{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:B6NOZPYG47CHAHVBUKFRA27GDY","short_pith_number":"pith:B6NOZPYG","canonical_record":{"source":{"id":"2606.11206","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-04-22T14:47:30Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"59dc272294d303db585b7c9a6c8a301581f487de46c8673d719351f18d8fc5d3","abstract_canon_sha256":"f301f9604e0e3027be1c972d281f67c91cc2416cc4af4610cdedb40bd91d5071"},"schema_version":"1.0"},"canonical_sha256":"0f9aecbf06e7c4701ea1a28b106be61e2707260866a9df6b976adf8d7283ed80","source":{"kind":"arxiv","id":"2606.11206","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.11206","created_at":"2026-06-11T00:08:06Z"},{"alias_kind":"arxiv_version","alias_value":"2606.11206v1","created_at":"2026-06-11T00:08:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.11206","created_at":"2026-06-11T00:08:06Z"},{"alias_kind":"pith_short_12","alias_value":"B6NOZPYG47CH","created_at":"2026-06-11T00:08:06Z"},{"alias_kind":"pith_short_16","alias_value":"B6NOZPYG47CHAHVB","created_at":"2026-06-11T00:08:06Z"},{"alias_kind":"pith_short_8","alias_value":"B6NOZPYG","created_at":"2026-06-11T00:08:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:B6NOZPYG47CHAHVBUKFRA27GDY","target":"record","payload":{"canonical_record":{"source":{"id":"2606.11206","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-04-22T14:47:30Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"59dc272294d303db585b7c9a6c8a301581f487de46c8673d719351f18d8fc5d3","abstract_canon_sha256":"f301f9604e0e3027be1c972d281f67c91cc2416cc4af4610cdedb40bd91d5071"},"schema_version":"1.0"},"canonical_sha256":"0f9aecbf06e7c4701ea1a28b106be61e2707260866a9df6b976adf8d7283ed80","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-11T00:08:06.450460Z","signature_b64":"gOLaJIs2bE4uI9STUi0zeKpDaiIe9ylfQXs/3MrqbMiWVURiwAxuJAGii8tUbCvug5/LrIkg8m+UrMn5TNzaDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0f9aecbf06e7c4701ea1a28b106be61e2707260866a9df6b976adf8d7283ed80","last_reissued_at":"2026-06-11T00:08:06.449515Z","signature_status":"signed_v1","first_computed_at":"2026-06-11T00:08:06.449515Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.11206","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":"2026-06-11T00:08:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dH7q7GMqfkS5hMFY+E4Xgi58ajpCgfa/zjL3OuzgB2R3hPkOYoojqMfGUIhjkG1ZV8Z5EBa4dqCxtouWRfJjDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T12:52:48.034800Z"},"content_sha256":"1ddb4ef60666531e02c7e661ba29fdf75ea110d73948fc582632873be85e824b","schema_version":"1.0","event_id":"sha256:1ddb4ef60666531e02c7e661ba29fdf75ea110d73948fc582632873be85e824b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:B6NOZPYG47CHAHVBUKFRA27GDY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Compatibility-Aware Dynamic Fine-Tuning for Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Jianbing Shen, Junwei Sheng, Qianning Wang, Yucheng Zhou","submitted_at":"2026-04-22T14:47:30Z","abstract_excerpt":"Supervised Fine-Tuning (SFT) is the predominant paradigm for aligning large language models (LLMs), yet it suffers from optimization instability and limited generalization. Recent work attributes this issue to pathological gradient scaling and proposes Dynamic Fine-Tuning (DFT) to correct it at the token level. However, DFT assumes all demonstrations are equally suitable learning targets, an assumption violated by the strong heterogeneity of large-scale instruction data, where demonstration-policy mismatch induces high-variance updates at the sample level. We introduce Compatibility-Aware Dyna"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.11206","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/2606.11206/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":"2026-06-11T00:08:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ogonDSQzZv9uO8ihGC9IzXzxpO5ddGLj5CijsXJIbaXmn4xg3S5LEtXWB8Vg85gFox+2ykyEJTv3/uhowh2wCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T12:52:48.035177Z"},"content_sha256":"9716b8bc60f6d0a6f5b3795371d2b8de89839c70e24b4d41b94c4683a736c5c7","schema_version":"1.0","event_id":"sha256:9716b8bc60f6d0a6f5b3795371d2b8de89839c70e24b4d41b94c4683a736c5c7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/B6NOZPYG47CHAHVBUKFRA27GDY/bundle.json","state_url":"https://pith.science/pith/B6NOZPYG47CHAHVBUKFRA27GDY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/B6NOZPYG47CHAHVBUKFRA27GDY/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":"2026-07-05T12:52:48Z","links":{"resolver":"https://pith.science/pith/B6NOZPYG47CHAHVBUKFRA27GDY","bundle":"https://pith.science/pith/B6NOZPYG47CHAHVBUKFRA27GDY/bundle.json","state":"https://pith.science/pith/B6NOZPYG47CHAHVBUKFRA27GDY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/B6NOZPYG47CHAHVBUKFRA27GDY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:B6NOZPYG47CHAHVBUKFRA27GDY","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":"f301f9604e0e3027be1c972d281f67c91cc2416cc4af4610cdedb40bd91d5071","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-04-22T14:47:30Z","title_canon_sha256":"59dc272294d303db585b7c9a6c8a301581f487de46c8673d719351f18d8fc5d3"},"schema_version":"1.0","source":{"id":"2606.11206","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.11206","created_at":"2026-06-11T00:08:06Z"},{"alias_kind":"arxiv_version","alias_value":"2606.11206v1","created_at":"2026-06-11T00:08:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.11206","created_at":"2026-06-11T00:08:06Z"},{"alias_kind":"pith_short_12","alias_value":"B6NOZPYG47CH","created_at":"2026-06-11T00:08:06Z"},{"alias_kind":"pith_short_16","alias_value":"B6NOZPYG47CHAHVB","created_at":"2026-06-11T00:08:06Z"},{"alias_kind":"pith_short_8","alias_value":"B6NOZPYG","created_at":"2026-06-11T00:08:06Z"}],"graph_snapshots":[{"event_id":"sha256:9716b8bc60f6d0a6f5b3795371d2b8de89839c70e24b4d41b94c4683a736c5c7","target":"graph","created_at":"2026-06-11T00:08:06Z","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/2606.11206/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Supervised Fine-Tuning (SFT) is the predominant paradigm for aligning large language models (LLMs), yet it suffers from optimization instability and limited generalization. Recent work attributes this issue to pathological gradient scaling and proposes Dynamic Fine-Tuning (DFT) to correct it at the token level. However, DFT assumes all demonstrations are equally suitable learning targets, an assumption violated by the strong heterogeneity of large-scale instruction data, where demonstration-policy mismatch induces high-variance updates at the sample level. We introduce Compatibility-Aware Dyna","authors_text":"Jianbing Shen, Junwei Sheng, Qianning Wang, Yucheng Zhou","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-04-22T14:47:30Z","title":"Compatibility-Aware Dynamic Fine-Tuning for Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.11206","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":"sha256:1ddb4ef60666531e02c7e661ba29fdf75ea110d73948fc582632873be85e824b","target":"record","created_at":"2026-06-11T00:08:06Z","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":"f301f9604e0e3027be1c972d281f67c91cc2416cc4af4610cdedb40bd91d5071","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-04-22T14:47:30Z","title_canon_sha256":"59dc272294d303db585b7c9a6c8a301581f487de46c8673d719351f18d8fc5d3"},"schema_version":"1.0","source":{"id":"2606.11206","kind":"arxiv","version":1}},"canonical_sha256":"0f9aecbf06e7c4701ea1a28b106be61e2707260866a9df6b976adf8d7283ed80","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0f9aecbf06e7c4701ea1a28b106be61e2707260866a9df6b976adf8d7283ed80","first_computed_at":"2026-06-11T00:08:06.449515Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-11T00:08:06.449515Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gOLaJIs2bE4uI9STUi0zeKpDaiIe9ylfQXs/3MrqbMiWVURiwAxuJAGii8tUbCvug5/LrIkg8m+UrMn5TNzaDg==","signature_status":"signed_v1","signed_at":"2026-06-11T00:08:06.450460Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.11206","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1ddb4ef60666531e02c7e661ba29fdf75ea110d73948fc582632873be85e824b","sha256:9716b8bc60f6d0a6f5b3795371d2b8de89839c70e24b4d41b94c4683a736c5c7"],"state_sha256":"1c17bbc3805ddda081e314db5eced0b4f31374715e761bdf12da1ed5bc6300ba"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"alPn5+H1/CfZc30eeco5G031aWYQGs2Rpgnl/cSM9WJSV1CXHNkt7tALVgbGUxPy2EeZlPq0mJftuxyox+UUBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-05T12:52:48.037192Z","bundle_sha256":"25f94d0b13f1f2ada3bc44e6afa0668ee7d84f15c5a083337aa9f922dfd795b9"}}