{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:CY5DYA7URB67LNIREAFIXIH7GV","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":"8afdb586515c0a6319a2d09d2d678c314dd93e4b9fb723457f71124334a79ff6","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2025-06-28T20:24:34Z","title_canon_sha256":"d97f3ca042de8d76b4219a48ce84bbeea37d915ee77b8fec37d316f06e30ec35"},"schema_version":"1.0","source":{"id":"2507.01058","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.01058","created_at":"2026-07-05T11:30:24Z"},{"alias_kind":"arxiv_version","alias_value":"2507.01058v1","created_at":"2026-07-05T11:30:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.01058","created_at":"2026-07-05T11:30:24Z"},{"alias_kind":"pith_short_12","alias_value":"CY5DYA7URB67","created_at":"2026-07-05T11:30:24Z"},{"alias_kind":"pith_short_16","alias_value":"CY5DYA7URB67LNIR","created_at":"2026-07-05T11:30:24Z"},{"alias_kind":"pith_short_8","alias_value":"CY5DYA7U","created_at":"2026-07-05T11:30:24Z"}],"graph_snapshots":[{"event_id":"sha256:5d15451bad7165ca4ad0c331688e1eb597e0280a3be0dc0856b9b2689543d6a5","target":"graph","created_at":"2026-07-05T11:30:24Z","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/2507.01058/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The judiciary, as one of democracy's three pillars, is dealing with a rising amount of legal issues, needing careful use of judicial resources. This research presents a complex framework that leverages Data Science methodologies, notably Large Language Models (LLM) and Retrieval-Augmented Generation (RAG) techniques, to improve the efficiency of analyzing Calcutta High Court verdicts. Our framework focuses on two key aspects: first, the creation of a robust summarization mechanism that distills complex legal texts into concise and coherent summaries; and second, the development of an intellige","authors_text":"Amlan Chakrabarti, Aritra Mazumdar, Puspendu Banerjee, Saptarsi Goswami, Wazib Ansar","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2025-06-28T20:24:34Z","title":"A Data Science Approach to Calcutta High Court Judgments: An Efficient LLM and RAG-powered Framework for Summarization and Similar Cases Retrieval"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.01058","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":"sha256:6eb3f7719d31b3978d56bfc14d86d70882c2fde70de6f5b1d4d93fcd845b285a","target":"record","created_at":"2026-07-05T11:30:24Z","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":"8afdb586515c0a6319a2d09d2d678c314dd93e4b9fb723457f71124334a79ff6","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2025-06-28T20:24:34Z","title_canon_sha256":"d97f3ca042de8d76b4219a48ce84bbeea37d915ee77b8fec37d316f06e30ec35"},"schema_version":"1.0","source":{"id":"2507.01058","kind":"arxiv","version":1}},"canonical_sha256":"163a3c03f4887df5b511200a8ba0ff3567949c16a6a6fba5dce2a873fd741bbf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"163a3c03f4887df5b511200a8ba0ff3567949c16a6a6fba5dce2a873fd741bbf","first_computed_at":"2026-07-05T11:30:24.477322Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:30:24.477322Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fMt44wglUV7b/7xxS6kSG+oN+XW6zxNmIPjgnxED/w0iouR5xntUxmst9m/45GcPzQLHuaki9dsrhgYPU/JWDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:30:24.477801Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.01058","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6eb3f7719d31b3978d56bfc14d86d70882c2fde70de6f5b1d4d93fcd845b285a","sha256:5d15451bad7165ca4ad0c331688e1eb597e0280a3be0dc0856b9b2689543d6a5"],"state_sha256":"9bcf3f91960eba38e5548bea1c0b1e933824b6306a1d3dbe11f8640579699ecd"}