{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:Y4FHJ6IGZHCNQ242W6GMK76VRR","short_pith_number":"pith:Y4FHJ6IG","canonical_record":{"source":{"id":"2505.21963","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-28T04:30:51Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a1968b9eeebc7fad1bea04905065876eea9b7d0ce9d884e581545806869a42bf","abstract_canon_sha256":"cd453dea408198c0453749232abf07557e840ac47a84b172cd872b44a7e231b1"},"schema_version":"1.0"},"canonical_sha256":"c70a74f906c9c4d86b9ab78cc57fd58c42ac4cf0f4254460ebebd6321419922f","source":{"kind":"arxiv","id":"2505.21963","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.21963","created_at":"2026-07-05T11:11:06Z"},{"alias_kind":"arxiv_version","alias_value":"2505.21963v1","created_at":"2026-07-05T11:11:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.21963","created_at":"2026-07-05T11:11:06Z"},{"alias_kind":"pith_short_12","alias_value":"Y4FHJ6IGZHCN","created_at":"2026-07-05T11:11:06Z"},{"alias_kind":"pith_short_16","alias_value":"Y4FHJ6IGZHCNQ242","created_at":"2026-07-05T11:11:06Z"},{"alias_kind":"pith_short_8","alias_value":"Y4FHJ6IG","created_at":"2026-07-05T11:11:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:Y4FHJ6IGZHCNQ242W6GMK76VRR","target":"record","payload":{"canonical_record":{"source":{"id":"2505.21963","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-28T04:30:51Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a1968b9eeebc7fad1bea04905065876eea9b7d0ce9d884e581545806869a42bf","abstract_canon_sha256":"cd453dea408198c0453749232abf07557e840ac47a84b172cd872b44a7e231b1"},"schema_version":"1.0"},"canonical_sha256":"c70a74f906c9c4d86b9ab78cc57fd58c42ac4cf0f4254460ebebd6321419922f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:11:06.971392Z","signature_b64":"yIgpZ4WFhG1f1HTEC2CblbuH7L1xWIsqCMELJeOaFIFattEM9SGWCjgwrFZxAZIkFVI2Q3UAUOO9cqwY80ajCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c70a74f906c9c4d86b9ab78cc57fd58c42ac4cf0f4254460ebebd6321419922f","last_reissued_at":"2026-07-05T11:11:06.970897Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:11:06.970897Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.21963","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":"2026-07-05T11:11:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VNrFVCgYQjeFm0PtBLbK1vo5r/CAYaf6coMh8ycOQEMGjw5WHEV53A3tptcF1qRyX7pEtqgl7ly7Wt4Qj2EbCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T15:25:23.523384Z"},"content_sha256":"d24f4bb034a7a1866bb746d94b9ad064bfe0cc166c2b40bee4e5441a599fc651","schema_version":"1.0","event_id":"sha256:d24f4bb034a7a1866bb746d94b9ad064bfe0cc166c2b40bee4e5441a599fc651"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:Y4FHJ6IGZHCNQ242W6GMK76VRR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LaMDAgent: An Autonomous Framework for Post-Training Pipeline Optimization via LLM Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Masafumi Oyamada, Taro Yano, Yoichi Ishibashi","submitted_at":"2025-05-28T04:30:51Z","abstract_excerpt":"Large Language Models (LLMs) have demonstrated exceptional performance across a wide range of tasks. To further tailor LLMs to specific domains or applications, post-training techniques such as Supervised Fine-Tuning (SFT), Preference Learning, and model merging are commonly employed. While each of these methods has been extensively studied in isolation, the automated construction of complete post-training pipelines remains an underexplored area. Existing approaches typically rely on manual design or focus narrowly on optimizing individual components, such as data ordering or merging strategie"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.21963","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/2505.21963/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":"2026-07-05T11:11:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9sNTTpHX9WMgw+UGWDGvQx1Ouh80znkEhtUqie3L+QISvu0GGmjxv2wDIiIW0rncpA8zhreoLwMR/RNl1UQxBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T15:25:23.524227Z"},"content_sha256":"9eaab1519f676e4e456ca98e04407f7d2663f25244352298010f0c00d1af2fbe","schema_version":"1.0","event_id":"sha256:9eaab1519f676e4e456ca98e04407f7d2663f25244352298010f0c00d1af2fbe"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Y4FHJ6IGZHCNQ242W6GMK76VRR/bundle.json","state_url":"https://pith.science/pith/Y4FHJ6IGZHCNQ242W6GMK76VRR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Y4FHJ6IGZHCNQ242W6GMK76VRR/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":"2026-08-09T15:25:23Z","links":{"resolver":"https://pith.science/pith/Y4FHJ6IGZHCNQ242W6GMK76VRR","bundle":"https://pith.science/pith/Y4FHJ6IGZHCNQ242W6GMK76VRR/bundle.json","state":"https://pith.science/pith/Y4FHJ6IGZHCNQ242W6GMK76VRR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Y4FHJ6IGZHCNQ242W6GMK76VRR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:Y4FHJ6IGZHCNQ242W6GMK76VRR","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":"cd453dea408198c0453749232abf07557e840ac47a84b172cd872b44a7e231b1","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-28T04:30:51Z","title_canon_sha256":"a1968b9eeebc7fad1bea04905065876eea9b7d0ce9d884e581545806869a42bf"},"schema_version":"1.0","source":{"id":"2505.21963","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.21963","created_at":"2026-07-05T11:11:06Z"},{"alias_kind":"arxiv_version","alias_value":"2505.21963v1","created_at":"2026-07-05T11:11:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.21963","created_at":"2026-07-05T11:11:06Z"},{"alias_kind":"pith_short_12","alias_value":"Y4FHJ6IGZHCN","created_at":"2026-07-05T11:11:06Z"},{"alias_kind":"pith_short_16","alias_value":"Y4FHJ6IGZHCNQ242","created_at":"2026-07-05T11:11:06Z"},{"alias_kind":"pith_short_8","alias_value":"Y4FHJ6IG","created_at":"2026-07-05T11:11:06Z"}],"graph_snapshots":[{"event_id":"sha256:9eaab1519f676e4e456ca98e04407f7d2663f25244352298010f0c00d1af2fbe","target":"graph","created_at":"2026-07-05T11:11:06Z","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/2505.21963/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) have demonstrated exceptional performance across a wide range of tasks. To further tailor LLMs to specific domains or applications, post-training techniques such as Supervised Fine-Tuning (SFT), Preference Learning, and model merging are commonly employed. While each of these methods has been extensively studied in isolation, the automated construction of complete post-training pipelines remains an underexplored area. Existing approaches typically rely on manual design or focus narrowly on optimizing individual components, such as data ordering or merging strategie","authors_text":"Masafumi Oyamada, Taro Yano, Yoichi Ishibashi","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-28T04:30:51Z","title":"LaMDAgent: An Autonomous Framework for Post-Training Pipeline Optimization via LLM Agents"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.21963","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:d24f4bb034a7a1866bb746d94b9ad064bfe0cc166c2b40bee4e5441a599fc651","target":"record","created_at":"2026-07-05T11:11:06Z","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":"cd453dea408198c0453749232abf07557e840ac47a84b172cd872b44a7e231b1","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-28T04:30:51Z","title_canon_sha256":"a1968b9eeebc7fad1bea04905065876eea9b7d0ce9d884e581545806869a42bf"},"schema_version":"1.0","source":{"id":"2505.21963","kind":"arxiv","version":1}},"canonical_sha256":"c70a74f906c9c4d86b9ab78cc57fd58c42ac4cf0f4254460ebebd6321419922f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c70a74f906c9c4d86b9ab78cc57fd58c42ac4cf0f4254460ebebd6321419922f","first_computed_at":"2026-07-05T11:11:06.970897Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:11:06.970897Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yIgpZ4WFhG1f1HTEC2CblbuH7L1xWIsqCMELJeOaFIFattEM9SGWCjgwrFZxAZIkFVI2Q3UAUOO9cqwY80ajCg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:11:06.971392Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.21963","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d24f4bb034a7a1866bb746d94b9ad064bfe0cc166c2b40bee4e5441a599fc651","sha256:9eaab1519f676e4e456ca98e04407f7d2663f25244352298010f0c00d1af2fbe"],"state_sha256":"28a2f2d070679062389b9bc0fd8537e435d68a4f6ae9cbf792af85dde6b2c47d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0EZaF+b/54SPszRBtBkEfys4D6J/07dgiu1jre30lD0ITi+ziD3nUvb9taZ9bHH1bU0FxSQhHtFUQwkJE78JCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T15:25:23.531690Z","bundle_sha256":"976df1d0d6a23f45e9c0c65045b5a153d2f6ff29eeae7d001719048b4bbb8670"}}