{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:2BP5BYL65HVTENSE3LTMIBFTST","short_pith_number":"pith:2BP5BYL6","canonical_record":{"source":{"id":"2508.13107","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-18T17:14:03Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"bd257039fa5e19dd164ee0a37235c2e2427605974ad7a3da3f3b58ee08be315c","abstract_canon_sha256":"d9eecebeb411553c32c4c2bd86c1dd13ad17e26abaf4a51a3b2a17d0f24189a8"},"schema_version":"1.0"},"canonical_sha256":"d05fd0e17ee9eb323644dae6c404b394cec4a888bdaca9ea4d1e53bbf6d4c3c2","source":{"kind":"arxiv","id":"2508.13107","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.13107","created_at":"2026-07-05T12:08:06Z"},{"alias_kind":"arxiv_version","alias_value":"2508.13107v2","created_at":"2026-07-05T12:08:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.13107","created_at":"2026-07-05T12:08:06Z"},{"alias_kind":"pith_short_12","alias_value":"2BP5BYL65HVT","created_at":"2026-07-05T12:08:06Z"},{"alias_kind":"pith_short_16","alias_value":"2BP5BYL65HVTENSE","created_at":"2026-07-05T12:08:06Z"},{"alias_kind":"pith_short_8","alias_value":"2BP5BYL6","created_at":"2026-07-05T12:08:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:2BP5BYL65HVTENSE3LTMIBFTST","target":"record","payload":{"canonical_record":{"source":{"id":"2508.13107","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-18T17:14:03Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"bd257039fa5e19dd164ee0a37235c2e2427605974ad7a3da3f3b58ee08be315c","abstract_canon_sha256":"d9eecebeb411553c32c4c2bd86c1dd13ad17e26abaf4a51a3b2a17d0f24189a8"},"schema_version":"1.0"},"canonical_sha256":"d05fd0e17ee9eb323644dae6c404b394cec4a888bdaca9ea4d1e53bbf6d4c3c2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:08:06.544701Z","signature_b64":"2JC6QK649e3famig3Kf2uDt3yChy3q3/7mqH+Qb+AlH8VwCLMumZQ5muQ8znqaZBurVBmTP4OBxxlfU8glQkCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d05fd0e17ee9eb323644dae6c404b394cec4a888bdaca9ea4d1e53bbf6d4c3c2","last_reissued_at":"2026-07-05T12:08:06.543947Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:08:06.543947Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2508.13107","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T12:08:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WT/d1zUbltAuHT5/vyoirBj+mHo0gv/62iD9oJSLBTx7qwsDdfvnstrAv70/FoPCLhoodSsQlmn62FkI+ljdBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-02T17:37:47.337527Z"},"content_sha256":"d8aa7ec0f9ef73177097f4cc345f77fa4019b2498400c01e9ad60eb017d75eff","schema_version":"1.0","event_id":"sha256:d8aa7ec0f9ef73177097f4cc345f77fa4019b2498400c01e9ad60eb017d75eff"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:2BP5BYL65HVTENSE3LTMIBFTST","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"All for law and law for all: Adaptive RAG Pipeline for Legal Research","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Aravindh Manivannan, Dion Fernandes, Faisal Ahmad, Figarri Keisha, Ilham Wicaksono, Pallavi, Prince Singh, Wiem Ben Rim","submitted_at":"2025-08-18T17:14:03Z","abstract_excerpt":"Retrieval-Augmented Generation (RAG) has transformed how we approach text generation tasks by grounding Large Language Model (LLM) outputs in retrieved knowledge. This capability is especially critical in the legal domain. In this work, we introduce a novel end-to-end RAG pipeline that improves upon previous baselines using three targeted enhancements: (i) a context-aware query translator that disentangles document references from natural-language questions and adapts retrieval depth and response style based on expertise and specificity, (ii) open-source retrieval strategies using SBERT and GT"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.13107","kind":"arxiv","version":2},"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/2508.13107/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":"2026-07-05T12:08:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NK+nyo8DokbevPEUg9LORvx1BksveQ/mqBvwEwIsMJmhuZbrtSw02gCKx+V+hGv86eACbBlgxTmc4YWWXGVrAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-02T17:37:47.338120Z"},"content_sha256":"7fb2ffcf97e23875e688f08b3bdb834cb3eb3854b4cb6273917313c6bdfcde22","schema_version":"1.0","event_id":"sha256:7fb2ffcf97e23875e688f08b3bdb834cb3eb3854b4cb6273917313c6bdfcde22"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2BP5BYL65HVTENSE3LTMIBFTST/bundle.json","state_url":"https://pith.science/pith/2BP5BYL65HVTENSE3LTMIBFTST/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2BP5BYL65HVTENSE3LTMIBFTST/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":"2026-08-02T17:37:47Z","links":{"resolver":"https://pith.science/pith/2BP5BYL65HVTENSE3LTMIBFTST","bundle":"https://pith.science/pith/2BP5BYL65HVTENSE3LTMIBFTST/bundle.json","state":"https://pith.science/pith/2BP5BYL65HVTENSE3LTMIBFTST/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2BP5BYL65HVTENSE3LTMIBFTST/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:2BP5BYL65HVTENSE3LTMIBFTST","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":"d9eecebeb411553c32c4c2bd86c1dd13ad17e26abaf4a51a3b2a17d0f24189a8","cross_cats_sorted":["cs.IR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-18T17:14:03Z","title_canon_sha256":"bd257039fa5e19dd164ee0a37235c2e2427605974ad7a3da3f3b58ee08be315c"},"schema_version":"1.0","source":{"id":"2508.13107","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.13107","created_at":"2026-07-05T12:08:06Z"},{"alias_kind":"arxiv_version","alias_value":"2508.13107v2","created_at":"2026-07-05T12:08:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.13107","created_at":"2026-07-05T12:08:06Z"},{"alias_kind":"pith_short_12","alias_value":"2BP5BYL65HVT","created_at":"2026-07-05T12:08:06Z"},{"alias_kind":"pith_short_16","alias_value":"2BP5BYL65HVTENSE","created_at":"2026-07-05T12:08:06Z"},{"alias_kind":"pith_short_8","alias_value":"2BP5BYL6","created_at":"2026-07-05T12:08:06Z"}],"graph_snapshots":[{"event_id":"sha256:7fb2ffcf97e23875e688f08b3bdb834cb3eb3854b4cb6273917313c6bdfcde22","target":"graph","created_at":"2026-07-05T12:08:06Z","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/2508.13107/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Retrieval-Augmented Generation (RAG) has transformed how we approach text generation tasks by grounding Large Language Model (LLM) outputs in retrieved knowledge. This capability is especially critical in the legal domain. In this work, we introduce a novel end-to-end RAG pipeline that improves upon previous baselines using three targeted enhancements: (i) a context-aware query translator that disentangles document references from natural-language questions and adapts retrieval depth and response style based on expertise and specificity, (ii) open-source retrieval strategies using SBERT and GT","authors_text":"Aravindh Manivannan, Dion Fernandes, Faisal Ahmad, Figarri Keisha, Ilham Wicaksono, Pallavi, Prince Singh, Wiem Ben Rim","cross_cats":["cs.IR"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-18T17:14:03Z","title":"All for law and law for all: Adaptive RAG Pipeline for Legal Research"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.13107","kind":"arxiv","version":2},"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:d8aa7ec0f9ef73177097f4cc345f77fa4019b2498400c01e9ad60eb017d75eff","target":"record","created_at":"2026-07-05T12:08:06Z","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":"d9eecebeb411553c32c4c2bd86c1dd13ad17e26abaf4a51a3b2a17d0f24189a8","cross_cats_sorted":["cs.IR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-18T17:14:03Z","title_canon_sha256":"bd257039fa5e19dd164ee0a37235c2e2427605974ad7a3da3f3b58ee08be315c"},"schema_version":"1.0","source":{"id":"2508.13107","kind":"arxiv","version":2}},"canonical_sha256":"d05fd0e17ee9eb323644dae6c404b394cec4a888bdaca9ea4d1e53bbf6d4c3c2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d05fd0e17ee9eb323644dae6c404b394cec4a888bdaca9ea4d1e53bbf6d4c3c2","first_computed_at":"2026-07-05T12:08:06.543947Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:08:06.543947Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2JC6QK649e3famig3Kf2uDt3yChy3q3/7mqH+Qb+AlH8VwCLMumZQ5muQ8znqaZBurVBmTP4OBxxlfU8glQkCg==","signature_status":"signed_v1","signed_at":"2026-07-05T12:08:06.544701Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.13107","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d8aa7ec0f9ef73177097f4cc345f77fa4019b2498400c01e9ad60eb017d75eff","sha256:7fb2ffcf97e23875e688f08b3bdb834cb3eb3854b4cb6273917313c6bdfcde22"],"state_sha256":"e46350504830a4f3f338232d9946a2343ea4d2e4d48e63761441c81d651ab79c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"na3MgSpdnLW1eFqj5EX7l1/vqHVCnHmmlR9gv/0EXOVs9krYdMb/RtU8PDzOVAMMN9YelZBJbeeSEpe/WqNADA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-02T17:37:47.342253Z","bundle_sha256":"64ed4e6ba34d3de211a1a5dfec408b7187ae803dc2608e15a4c0461c3dce2921"}}