{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:HWFGHPV5NOLKDKGGCO6KSFW2NW","short_pith_number":"pith:HWFGHPV5","canonical_record":{"source":{"id":"2410.13073","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-16T22:25:15Z","cross_cats_sorted":[],"title_canon_sha256":"e67cfd8949974da67d2772d6bfaadfa857ecb87310bdbfac30e3616b199bd83f","abstract_canon_sha256":"02dc1e2d3f4a963bc7a729f27831c045c509c383dd68e694d223082bcfa4e193"},"schema_version":"1.0"},"canonical_sha256":"3d8a63bebd6b96a1a8c613bca916da6da683957031412025fb44fa14dc3415b9","source":{"kind":"arxiv","id":"2410.13073","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.13073","created_at":"2026-07-05T09:28:34Z"},{"alias_kind":"arxiv_version","alias_value":"2410.13073v3","created_at":"2026-07-05T09:28:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.13073","created_at":"2026-07-05T09:28:34Z"},{"alias_kind":"pith_short_12","alias_value":"HWFGHPV5NOLK","created_at":"2026-07-05T09:28:34Z"},{"alias_kind":"pith_short_16","alias_value":"HWFGHPV5NOLKDKGG","created_at":"2026-07-05T09:28:34Z"},{"alias_kind":"pith_short_8","alias_value":"HWFGHPV5","created_at":"2026-07-05T09:28:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:HWFGHPV5NOLKDKGGCO6KSFW2NW","target":"record","payload":{"canonical_record":{"source":{"id":"2410.13073","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-16T22:25:15Z","cross_cats_sorted":[],"title_canon_sha256":"e67cfd8949974da67d2772d6bfaadfa857ecb87310bdbfac30e3616b199bd83f","abstract_canon_sha256":"02dc1e2d3f4a963bc7a729f27831c045c509c383dd68e694d223082bcfa4e193"},"schema_version":"1.0"},"canonical_sha256":"3d8a63bebd6b96a1a8c613bca916da6da683957031412025fb44fa14dc3415b9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:28:34.861809Z","signature_b64":"rB6Qx3k+yXY6E8HMqNCABrVj5TLP8oCFmbY1HpBI6X9PIw+EdvFuvUTa1Yo6eCrdba1Bv5zMXye2tyYbOjqcCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d8a63bebd6b96a1a8c613bca916da6da683957031412025fb44fa14dc3415b9","last_reissued_at":"2026-07-05T09:28:34.861385Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:28:34.861385Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.13073","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":"2026-07-05T09:28:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"o4mdQovZwXpe7gYJ/cSfsh1PeGesXz8QsLlnvNsWtE6rsP/VK1lT+gWU+ID3NrS4vxLqJhXtk7mFzuxHEQQeAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T00:55:13.254807Z"},"content_sha256":"3cbbcb1fd81353351f18b02eff9a5bddfa5d34dd1f81a73491ceec7785d2d1ff","schema_version":"1.0","event_id":"sha256:3cbbcb1fd81353351f18b02eff9a5bddfa5d34dd1f81a73491ceec7785d2d1ff"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:HWFGHPV5NOLKDKGGCO6KSFW2NW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"PromptExp: Multi-granularity Prompt Explanation of Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ahmed E. Hassan, Boquan Zhou, Dayi Lin, Gopi Krishnan Rajbahadur, Shaowei Wang, Shichao Liu, Ximing Dong","submitted_at":"2024-10-16T22:25:15Z","abstract_excerpt":"Large Language Models excel in tasks like natural language understanding and text generation. Prompt engineering plays a critical role in leveraging LLM effectively. However, LLMs black-box nature hinders its interpretability and effective prompting engineering. A wide range of model explanation approaches have been developed for deep learning models, However, these local explanations are designed for single-output tasks like classification and regression,and cannot be directly applied to LLMs, which generate sequences of tokens. Recent efforts in LLM explanation focus on natural language expl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.13073","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/2410.13073/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":"2026-07-05T09:28:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tPfGgAOoTPMHUu7z1h032S5t9eGNokbYmbr49zlolLFGISvBbe7mMFE9kWYrKsUQ3hmRZTYKaDXHOZCr6sW+Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T00:55:13.255295Z"},"content_sha256":"9d6ca49b88103fd311d15e4a5749c73f6ba6f88c70b09c662a7327f488e35207","schema_version":"1.0","event_id":"sha256:9d6ca49b88103fd311d15e4a5749c73f6ba6f88c70b09c662a7327f488e35207"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HWFGHPV5NOLKDKGGCO6KSFW2NW/bundle.json","state_url":"https://pith.science/pith/HWFGHPV5NOLKDKGGCO6KSFW2NW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HWFGHPV5NOLKDKGGCO6KSFW2NW/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":"2026-08-10T00:55:13Z","links":{"resolver":"https://pith.science/pith/HWFGHPV5NOLKDKGGCO6KSFW2NW","bundle":"https://pith.science/pith/HWFGHPV5NOLKDKGGCO6KSFW2NW/bundle.json","state":"https://pith.science/pith/HWFGHPV5NOLKDKGGCO6KSFW2NW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HWFGHPV5NOLKDKGGCO6KSFW2NW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:HWFGHPV5NOLKDKGGCO6KSFW2NW","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":"02dc1e2d3f4a963bc7a729f27831c045c509c383dd68e694d223082bcfa4e193","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-16T22:25:15Z","title_canon_sha256":"e67cfd8949974da67d2772d6bfaadfa857ecb87310bdbfac30e3616b199bd83f"},"schema_version":"1.0","source":{"id":"2410.13073","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.13073","created_at":"2026-07-05T09:28:34Z"},{"alias_kind":"arxiv_version","alias_value":"2410.13073v3","created_at":"2026-07-05T09:28:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.13073","created_at":"2026-07-05T09:28:34Z"},{"alias_kind":"pith_short_12","alias_value":"HWFGHPV5NOLK","created_at":"2026-07-05T09:28:34Z"},{"alias_kind":"pith_short_16","alias_value":"HWFGHPV5NOLKDKGG","created_at":"2026-07-05T09:28:34Z"},{"alias_kind":"pith_short_8","alias_value":"HWFGHPV5","created_at":"2026-07-05T09:28:34Z"}],"graph_snapshots":[{"event_id":"sha256:9d6ca49b88103fd311d15e4a5749c73f6ba6f88c70b09c662a7327f488e35207","target":"graph","created_at":"2026-07-05T09:28:34Z","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/2410.13073/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models excel in tasks like natural language understanding and text generation. Prompt engineering plays a critical role in leveraging LLM effectively. However, LLMs black-box nature hinders its interpretability and effective prompting engineering. A wide range of model explanation approaches have been developed for deep learning models, However, these local explanations are designed for single-output tasks like classification and regression,and cannot be directly applied to LLMs, which generate sequences of tokens. Recent efforts in LLM explanation focus on natural language expl","authors_text":"Ahmed E. Hassan, Boquan Zhou, Dayi Lin, Gopi Krishnan Rajbahadur, Shaowei Wang, Shichao Liu, Ximing Dong","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-16T22:25:15Z","title":"PromptExp: Multi-granularity Prompt Explanation of Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.13073","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":"sha256:3cbbcb1fd81353351f18b02eff9a5bddfa5d34dd1f81a73491ceec7785d2d1ff","target":"record","created_at":"2026-07-05T09:28:34Z","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":"02dc1e2d3f4a963bc7a729f27831c045c509c383dd68e694d223082bcfa4e193","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-16T22:25:15Z","title_canon_sha256":"e67cfd8949974da67d2772d6bfaadfa857ecb87310bdbfac30e3616b199bd83f"},"schema_version":"1.0","source":{"id":"2410.13073","kind":"arxiv","version":3}},"canonical_sha256":"3d8a63bebd6b96a1a8c613bca916da6da683957031412025fb44fa14dc3415b9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3d8a63bebd6b96a1a8c613bca916da6da683957031412025fb44fa14dc3415b9","first_computed_at":"2026-07-05T09:28:34.861385Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:28:34.861385Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rB6Qx3k+yXY6E8HMqNCABrVj5TLP8oCFmbY1HpBI6X9PIw+EdvFuvUTa1Yo6eCrdba1Bv5zMXye2tyYbOjqcCg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:28:34.861809Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.13073","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3cbbcb1fd81353351f18b02eff9a5bddfa5d34dd1f81a73491ceec7785d2d1ff","sha256:9d6ca49b88103fd311d15e4a5749c73f6ba6f88c70b09c662a7327f488e35207"],"state_sha256":"f08359145bf036ba8aa4f971f1522e9720519aaab3e41c1643b489fbb9951fac"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WiDvHa+yAmeSSrx8ApmU+5sXmJ+MUR5R+kGNaClJnbpJa6HZZSRDA3YlOjJdy93guRJbYLaEJAY288Fn+9OyCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T00:55:13.258724Z","bundle_sha256":"e725fd49d6357f2edd6e8ab7f32a16a706957a88571d4fef9c7d6dcd868d4d12"}}