{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:CT6ZAVMFO5XAGNEUVRL6EO7IFY","short_pith_number":"pith:CT6ZAVMF","canonical_record":{"source":{"id":"2411.01168","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-02T07:38:02Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6404d19efa0e4f6dcaf108f7cbede4ee6e6df44212c2dbcf09e866a4d626c500","abstract_canon_sha256":"f299642929938d0fd0ab78960a14598966f1bbf9e1a7e3532ec69e7938fb8738"},"schema_version":"1.0"},"canonical_sha256":"14fd905585776e033494ac57e23be82e2b7253f12e937e59fb4da1098e077c2c","source":{"kind":"arxiv","id":"2411.01168","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.01168","created_at":"2026-07-05T09:30:25Z"},{"alias_kind":"arxiv_version","alias_value":"2411.01168v1","created_at":"2026-07-05T09:30:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.01168","created_at":"2026-07-05T09:30:25Z"},{"alias_kind":"pith_short_12","alias_value":"CT6ZAVMFO5XA","created_at":"2026-07-05T09:30:25Z"},{"alias_kind":"pith_short_16","alias_value":"CT6ZAVMFO5XAGNEU","created_at":"2026-07-05T09:30:25Z"},{"alias_kind":"pith_short_8","alias_value":"CT6ZAVMF","created_at":"2026-07-05T09:30:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:CT6ZAVMFO5XAGNEUVRL6EO7IFY","target":"record","payload":{"canonical_record":{"source":{"id":"2411.01168","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-02T07:38:02Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6404d19efa0e4f6dcaf108f7cbede4ee6e6df44212c2dbcf09e866a4d626c500","abstract_canon_sha256":"f299642929938d0fd0ab78960a14598966f1bbf9e1a7e3532ec69e7938fb8738"},"schema_version":"1.0"},"canonical_sha256":"14fd905585776e033494ac57e23be82e2b7253f12e937e59fb4da1098e077c2c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:30:25.991187Z","signature_b64":"cNT3ypuSs4e4w9URWzX8kuCpGezQOnHl9GiZ9nJT4/DfFiWsOSVcc1SXK+jdPMJVkBxQ1gamMfv9NvgCO/o1Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"14fd905585776e033494ac57e23be82e2b7253f12e937e59fb4da1098e077c2c","last_reissued_at":"2026-07-05T09:30:25.990670Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:30:25.990670Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.01168","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":"2026-07-05T09:30:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/QsM6tEWVvRyGSlehUCu43ST+MRTdkTDqZQXKrP8M112hvT4xsJnL9+qwN7kpcU2dFMewUwv4NLf8MQwlL3MAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T17:49:08.625901Z"},"content_sha256":"91cee623bc6a7d6a7e9f37a88453c119abedad5e4a3f124777dd2e3381aaf779","schema_version":"1.0","event_id":"sha256:91cee623bc6a7d6a7e9f37a88453c119abedad5e4a3f124777dd2e3381aaf779"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:CT6ZAVMFO5XAGNEUVRL6EO7IFY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Prompt Tuning with Diffusion for Few-Shot Pre-trained Policy Generalization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Dacheng Tao, Li Shen, Shengchao Hu, Wanru Zhao, Weixiong Lin, Ya Zhang","submitted_at":"2024-11-02T07:38:02Z","abstract_excerpt":"Offline reinforcement learning (RL) methods harness previous experiences to derive an optimal policy, forming the foundation for pre-trained large-scale models (PLMs). When encountering tasks not seen before, PLMs often utilize several expert trajectories as prompts to expedite their adaptation to new requirements. Though a range of prompt-tuning methods have been proposed to enhance the quality of prompts, these methods often face optimization restrictions due to prompt initialization, which can significantly constrain the exploration domain and potentially lead to suboptimal solutions. To el"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.01168","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/2411.01168/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":"2026-07-05T09:30:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"901xfGKHGLl0sYKgEZMFFUI5E/tIRHVuWpAuCmEpC+h+EaQBIcd6vQ1q59jjXZu/APcMPVHVG029dQthbnpUCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T17:49:08.627166Z"},"content_sha256":"79622d48a5abd55887ad722187e35a36b94f8859a947657d25a9afbdad67b356","schema_version":"1.0","event_id":"sha256:79622d48a5abd55887ad722187e35a36b94f8859a947657d25a9afbdad67b356"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CT6ZAVMFO5XAGNEUVRL6EO7IFY/bundle.json","state_url":"https://pith.science/pith/CT6ZAVMFO5XAGNEUVRL6EO7IFY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CT6ZAVMFO5XAGNEUVRL6EO7IFY/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":"2026-08-10T17:49:08Z","links":{"resolver":"https://pith.science/pith/CT6ZAVMFO5XAGNEUVRL6EO7IFY","bundle":"https://pith.science/pith/CT6ZAVMFO5XAGNEUVRL6EO7IFY/bundle.json","state":"https://pith.science/pith/CT6ZAVMFO5XAGNEUVRL6EO7IFY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CT6ZAVMFO5XAGNEUVRL6EO7IFY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:CT6ZAVMFO5XAGNEUVRL6EO7IFY","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":"f299642929938d0fd0ab78960a14598966f1bbf9e1a7e3532ec69e7938fb8738","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-02T07:38:02Z","title_canon_sha256":"6404d19efa0e4f6dcaf108f7cbede4ee6e6df44212c2dbcf09e866a4d626c500"},"schema_version":"1.0","source":{"id":"2411.01168","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.01168","created_at":"2026-07-05T09:30:25Z"},{"alias_kind":"arxiv_version","alias_value":"2411.01168v1","created_at":"2026-07-05T09:30:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.01168","created_at":"2026-07-05T09:30:25Z"},{"alias_kind":"pith_short_12","alias_value":"CT6ZAVMFO5XA","created_at":"2026-07-05T09:30:25Z"},{"alias_kind":"pith_short_16","alias_value":"CT6ZAVMFO5XAGNEU","created_at":"2026-07-05T09:30:25Z"},{"alias_kind":"pith_short_8","alias_value":"CT6ZAVMF","created_at":"2026-07-05T09:30:25Z"}],"graph_snapshots":[{"event_id":"sha256:79622d48a5abd55887ad722187e35a36b94f8859a947657d25a9afbdad67b356","target":"graph","created_at":"2026-07-05T09:30:25Z","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/2411.01168/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Offline reinforcement learning (RL) methods harness previous experiences to derive an optimal policy, forming the foundation for pre-trained large-scale models (PLMs). When encountering tasks not seen before, PLMs often utilize several expert trajectories as prompts to expedite their adaptation to new requirements. Though a range of prompt-tuning methods have been proposed to enhance the quality of prompts, these methods often face optimization restrictions due to prompt initialization, which can significantly constrain the exploration domain and potentially lead to suboptimal solutions. To el","authors_text":"Dacheng Tao, Li Shen, Shengchao Hu, Wanru Zhao, Weixiong Lin, Ya Zhang","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-02T07:38:02Z","title":"Prompt Tuning with Diffusion for Few-Shot Pre-trained Policy Generalization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.01168","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":"sha256:91cee623bc6a7d6a7e9f37a88453c119abedad5e4a3f124777dd2e3381aaf779","target":"record","created_at":"2026-07-05T09:30:25Z","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":"f299642929938d0fd0ab78960a14598966f1bbf9e1a7e3532ec69e7938fb8738","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-02T07:38:02Z","title_canon_sha256":"6404d19efa0e4f6dcaf108f7cbede4ee6e6df44212c2dbcf09e866a4d626c500"},"schema_version":"1.0","source":{"id":"2411.01168","kind":"arxiv","version":1}},"canonical_sha256":"14fd905585776e033494ac57e23be82e2b7253f12e937e59fb4da1098e077c2c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"14fd905585776e033494ac57e23be82e2b7253f12e937e59fb4da1098e077c2c","first_computed_at":"2026-07-05T09:30:25.990670Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:30:25.990670Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cNT3ypuSs4e4w9URWzX8kuCpGezQOnHl9GiZ9nJT4/DfFiWsOSVcc1SXK+jdPMJVkBxQ1gamMfv9NvgCO/o1Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:30:25.991187Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.01168","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:91cee623bc6a7d6a7e9f37a88453c119abedad5e4a3f124777dd2e3381aaf779","sha256:79622d48a5abd55887ad722187e35a36b94f8859a947657d25a9afbdad67b356"],"state_sha256":"46be38651e94a0e42c24a4cac569361b39dd6422f0f7874264e07dd58d61cf80"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IJJR/WdWwTa1eyUlGVNPEh4eQKhK9mW4Y6RySWfQvz68B1NAjDqrhh1ofS1jttjOnOvVhiQ1kNCriCDnBO2HAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T17:49:08.631605Z","bundle_sha256":"a7fd5a9d8c52a8f81a782a06e322ef3dcace0f2636613594d9f1e25889cfaa71"}}