{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:XYJJMVSVYREF6NJTNNTN5JB4QN","short_pith_number":"pith:XYJJMVSV","canonical_record":{"source":{"id":"2506.15702","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-30T01:54:12Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"0044cbc68148c4b47ca729e3122e35e6a72bebc9a569f277cd7834ea8260d182","abstract_canon_sha256":"36fe90c3266bf2ffc1f1a22884c493c29c96b5cde038606e2bde89e8f67358e0"},"schema_version":"1.0"},"canonical_sha256":"be12965655c4485f35336b66dea43c834bca340bae702702bde522092e77b6bc","source":{"kind":"arxiv","id":"2506.15702","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.15702","created_at":"2026-07-05T11:23:50Z"},{"alias_kind":"arxiv_version","alias_value":"2506.15702v1","created_at":"2026-07-05T11:23:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.15702","created_at":"2026-07-05T11:23:50Z"},{"alias_kind":"pith_short_12","alias_value":"XYJJMVSVYREF","created_at":"2026-07-05T11:23:50Z"},{"alias_kind":"pith_short_16","alias_value":"XYJJMVSVYREF6NJT","created_at":"2026-07-05T11:23:50Z"},{"alias_kind":"pith_short_8","alias_value":"XYJJMVSV","created_at":"2026-07-05T11:23:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:XYJJMVSVYREF6NJTNNTN5JB4QN","target":"record","payload":{"canonical_record":{"source":{"id":"2506.15702","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-30T01:54:12Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"0044cbc68148c4b47ca729e3122e35e6a72bebc9a569f277cd7834ea8260d182","abstract_canon_sha256":"36fe90c3266bf2ffc1f1a22884c493c29c96b5cde038606e2bde89e8f67358e0"},"schema_version":"1.0"},"canonical_sha256":"be12965655c4485f35336b66dea43c834bca340bae702702bde522092e77b6bc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:23:50.606754Z","signature_b64":"Qf1tI+sm2fUkJY9QjRLRB61+nqpeVdg8Km/9CeCG7insm7yOBOhK2fRWo/95WfPbWCA+JWJy4rZrawEcjm9qCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"be12965655c4485f35336b66dea43c834bca340bae702702bde522092e77b6bc","last_reissued_at":"2026-07-05T11:23:50.606243Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:23:50.606243Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.15702","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":"2026-07-05T11:23:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hdTsCxZy+KF/O64irVxyXlN90kn/T6yLyTQorGG318ICVlNTt3KZxVHYQ8sVI8X9ofeAKGPnkZYR5u9Ke65kAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T08:47:40.616430Z"},"content_sha256":"dcc08d35f3f1095b8eece93ffa2273f6c6325c46c52b9f6acd4a364a3d430b56","schema_version":"1.0","event_id":"sha256:dcc08d35f3f1095b8eece93ffa2273f6c6325c46c52b9f6acd4a364a3d430b56"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:XYJJMVSVYREF6NJTNNTN5JB4QN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Greg Heinrich, Jan Kautz, Pavlo Molchanov, Peter Belcak","submitted_at":"2025-05-30T01:54:12Z","abstract_excerpt":"Finetuning language models for a new domain inevitably leads to the deterioration of their general performance. This becomes more pronounced the more limited the finetuning data resource.\n  We introduce minifinetuning (MFT), a method for language model domain adaptation that considerably reduces the effects of overfitting-induced degeneralization in low-data settings and which does so in the absence of any pre-training data for replay. MFT demonstrates 2-10x more favourable specialization-to-degeneralization ratios than standard finetuning across a wide range of models and domains and exhibits"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.15702","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/2506.15702/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":"2026-07-05T11:23:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T/jt4j50mHUn1It9cA22hbBbxIuKvm5FB6/XMpsJEY6HfTesvhqZpYhWyP6RgKLD9qiCMtVEtSupSM+G9kHiCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T08:47:40.617256Z"},"content_sha256":"9c7dd5e8b77fd1bf5ad0c55ac73abb5ac7d535a53abe6c3b4f80340c4618b0bc","schema_version":"1.0","event_id":"sha256:9c7dd5e8b77fd1bf5ad0c55ac73abb5ac7d535a53abe6c3b4f80340c4618b0bc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XYJJMVSVYREF6NJTNNTN5JB4QN/bundle.json","state_url":"https://pith.science/pith/XYJJMVSVYREF6NJTNNTN5JB4QN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XYJJMVSVYREF6NJTNNTN5JB4QN/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":"2026-08-09T08:47:40Z","links":{"resolver":"https://pith.science/pith/XYJJMVSVYREF6NJTNNTN5JB4QN","bundle":"https://pith.science/pith/XYJJMVSVYREF6NJTNNTN5JB4QN/bundle.json","state":"https://pith.science/pith/XYJJMVSVYREF6NJTNNTN5JB4QN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XYJJMVSVYREF6NJTNNTN5JB4QN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:XYJJMVSVYREF6NJTNNTN5JB4QN","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":"36fe90c3266bf2ffc1f1a22884c493c29c96b5cde038606e2bde89e8f67358e0","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-30T01:54:12Z","title_canon_sha256":"0044cbc68148c4b47ca729e3122e35e6a72bebc9a569f277cd7834ea8260d182"},"schema_version":"1.0","source":{"id":"2506.15702","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.15702","created_at":"2026-07-05T11:23:50Z"},{"alias_kind":"arxiv_version","alias_value":"2506.15702v1","created_at":"2026-07-05T11:23:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.15702","created_at":"2026-07-05T11:23:50Z"},{"alias_kind":"pith_short_12","alias_value":"XYJJMVSVYREF","created_at":"2026-07-05T11:23:50Z"},{"alias_kind":"pith_short_16","alias_value":"XYJJMVSVYREF6NJT","created_at":"2026-07-05T11:23:50Z"},{"alias_kind":"pith_short_8","alias_value":"XYJJMVSV","created_at":"2026-07-05T11:23:50Z"}],"graph_snapshots":[{"event_id":"sha256:9c7dd5e8b77fd1bf5ad0c55ac73abb5ac7d535a53abe6c3b4f80340c4618b0bc","target":"graph","created_at":"2026-07-05T11:23:50Z","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/2506.15702/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Finetuning language models for a new domain inevitably leads to the deterioration of their general performance. This becomes more pronounced the more limited the finetuning data resource.\n  We introduce minifinetuning (MFT), a method for language model domain adaptation that considerably reduces the effects of overfitting-induced degeneralization in low-data settings and which does so in the absence of any pre-training data for replay. MFT demonstrates 2-10x more favourable specialization-to-degeneralization ratios than standard finetuning across a wide range of models and domains and exhibits","authors_text":"Greg Heinrich, Jan Kautz, Pavlo Molchanov, Peter Belcak","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-30T01:54:12Z","title":"Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.15702","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:dcc08d35f3f1095b8eece93ffa2273f6c6325c46c52b9f6acd4a364a3d430b56","target":"record","created_at":"2026-07-05T11:23:50Z","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":"36fe90c3266bf2ffc1f1a22884c493c29c96b5cde038606e2bde89e8f67358e0","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-30T01:54:12Z","title_canon_sha256":"0044cbc68148c4b47ca729e3122e35e6a72bebc9a569f277cd7834ea8260d182"},"schema_version":"1.0","source":{"id":"2506.15702","kind":"arxiv","version":1}},"canonical_sha256":"be12965655c4485f35336b66dea43c834bca340bae702702bde522092e77b6bc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"be12965655c4485f35336b66dea43c834bca340bae702702bde522092e77b6bc","first_computed_at":"2026-07-05T11:23:50.606243Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:23:50.606243Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Qf1tI+sm2fUkJY9QjRLRB61+nqpeVdg8Km/9CeCG7insm7yOBOhK2fRWo/95WfPbWCA+JWJy4rZrawEcjm9qCg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:23:50.606754Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.15702","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dcc08d35f3f1095b8eece93ffa2273f6c6325c46c52b9f6acd4a364a3d430b56","sha256:9c7dd5e8b77fd1bf5ad0c55ac73abb5ac7d535a53abe6c3b4f80340c4618b0bc"],"state_sha256":"bba5b2cb325de5c6b728c3dffb64ee3388cb3c35525911f3f92ce436783295df"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5ww/qaS5p1TF3rO5QchuT+UnDFmN6O0TY6cQ1Ndmzk3IsrkTqGMXU7k+JUbOAIK9Hvp11CyrxQGgcU+YbLu/Dw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T08:47:40.623892Z","bundle_sha256":"a515443c10a169a05a72142f182d9d05fda36795a682e61d07966d39f9f4afdb"}}