"id":"2506.11120","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-10T02:24:32Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"879f5bd5b6c6f8e41c6ee1cdbd9ab9efc1c73a289aaf216bc7059b63ac095f10","abstract_canon_sha256":"3990792a1c657d0c1f8838e11a7c5604db06ed3ec48cfea6aa0c546306c6c42c"},"schema_version":"1.0"},"canonical_sha256":"3c0d07e381171eccb2c9d29981845742cf5dcf543a040030dcaa9d2a792fe6f5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:20:59.834283Z","signature_b64":"bLL0xrsM7tx9qtJVwU3jND+XGS3so6uCDEzNnxJ5e8il/CCyWDy0utZxSOZELQxf1ZZ1Vis/MeUS0DgqcwblDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3c0d07e381171eccb2c9d29981845742cf5dcf543a040030dcaa9d2a792fe6f5","last_reissued_at":"2026-07-05T11:20:59.833836Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:20:59.833836Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.11120","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":"2026-07-05T11:20:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"51fDEmBHwB1wufhK/0A3rm10HqRd8kMVOANqenbbcVKIO5iU1+m+BZ+o/z+KTC2brjE4//mUoljLa7WoLwr2Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T19:17:52.154336Z"},"content_sha256":"ed5ffc7f3f6e5e62d13b46650ffb16acc6cf0cff0f05199cf4ee7f9e7ec87ec0","schema_version":"1.0","event_id":"sha256:ed5ffc7f3f6e5e62d13b46650ffb16acc6cf0cff0f05199cf4ee7f9e7ec87ec0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:HQGQPY4BC4PMZMWJ2KMYDBCXIL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Chengchao Shen, Hourun Zhu","submitted_at":"2025-06-10T02:24:32Z","abstract_excerpt":"In spite of strong performance achieved by LLMs, the costs of their deployment are unaffordable. For the compression of LLMs, gradient-based pruning methods present promising effectiveness. However, in these methods, the gradient computation with one-hot labels ignore the potential predictions on other words, thus missing key information for generative capability of the original model. To address this issue, we introduce a self-distillation loss during the pruning phase (rather than post-training) to fully exploit the predictions of the original model, thereby obtaining more accurate gradient "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.11120","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/2506.11120/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":"2026-07-05T11:20:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HGY7/uOhMTlhoo1pXTgmV2B51lQ1Vh9Gek4Ib0ahvqjZeCqXsepaI7EZdqCmQ5qrwtZBQGbEOplRyzL5HI6RAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T19:17:52.156442Z"},"content_sha256":"e44554e3b481dfbc48185ec34ade8eb2c527df3566c6adea716ed248fa80e3ec","schema_version":"1.0","event_id":"sha256:e44554e3b481dfbc48185ec34ade8eb2c527df3566c6adea716ed248fa80e3ec"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HQGQPY4BC4PMZMWJ2KMYDBCXIL/bundle.json","state_url":"https://pith.science/pith/HQGQPY4BC4PMZMWJ2KMYDBCXIL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HQGQPY4BC4PMZMWJ2KMYDBCXIL/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":"2026-08-07T19:17:52Z","links":{"resolver":"https://pith.science/pith/HQGQPY4BC4PMZMWJ2KMYDBCXIL","bundle":"https://pith.science/pith/HQGQPY4BC4PMZMWJ2KMYDBCXIL/bundle.json","state":"https://pith.science/pith/HQGQPY4BC4PMZMWJ2KMYDBCXIL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HQGQPY4BC4PMZMWJ2KMYDBCXIL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:HQGQPY4BC4PMZMWJ2KMYDBCXIL","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":"3990792a1c657d0c1f8838e11a7c5604db06ed3ec48cfea6aa0c546306c6c42c","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-10T02:24:32Z","title_canon_sha256":"879f5bd5b6c6f8e41c6ee1cdbd9ab9efc1c73a289aaf216bc7059b63ac095f10"},"schema_version":"1.0","source":{"id":"2506.11120","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.11120","created_at":"2026-07-05T11:20:59Z"},{"alias_kind":"arxiv_version","alias_value":"2506.11120v1","created_at":"2026-07-05T11:20:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.11120","created_at":"2026-07-05T11:20:59Z"},{"alias_kind":"pith_short_12","alias_value":"HQGQPY4BC4PM","created_at":"2026-07-05T11:20:59Z"},{"alias_kind":"pith_short_16","alias_value":"HQGQPY4BC4PMZMWJ","created_at":"2026-07-05T11:20:59Z"},{"alias_kind":"pith_short_8","alias_value":"HQGQPY4B","created_at":"2026-07-05T11:20:59Z"}],"graph_snapshots":[{"event_id":"sha256:e44554e3b481dfbc48185ec34ade8eb2c527df3566c6adea716ed248fa80e3ec","target":"graph","created_at":"2026-07-05T11:20:59Z","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/2506.11120/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In spite of strong performance achieved by LLMs, the costs of their deployment are unaffordable. For the compression of LLMs, gradient-based pruning methods present promising effectiveness. However, in these methods, the gradient computation with one-hot labels ignore the potential predictions on other words, thus missing key information for generative capability of the original model. To address this issue, we introduce a self-distillation loss during the pruning phase (rather than post-training) to fully exploit the predictions of the original model, thereby obtaining more accurate gradient ","authors_text":"Chengchao Shen, Hourun Zhu","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-10T02:24:32Z","title":"SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.11120","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:ed5ffc7f3f6e5e62d13b46650ffb16acc6cf0cff0f05199cf4ee7f9e7ec87ec0","target":"record","created_at":"2026-07-05T11:20:59Z","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":"3990792a1c657d0c1f8838e11a7c5604db06ed3ec48cfea6aa0c546306c6c42c","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-10T02:24:32Z","title_canon_sha256":"879f5bd5b6c6f8e41c6ee1cdbd9ab9efc1c73a289aaf216bc7059b63ac095f10"},"schema_version":"1.0","source":{"id":"2506.11120","kind":"arxiv","version":1}},"canonical_sha256":"3c0d07e381171eccb2c9d29981845742cf5dcf543a040030dcaa9d2a792fe6f5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3c0d07e381171eccb2c9d29981845742cf5dcf543a040030dcaa9d2a792fe6f5","first_computed_at":"2026-07-05T11:20:59.833836Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:20:59.833836Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bLL0xrsM7tx9qtJVwU3jND+XGS3so6uCDEzNnxJ5e8il/CCyWDy0utZxSOZELQxf1ZZ1Vis/MeUS0DgqcwblDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:20:59.834283Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.11120","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ed5ffc7f3f6e5e62d13b46650ffb16acc6cf0cff0f05199cf4ee7f9e7ec87ec0","sha256:e44554e3b481dfbc48185ec34ade8eb2c527df3566c6adea716ed248fa80e3ec"],"state_sha256":"e1b9ccf7b378c016e3475ac083d30ada9643dd56f99347a3f0c9a49094f1f50c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lmC/SmHveTy9DX3mKI57YPoPqRHgMZq9s+oC+fmqCywHdLQq8y9FNHTm0NQUcFRR4hSh/sz5qN13e2rmvVYIDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T19:17:52.162841Z","bundle_sha256":"a47a845617e24e78a87f6ded144f2129e7cdf87c01fdfd40627fff0e3b67f81d"}}