{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:E2BFLFYJZQ6EOKMAAM7AVAVXKC","short_pith_number":"pith:E2BFLFYJ","canonical_record":{"source":{"id":"2406.03600","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-05T19:47:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5be5344224eb27bbfc9ed0b0b1b9524465caaf06a841a424c73eaf038af9106e","abstract_canon_sha256":"d127af8e6d1e36f82ff83946a8580ca2ae62c8d3dc0addad91c3a0f018925337"},"schema_version":"1.0"},"canonical_sha256":"2682559709cc3c472980033e0a82b750890fe0cc92f6762db919eab142a3c24b","source":{"kind":"arxiv","id":"2406.03600","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.03600","created_at":"2026-07-05T08:28:13Z"},{"alias_kind":"arxiv_version","alias_value":"2406.03600v1","created_at":"2026-07-05T08:28:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.03600","created_at":"2026-07-05T08:28:13Z"},{"alias_kind":"pith_short_12","alias_value":"E2BFLFYJZQ6E","created_at":"2026-07-05T08:28:13Z"},{"alias_kind":"pith_short_16","alias_value":"E2BFLFYJZQ6EOKMA","created_at":"2026-07-05T08:28:13Z"},{"alias_kind":"pith_short_8","alias_value":"E2BFLFYJ","created_at":"2026-07-05T08:28:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:E2BFLFYJZQ6EOKMAAM7AVAVXKC","target":"record","payload":{"canonical_record":{"source":{"id":"2406.03600","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-05T19:47:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5be5344224eb27bbfc9ed0b0b1b9524465caaf06a841a424c73eaf038af9106e","abstract_canon_sha256":"d127af8e6d1e36f82ff83946a8580ca2ae62c8d3dc0addad91c3a0f018925337"},"schema_version":"1.0"},"canonical_sha256":"2682559709cc3c472980033e0a82b750890fe0cc92f6762db919eab142a3c24b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:28:13.485217Z","signature_b64":"cbOx8Mx9yQ265LoiQfOJ0rUC/J7m362f0xph4fIhAf1G8VjhNNs/p7ayrtl5hMUxAhnDsZ+LsNP9YiwfkQXwDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2682559709cc3c472980033e0a82b750890fe0cc92f6762db919eab142a3c24b","last_reissued_at":"2026-07-05T08:28:13.484882Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:28:13.484882Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2406.03600","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":"2026-07-05T08:28:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KR63r4YQYmAVF/6gAud0QgZ+62/WiemgYRyWYPQrrJrt9x+ruA7e6HvN8zITIae1KP4/+NiGPH6p8i26K9Y1Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-17T18:14:38.088657Z"},"content_sha256":"9606f00410f904fcd9a3ac4e13f49810cade80f4711b38a1cba3ec96a163b18c","schema_version":"1.0","event_id":"sha256:9606f00410f904fcd9a3ac4e13f49810cade80f4711b38a1cba3ec96a163b18c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:E2BFLFYJZQ6EOKMAAM7AVAVXKC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Knowledge-Infused Legal Wisdom: Navigating LLM Consultation through the Lens of Diagnostics and Positive-Unlabeled Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Chenghao Wang, Ece Gumusel, Xiaozhong Liu, Yang Wu","submitted_at":"2024-06-05T19:47:35Z","abstract_excerpt":"The integration of generative Large Language Models (LLMs) into various applications, including the legal domain, has been accelerated by their expansive and versatile nature. However, when facing a legal case, users without a legal background often struggle to formulate professional queries and may inadvertently overlook critical legal factors when presenting their case narrative to LLMs. To address this issue, we propose the Diagnostic Legal Large Language Model (D3LM), which utilizes adaptive lawyer-like diagnostic questions to collect additional case information and then provides high-qual"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.03600","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/2406.03600/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":"2026-07-05T08:28:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dwcA/IUiZqdZBQ2/VNZQMemYwPt+6k8gaN3NH/uPxLZcxudvThY2iGtv9uG7/60FxK6GF17FR8sADse0ytcODg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-17T18:14:38.089029Z"},"content_sha256":"3e7da3a1dc5ae29c8d07a4009cc38cd38094879690ee7af146314200223886ff","schema_version":"1.0","event_id":"sha256:3e7da3a1dc5ae29c8d07a4009cc38cd38094879690ee7af146314200223886ff"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/E2BFLFYJZQ6EOKMAAM7AVAVXKC/bundle.json","state_url":"https://pith.science/pith/E2BFLFYJZQ6EOKMAAM7AVAVXKC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/E2BFLFYJZQ6EOKMAAM7AVAVXKC/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":"2026-07-17T18:14:38Z","links":{"resolver":"https://pith.science/pith/E2BFLFYJZQ6EOKMAAM7AVAVXKC","bundle":"https://pith.science/pith/E2BFLFYJZQ6EOKMAAM7AVAVXKC/bundle.json","state":"https://pith.science/pith/E2BFLFYJZQ6EOKMAAM7AVAVXKC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/E2BFLFYJZQ6EOKMAAM7AVAVXKC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:E2BFLFYJZQ6EOKMAAM7AVAVXKC","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":"d127af8e6d1e36f82ff83946a8580ca2ae62c8d3dc0addad91c3a0f018925337","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-05T19:47:35Z","title_canon_sha256":"5be5344224eb27bbfc9ed0b0b1b9524465caaf06a841a424c73eaf038af9106e"},"schema_version":"1.0","source":{"id":"2406.03600","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.03600","created_at":"2026-07-05T08:28:13Z"},{"alias_kind":"arxiv_version","alias_value":"2406.03600v1","created_at":"2026-07-05T08:28:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.03600","created_at":"2026-07-05T08:28:13Z"},{"alias_kind":"pith_short_12","alias_value":"E2BFLFYJZQ6E","created_at":"2026-07-05T08:28:13Z"},{"alias_kind":"pith_short_16","alias_value":"E2BFLFYJZQ6EOKMA","created_at":"2026-07-05T08:28:13Z"},{"alias_kind":"pith_short_8","alias_value":"E2BFLFYJ","created_at":"2026-07-05T08:28:13Z"}],"graph_snapshots":[{"event_id":"sha256:3e7da3a1dc5ae29c8d07a4009cc38cd38094879690ee7af146314200223886ff","target":"graph","created_at":"2026-07-05T08:28:13Z","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/2406.03600/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The integration of generative Large Language Models (LLMs) into various applications, including the legal domain, has been accelerated by their expansive and versatile nature. However, when facing a legal case, users without a legal background often struggle to formulate professional queries and may inadvertently overlook critical legal factors when presenting their case narrative to LLMs. To address this issue, we propose the Diagnostic Legal Large Language Model (D3LM), which utilizes adaptive lawyer-like diagnostic questions to collect additional case information and then provides high-qual","authors_text":"Chenghao Wang, Ece Gumusel, Xiaozhong Liu, Yang Wu","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-05T19:47:35Z","title":"Knowledge-Infused Legal Wisdom: Navigating LLM Consultation through the Lens of Diagnostics and Positive-Unlabeled Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.03600","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":"sha256:9606f00410f904fcd9a3ac4e13f49810cade80f4711b38a1cba3ec96a163b18c","target":"record","created_at":"2026-07-05T08:28:13Z","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":"d127af8e6d1e36f82ff83946a8580ca2ae62c8d3dc0addad91c3a0f018925337","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-05T19:47:35Z","title_canon_sha256":"5be5344224eb27bbfc9ed0b0b1b9524465caaf06a841a424c73eaf038af9106e"},"schema_version":"1.0","source":{"id":"2406.03600","kind":"arxiv","version":1}},"canonical_sha256":"2682559709cc3c472980033e0a82b750890fe0cc92f6762db919eab142a3c24b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2682559709cc3c472980033e0a82b750890fe0cc92f6762db919eab142a3c24b","first_computed_at":"2026-07-05T08:28:13.484882Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:28:13.484882Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cbOx8Mx9yQ265LoiQfOJ0rUC/J7m362f0xph4fIhAf1G8VjhNNs/p7ayrtl5hMUxAhnDsZ+LsNP9YiwfkQXwDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:28:13.485217Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.03600","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9606f00410f904fcd9a3ac4e13f49810cade80f4711b38a1cba3ec96a163b18c","sha256:3e7da3a1dc5ae29c8d07a4009cc38cd38094879690ee7af146314200223886ff"],"state_sha256":"477aa5eec1b9b894456721c50780eb46fe24172200997e48d21aa9153e7b80dd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gK2/fy2tDmJecn487XKPztP+hscuEpZiA8Mk2zt2LEpy9cxLVkiHW9lWN8g0I7wGrN8PuZH9zZ0Iq0Y0mQkIDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-17T18:14:38.091520Z","bundle_sha256":"3fbab20ee7f52f2c235ea42e7c5bbed14383220583b5c663b236b58f2622d828"}}