{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:NGIVPJFF7JNHSIELUBWXADFHNR","short_pith_number":"pith:NGIVPJFF","canonical_record":{"source":{"id":"2506.16688","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-20T02:12:04Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1db982e3e855b68a147282c04b7326632ac7dcd5fbc5097159e1bc025de25d3a","abstract_canon_sha256":"594769b71490aae1cfac7e183be810b00b99d264aa19eb4774eae290dbe8b18e"},"schema_version":"1.0"},"canonical_sha256":"699157a4a5fa5a79208ba06d700ca76c4acb23f6cf9278f892a8726bf61862a3","source":{"kind":"arxiv","id":"2506.16688","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.16688","created_at":"2026-07-05T11:24:40Z"},{"alias_kind":"arxiv_version","alias_value":"2506.16688v1","created_at":"2026-07-05T11:24:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.16688","created_at":"2026-07-05T11:24:40Z"},{"alias_kind":"pith_short_12","alias_value":"NGIVPJFF7JNH","created_at":"2026-07-05T11:24:40Z"},{"alias_kind":"pith_short_16","alias_value":"NGIVPJFF7JNHSIEL","created_at":"2026-07-05T11:24:40Z"},{"alias_kind":"pith_short_8","alias_value":"NGIVPJFF","created_at":"2026-07-05T11:24:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:NGIVPJFF7JNHSIELUBWXADFHNR","target":"record","payload":{"canonical_record":{"source":{"id":"2506.16688","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-20T02:12:04Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1db982e3e855b68a147282c04b7326632ac7dcd5fbc5097159e1bc025de25d3a","abstract_canon_sha256":"594769b71490aae1cfac7e183be810b00b99d264aa19eb4774eae290dbe8b18e"},"schema_version":"1.0"},"canonical_sha256":"699157a4a5fa5a79208ba06d700ca76c4acb23f6cf9278f892a8726bf61862a3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:24:40.880014Z","signature_b64":"CLLU1qd4S/prk0uNjvZigs8wnu6LI8Zxx//Gp1Qi1j+N1c3AX0Z3ibbo9xhHQDIdEG+t2rN+AlsjRr3rSr5XCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"699157a4a5fa5a79208ba06d700ca76c4acb23f6cf9278f892a8726bf61862a3","last_reissued_at":"2026-07-05T11:24:40.879536Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:24:40.879536Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.16688","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":"2026-07-05T11:24:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"U9gphPGXqi6wUgvETLIB1X9KcWEHNjUFcfxOc56T7ueQY8hOBxAbme/0RTf/mRz+Ogpf3P8MZ/5nwr1dtPErDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T04:04:59.468132Z"},"content_sha256":"6b6bd1a1bedda8b41dde339245f61ecd1dc32430ec7870ebcb4beae640da232b","schema_version":"1.0","event_id":"sha256:6b6bd1a1bedda8b41dde339245f61ecd1dc32430ec7870ebcb4beae640da232b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:NGIVPJFF7JNHSIELUBWXADFHNR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fast and Stable Diffusion Planning through Variational Adaptive Weighting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Tao Lin, Zhiying Qiu","submitted_at":"2025-06-20T02:12:04Z","abstract_excerpt":"Diffusion models have recently shown promise in offline RL. However, these methods often suffer from high training costs and slow convergence, particularly when using transformer-based denoising backbones. While several optimization strategies have been proposed -- such as modified noise schedules, auxiliary prediction targets, and adaptive loss weighting -- challenges remain in achieving stable and efficient training. In particular, existing loss weighting functions typically rely on neural network approximators, which can be ineffective in early training phases due to limited generalization "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.16688","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/2506.16688/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":"2026-07-05T11:24:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JiMIJYz9utRr5D4knrwQIKPsbxJIt/QddoPAhf/OiCRgeKDNqrBklDGGxnIguN52m10CJdtZmUXQATv9KK8BBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T04:04:59.469167Z"},"content_sha256":"368a072908810bc0a37dcc1415e9dba1f0a06c5c5fc032502fab463f760e4dbc","schema_version":"1.0","event_id":"sha256:368a072908810bc0a37dcc1415e9dba1f0a06c5c5fc032502fab463f760e4dbc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NGIVPJFF7JNHSIELUBWXADFHNR/bundle.json","state_url":"https://pith.science/pith/NGIVPJFF7JNHSIELUBWXADFHNR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NGIVPJFF7JNHSIELUBWXADFHNR/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":"2026-08-17T04:04:59Z","links":{"resolver":"https://pith.science/pith/NGIVPJFF7JNHSIELUBWXADFHNR","bundle":"https://pith.science/pith/NGIVPJFF7JNHSIELUBWXADFHNR/bundle.json","state":"https://pith.science/pith/NGIVPJFF7JNHSIELUBWXADFHNR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NGIVPJFF7JNHSIELUBWXADFHNR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:NGIVPJFF7JNHSIELUBWXADFHNR","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":"594769b71490aae1cfac7e183be810b00b99d264aa19eb4774eae290dbe8b18e","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-20T02:12:04Z","title_canon_sha256":"1db982e3e855b68a147282c04b7326632ac7dcd5fbc5097159e1bc025de25d3a"},"schema_version":"1.0","source":{"id":"2506.16688","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.16688","created_at":"2026-07-05T11:24:40Z"},{"alias_kind":"arxiv_version","alias_value":"2506.16688v1","created_at":"2026-07-05T11:24:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.16688","created_at":"2026-07-05T11:24:40Z"},{"alias_kind":"pith_short_12","alias_value":"NGIVPJFF7JNH","created_at":"2026-07-05T11:24:40Z"},{"alias_kind":"pith_short_16","alias_value":"NGIVPJFF7JNHSIEL","created_at":"2026-07-05T11:24:40Z"},{"alias_kind":"pith_short_8","alias_value":"NGIVPJFF","created_at":"2026-07-05T11:24:40Z"}],"graph_snapshots":[{"event_id":"sha256:368a072908810bc0a37dcc1415e9dba1f0a06c5c5fc032502fab463f760e4dbc","target":"graph","created_at":"2026-07-05T11:24:40Z","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/2506.16688/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Diffusion models have recently shown promise in offline RL. However, these methods often suffer from high training costs and slow convergence, particularly when using transformer-based denoising backbones. While several optimization strategies have been proposed -- such as modified noise schedules, auxiliary prediction targets, and adaptive loss weighting -- challenges remain in achieving stable and efficient training. In particular, existing loss weighting functions typically rely on neural network approximators, which can be ineffective in early training phases due to limited generalization ","authors_text":"Tao Lin, Zhiying Qiu","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-20T02:12:04Z","title":"Fast and Stable Diffusion Planning through Variational Adaptive Weighting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.16688","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":"sha256:6b6bd1a1bedda8b41dde339245f61ecd1dc32430ec7870ebcb4beae640da232b","target":"record","created_at":"2026-07-05T11:24:40Z","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":"594769b71490aae1cfac7e183be810b00b99d264aa19eb4774eae290dbe8b18e","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-20T02:12:04Z","title_canon_sha256":"1db982e3e855b68a147282c04b7326632ac7dcd5fbc5097159e1bc025de25d3a"},"schema_version":"1.0","source":{"id":"2506.16688","kind":"arxiv","version":1}},"canonical_sha256":"699157a4a5fa5a79208ba06d700ca76c4acb23f6cf9278f892a8726bf61862a3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"699157a4a5fa5a79208ba06d700ca76c4acb23f6cf9278f892a8726bf61862a3","first_computed_at":"2026-07-05T11:24:40.879536Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:24:40.879536Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CLLU1qd4S/prk0uNjvZigs8wnu6LI8Zxx//Gp1Qi1j+N1c3AX0Z3ibbo9xhHQDIdEG+t2rN+AlsjRr3rSr5XCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:24:40.880014Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.16688","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6b6bd1a1bedda8b41dde339245f61ecd1dc32430ec7870ebcb4beae640da232b","sha256:368a072908810bc0a37dcc1415e9dba1f0a06c5c5fc032502fab463f760e4dbc"],"state_sha256":"b07538addb1a14357d6562157e5d3c0ed2c6c0f76a37f8a27c033ccc1088b394"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l0udWim7uskZdzrk1uot4vaW4Tw1Q9BSxGhRLNTIQIsoG2NjsW4AzzKoKWpnOvnbmcvWd34Hmir6zwVj/sYeDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T04:04:59.475468Z","bundle_sha256":"fd9da315560a7e55ad31f5da5c6b7e70db3c175bf6284dbb561760e5b96a5533"}}