{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:LGHMM3QFKQZR3YI5G5MAOYYEHP","short_pith_number":"pith:LGHMM3QF","canonical_record":{"source":{"id":"2412.08385","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-11T13:50:17Z","cross_cats_sorted":["cs.AI","cs.IR","cs.LG"],"title_canon_sha256":"8ca9f49a79a2e11deadc511f2670efb544d57441e694b22210480d9e13847517","abstract_canon_sha256":"5872439ed2d05bc81f85b1044570c045acc93d3c2117ad43f7daca50fdd35b3e"},"schema_version":"1.0"},"canonical_sha256":"598ec66e0554331de11d37580763043bf3a175133eb917b244a688b99dd9cec1","source":{"kind":"arxiv","id":"2412.08385","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.08385","created_at":"2026-07-05T09:47:49Z"},{"alias_kind":"arxiv_version","alias_value":"2412.08385v1","created_at":"2026-07-05T09:47:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.08385","created_at":"2026-07-05T09:47:49Z"},{"alias_kind":"pith_short_12","alias_value":"LGHMM3QFKQZR","created_at":"2026-07-05T09:47:49Z"},{"alias_kind":"pith_short_16","alias_value":"LGHMM3QFKQZR3YI5","created_at":"2026-07-05T09:47:49Z"},{"alias_kind":"pith_short_8","alias_value":"LGHMM3QF","created_at":"2026-07-05T09:47:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:LGHMM3QFKQZR3YI5G5MAOYYEHP","target":"record","payload":{"canonical_record":{"source":{"id":"2412.08385","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-11T13:50:17Z","cross_cats_sorted":["cs.AI","cs.IR","cs.LG"],"title_canon_sha256":"8ca9f49a79a2e11deadc511f2670efb544d57441e694b22210480d9e13847517","abstract_canon_sha256":"5872439ed2d05bc81f85b1044570c045acc93d3c2117ad43f7daca50fdd35b3e"},"schema_version":"1.0"},"canonical_sha256":"598ec66e0554331de11d37580763043bf3a175133eb917b244a688b99dd9cec1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:47:49.172119Z","signature_b64":"ZkYCMrAyhEi/Tfxg4v3hz29lhIH7W/u+oWbGkAwNlcBUmV61spD0cMyXQ22PTDE4SOt++MNb4KHNDvpeINfvBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"598ec66e0554331de11d37580763043bf3a175133eb917b244a688b99dd9cec1","last_reissued_at":"2026-07-05T09:47:49.171716Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:47:49.171716Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.08385","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":"2026-07-05T09:47:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zPgunlE5cWVwv2rlvy3NRvNcyVU68HKLycJig3oHAIUpteZaNNgIUFMli6joi4ywQkWACv0VnU80JLrnS+kHBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T14:03:45.053059Z"},"content_sha256":"160ae39f2dba4787aeada53f6114001a43ce2db614231b3295a8643997236872","schema_version":"1.0","event_id":"sha256:160ae39f2dba4787aeada53f6114001a43ce2db614231b3295a8643997236872"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:LGHMM3QFKQZR3YI5G5MAOYYEHP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"NyayaAnumana & INLegalLlama: The Largest Indian Legal Judgment Prediction Dataset and Specialized Language Model for Enhanced Decision Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.IR","cs.LG"],"primary_cat":"cs.CL","authors_text":"Arnab Bhattacharya, Balaramamahanthi Deepak Patnaik, Kripabandhu Ghosh, Noel Shallum, Shivam Mishra, Shubham Kumar Nigam","submitted_at":"2024-12-11T13:50:17Z","abstract_excerpt":"The integration of artificial intelligence (AI) in legal judgment prediction (LJP) has the potential to transform the legal landscape, particularly in jurisdictions like India, where a significant backlog of cases burdens the legal system. This paper introduces NyayaAnumana, the largest and most diverse corpus of Indian legal cases compiled for LJP, encompassing a total of 7,02,945 preprocessed cases. NyayaAnumana, which combines the words \"Nyay\" (judgment) and \"Anuman\" (prediction or inference) respectively for most major Indian languages, includes a wide range of cases from the Supreme Court"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.08385","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/2412.08385/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":"2026-07-05T09:47:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dcgltpiQnHvwqxH1sfJrkjAdYlwEJbOAAsMEW3NE3uYBglZcToD4PXcCq3lkipcoVNmzZjDNi1judlx6bM/3Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T14:03:45.053680Z"},"content_sha256":"0659782f4a13627f3c0ba792724563b2111c1646b0c69d363679fd0c282b92d4","schema_version":"1.0","event_id":"sha256:0659782f4a13627f3c0ba792724563b2111c1646b0c69d363679fd0c282b92d4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LGHMM3QFKQZR3YI5G5MAOYYEHP/bundle.json","state_url":"https://pith.science/pith/LGHMM3QFKQZR3YI5G5MAOYYEHP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LGHMM3QFKQZR3YI5G5MAOYYEHP/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":"2026-08-12T14:03:45Z","links":{"resolver":"https://pith.science/pith/LGHMM3QFKQZR3YI5G5MAOYYEHP","bundle":"https://pith.science/pith/LGHMM3QFKQZR3YI5G5MAOYYEHP/bundle.json","state":"https://pith.science/pith/LGHMM3QFKQZR3YI5G5MAOYYEHP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LGHMM3QFKQZR3YI5G5MAOYYEHP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:LGHMM3QFKQZR3YI5G5MAOYYEHP","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":"5872439ed2d05bc81f85b1044570c045acc93d3c2117ad43f7daca50fdd35b3e","cross_cats_sorted":["cs.AI","cs.IR","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-11T13:50:17Z","title_canon_sha256":"8ca9f49a79a2e11deadc511f2670efb544d57441e694b22210480d9e13847517"},"schema_version":"1.0","source":{"id":"2412.08385","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.08385","created_at":"2026-07-05T09:47:49Z"},{"alias_kind":"arxiv_version","alias_value":"2412.08385v1","created_at":"2026-07-05T09:47:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.08385","created_at":"2026-07-05T09:47:49Z"},{"alias_kind":"pith_short_12","alias_value":"LGHMM3QFKQZR","created_at":"2026-07-05T09:47:49Z"},{"alias_kind":"pith_short_16","alias_value":"LGHMM3QFKQZR3YI5","created_at":"2026-07-05T09:47:49Z"},{"alias_kind":"pith_short_8","alias_value":"LGHMM3QF","created_at":"2026-07-05T09:47:49Z"}],"graph_snapshots":[{"event_id":"sha256:0659782f4a13627f3c0ba792724563b2111c1646b0c69d363679fd0c282b92d4","target":"graph","created_at":"2026-07-05T09:47:49Z","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/2412.08385/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The integration of artificial intelligence (AI) in legal judgment prediction (LJP) has the potential to transform the legal landscape, particularly in jurisdictions like India, where a significant backlog of cases burdens the legal system. This paper introduces NyayaAnumana, the largest and most diverse corpus of Indian legal cases compiled for LJP, encompassing a total of 7,02,945 preprocessed cases. NyayaAnumana, which combines the words \"Nyay\" (judgment) and \"Anuman\" (prediction or inference) respectively for most major Indian languages, includes a wide range of cases from the Supreme Court","authors_text":"Arnab Bhattacharya, Balaramamahanthi Deepak Patnaik, Kripabandhu Ghosh, Noel Shallum, Shivam Mishra, Shubham Kumar Nigam","cross_cats":["cs.AI","cs.IR","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-11T13:50:17Z","title":"NyayaAnumana & INLegalLlama: The Largest Indian Legal Judgment Prediction Dataset and Specialized Language Model for Enhanced Decision Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.08385","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":"sha256:160ae39f2dba4787aeada53f6114001a43ce2db614231b3295a8643997236872","target":"record","created_at":"2026-07-05T09:47:49Z","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":"5872439ed2d05bc81f85b1044570c045acc93d3c2117ad43f7daca50fdd35b3e","cross_cats_sorted":["cs.AI","cs.IR","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-11T13:50:17Z","title_canon_sha256":"8ca9f49a79a2e11deadc511f2670efb544d57441e694b22210480d9e13847517"},"schema_version":"1.0","source":{"id":"2412.08385","kind":"arxiv","version":1}},"canonical_sha256":"598ec66e0554331de11d37580763043bf3a175133eb917b244a688b99dd9cec1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"598ec66e0554331de11d37580763043bf3a175133eb917b244a688b99dd9cec1","first_computed_at":"2026-07-05T09:47:49.171716Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:47:49.171716Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZkYCMrAyhEi/Tfxg4v3hz29lhIH7W/u+oWbGkAwNlcBUmV61spD0cMyXQ22PTDE4SOt++MNb4KHNDvpeINfvBw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:47:49.172119Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.08385","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:160ae39f2dba4787aeada53f6114001a43ce2db614231b3295a8643997236872","sha256:0659782f4a13627f3c0ba792724563b2111c1646b0c69d363679fd0c282b92d4"],"state_sha256":"82c3521aa5a3cf941e881a7144955ad8212f9f033bcec7038520b119bb683d09"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"w3ur7VVO7zaGf02EtbPCoPPEchIRzhYtTQKECSqnPXBYOIBORc9UzR90+YXC1usgMmEn+1vyrQgknL3KqgJSBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T14:03:45.058396Z","bundle_sha256":"972f9e793f7c56876da833966d0701a1c82ccfa9dea1f98b79545c392466539e"}}