{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:AKGZG3SFNXE3XLIX7YZFOY7GA5","short_pith_number":"pith:AKGZG3SF","canonical_record":{"source":{"id":"2503.13413","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-03-17T17:42:51Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"bf4bd16eae7f4fc82c9ddfdae07ec361353c52e86a57650ebe78cdaab9932f8d","abstract_canon_sha256":"291fe8003c361e675f6e207c789ff2247697b9d1eb1af586a737daf90bbe1f15"},"schema_version":"1.0"},"canonical_sha256":"028d936e456dc9bbad17fe325763e60765ee5f8294db9602a25f36502b4b9bc8","source":{"kind":"arxiv","id":"2503.13413","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.13413","created_at":"2026-07-05T10:34:43Z"},{"alias_kind":"arxiv_version","alias_value":"2503.13413v3","created_at":"2026-07-05T10:34:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.13413","created_at":"2026-07-05T10:34:43Z"},{"alias_kind":"pith_short_12","alias_value":"AKGZG3SFNXE3","created_at":"2026-07-05T10:34:43Z"},{"alias_kind":"pith_short_16","alias_value":"AKGZG3SFNXE3XLIX","created_at":"2026-07-05T10:34:43Z"},{"alias_kind":"pith_short_8","alias_value":"AKGZG3SF","created_at":"2026-07-05T10:34:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:AKGZG3SFNXE3XLIX7YZFOY7GA5","target":"record","payload":{"canonical_record":{"source":{"id":"2503.13413","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-03-17T17:42:51Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"bf4bd16eae7f4fc82c9ddfdae07ec361353c52e86a57650ebe78cdaab9932f8d","abstract_canon_sha256":"291fe8003c361e675f6e207c789ff2247697b9d1eb1af586a737daf90bbe1f15"},"schema_version":"1.0"},"canonical_sha256":"028d936e456dc9bbad17fe325763e60765ee5f8294db9602a25f36502b4b9bc8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:34:43.455965Z","signature_b64":"zxnkpUlwb4rHxqC3DL/zOLXZktSc0Uy/0L9dc6O0Bw4F9YMddQQ656Cjh++/qJ80Bt3Uq6Jf2iXuadUTTwoZDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"028d936e456dc9bbad17fe325763e60765ee5f8294db9602a25f36502b4b9bc8","last_reissued_at":"2026-07-05T10:34:43.455486Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:34:43.455486Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2503.13413","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:34:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LUJo3OIwxh5kp+3a4S6LBjAlh5l+3rndAsEvhUESIUf5FbeGeg1aSHofKBMwyCdM/HGhIFf/qpDryBpbT+FqCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T18:38:13.760023Z"},"content_sha256":"604a299b6cbd3b85d8eec1ecbffed46b4b503eaf2743278cab19ddeb48d42ed9","schema_version":"1.0","event_id":"sha256:604a299b6cbd3b85d8eec1ecbffed46b4b503eaf2743278cab19ddeb48d42ed9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:AKGZG3SFNXE3XLIX7YZFOY7GA5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DLPO: Towards a Robust, Efficient, and Generalizable Prompt Optimization Framework from a Deep-Learning Perspective","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Dengyun Peng, Jingjing Chen, Jinhao Liu, Libo Qin, Qiguang Chen, Yuhang Zhou","submitted_at":"2025-03-17T17:42:51Z","abstract_excerpt":"Large Language Models (LLMs) have achieved remarkable success across diverse tasks, largely driven by well-designed prompts. However, crafting and selecting such prompts often requires considerable human effort, significantly limiting its scalability. To mitigate this, recent studies have explored automated prompt optimization as a promising solution. Despite these efforts, existing methods still face critical challenges in robustness, efficiency, and generalization. To systematically address these challenges, we first conduct an empirical analysis to identify the limitations of current reflec"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.13413","kind":"arxiv","version":3},"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/2503.13413/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":"2026-07-05T10:34:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xlfwmDTQa00KE5MAPG36VBZszh1axZiEPF7Qyyj65aWD8l2P9E/r0YFQAZ/N9MGyUhEJNhNh5MUJll046JY4DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T18:38:13.760493Z"},"content_sha256":"6159a8c1d97b4bb81b90112fbc7af70bbe35dbcec8f3a1e38119661f7a01c672","schema_version":"1.0","event_id":"sha256:6159a8c1d97b4bb81b90112fbc7af70bbe35dbcec8f3a1e38119661f7a01c672"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AKGZG3SFNXE3XLIX7YZFOY7GA5/bundle.json","state_url":"https://pith.science/pith/AKGZG3SFNXE3XLIX7YZFOY7GA5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AKGZG3SFNXE3XLIX7YZFOY7GA5/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":"2026-08-05T18:38:13Z","links":{"resolver":"https://pith.science/pith/AKGZG3SFNXE3XLIX7YZFOY7GA5","bundle":"https://pith.science/pith/AKGZG3SFNXE3XLIX7YZFOY7GA5/bundle.json","state":"https://pith.science/pith/AKGZG3SFNXE3XLIX7YZFOY7GA5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AKGZG3SFNXE3XLIX7YZFOY7GA5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:AKGZG3SFNXE3XLIX7YZFOY7GA5","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":"291fe8003c361e675f6e207c789ff2247697b9d1eb1af586a737daf90bbe1f15","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-03-17T17:42:51Z","title_canon_sha256":"bf4bd16eae7f4fc82c9ddfdae07ec361353c52e86a57650ebe78cdaab9932f8d"},"schema_version":"1.0","source":{"id":"2503.13413","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.13413","created_at":"2026-07-05T10:34:43Z"},{"alias_kind":"arxiv_version","alias_value":"2503.13413v3","created_at":"2026-07-05T10:34:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.13413","created_at":"2026-07-05T10:34:43Z"},{"alias_kind":"pith_short_12","alias_value":"AKGZG3SFNXE3","created_at":"2026-07-05T10:34:43Z"},{"alias_kind":"pith_short_16","alias_value":"AKGZG3SFNXE3XLIX","created_at":"2026-07-05T10:34:43Z"},{"alias_kind":"pith_short_8","alias_value":"AKGZG3SF","created_at":"2026-07-05T10:34:43Z"}],"graph_snapshots":[{"event_id":"sha256:6159a8c1d97b4bb81b90112fbc7af70bbe35dbcec8f3a1e38119661f7a01c672","target":"graph","created_at":"2026-07-05T10:34:43Z","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/2503.13413/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) have achieved remarkable success across diverse tasks, largely driven by well-designed prompts. However, crafting and selecting such prompts often requires considerable human effort, significantly limiting its scalability. To mitigate this, recent studies have explored automated prompt optimization as a promising solution. Despite these efforts, existing methods still face critical challenges in robustness, efficiency, and generalization. To systematically address these challenges, we first conduct an empirical analysis to identify the limitations of current reflec","authors_text":"Dengyun Peng, Jingjing Chen, Jinhao Liu, Libo Qin, Qiguang Chen, Yuhang Zhou","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-03-17T17:42:51Z","title":"DLPO: Towards a Robust, Efficient, and Generalizable Prompt Optimization Framework from a Deep-Learning Perspective"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.13413","kind":"arxiv","version":3},"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":"sha256:604a299b6cbd3b85d8eec1ecbffed46b4b503eaf2743278cab19ddeb48d42ed9","target":"record","created_at":"2026-07-05T10:34:43Z","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":"291fe8003c361e675f6e207c789ff2247697b9d1eb1af586a737daf90bbe1f15","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-03-17T17:42:51Z","title_canon_sha256":"bf4bd16eae7f4fc82c9ddfdae07ec361353c52e86a57650ebe78cdaab9932f8d"},"schema_version":"1.0","source":{"id":"2503.13413","kind":"arxiv","version":3}},"canonical_sha256":"028d936e456dc9bbad17fe325763e60765ee5f8294db9602a25f36502b4b9bc8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"028d936e456dc9bbad17fe325763e60765ee5f8294db9602a25f36502b4b9bc8","first_computed_at":"2026-07-05T10:34:43.455486Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:34:43.455486Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zxnkpUlwb4rHxqC3DL/zOLXZktSc0Uy/0L9dc6O0Bw4F9YMddQQ656Cjh++/qJ80Bt3Uq6Jf2iXuadUTTwoZDA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:34:43.455965Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.13413","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:604a299b6cbd3b85d8eec1ecbffed46b4b503eaf2743278cab19ddeb48d42ed9","sha256:6159a8c1d97b4bb81b90112fbc7af70bbe35dbcec8f3a1e38119661f7a01c672"],"state_sha256":"f3e6980cbf03ea8fa5690f95db02b6678c9382e42096c17aa00f0cf583f76d0c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iLoJRrX0eV1lO/I6voOV6sR1XICPbf6OURH79SA4006J3kRAmaBtcmo2UXtK8+9DohAG3pDpn23KYgYGoGphCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T18:38:13.763732Z","bundle_sha256":"7b3cb8e2e6682fbbc47d5c80b1443a6761087fbb5fbefab02c21677fb70b2150"}}