{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:EPDHVNUD2JSUOFZXEB7IRTS7EX","short_pith_number":"pith:EPDHVNUD","canonical_record":{"source":{"id":"2405.03963","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-05-07T02:49:59Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"29b43978aa5e6c9a79780744bb666c31498bc6ded8cfb55909ce0b7a53942047","abstract_canon_sha256":"f3fdfbd51ca46a6b4fd289a69c7053994f0734943b5c398535cb3d8f0015a4d4"},"schema_version":"1.0"},"canonical_sha256":"23c67ab683d265471737207e88ce5f25d5392be46d97662de2e7b785020950e4","source":{"kind":"arxiv","id":"2405.03963","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.03963","created_at":"2026-07-05T09:36:11Z"},{"alias_kind":"arxiv_version","alias_value":"2405.03963v4","created_at":"2026-07-05T09:36:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.03963","created_at":"2026-07-05T09:36:11Z"},{"alias_kind":"pith_short_12","alias_value":"EPDHVNUD2JSU","created_at":"2026-07-05T09:36:11Z"},{"alias_kind":"pith_short_16","alias_value":"EPDHVNUD2JSUOFZX","created_at":"2026-07-05T09:36:11Z"},{"alias_kind":"pith_short_8","alias_value":"EPDHVNUD","created_at":"2026-07-05T09:36:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:EPDHVNUD2JSUOFZXEB7IRTS7EX","target":"record","payload":{"canonical_record":{"source":{"id":"2405.03963","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-05-07T02:49:59Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"29b43978aa5e6c9a79780744bb666c31498bc6ded8cfb55909ce0b7a53942047","abstract_canon_sha256":"f3fdfbd51ca46a6b4fd289a69c7053994f0734943b5c398535cb3d8f0015a4d4"},"schema_version":"1.0"},"canonical_sha256":"23c67ab683d265471737207e88ce5f25d5392be46d97662de2e7b785020950e4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:36:11.355132Z","signature_b64":"niIabIyD1SUH1Zx+l2tcsPfC71k8sMb2uHLfgT1sXCeCWcJlTcaNL0ggMUFDurUtMA4RW4f4BQ9YV/65gkHrDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"23c67ab683d265471737207e88ce5f25d5392be46d97662de2e7b785020950e4","last_reissued_at":"2026-07-05T09:36:11.354635Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:36:11.354635Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.03963","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:36:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xM+QAsYU7VcO9SCrp2HNpHXoS11hEeZXJc0a8Tlo3FBM8Ik1CY4hb8l13Cay0l0G9jediTdq7G6wcVtkjQecAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T17:57:29.174757Z"},"content_sha256":"de65db1219da74c859c571da1794ef15c04f2f37ab24fc5a43796adfdd84c99f","schema_version":"1.0","event_id":"sha256:de65db1219da74c859c571da1794ef15c04f2f37ab24fc5a43796adfdd84c99f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:EPDHVNUD2JSUOFZXEB7IRTS7EX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ERATTA: Extreme RAG for Table To Answers with Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Anvar Mahammad, Arijit Mukherjee, Brian Moore, Marko Krema, Punit Prakashchandra, Sohini Roychowdhury","submitted_at":"2024-05-07T02:49:59Z","abstract_excerpt":"Large language models (LLMs) with retrieval augmented-generation (RAG) have been the optimal choice for scalable generative AI solutions in the recent past. Although RAG implemented with AI agents (agentic-RAG) has been recently popularized, its suffers from unstable cost and unreliable performances for Enterprise-level data-practices. Most existing use-cases that incorporate RAG with LLMs have been either generic or extremely domain specific, thereby questioning the scalability and generalizability of RAG-LLM approaches. In this work, we propose a unique LLM-based system where multiple LLMs c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.03963","kind":"arxiv","version":4},"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/2405.03963/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":"2026-07-05T09:36:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"p5iZKITvyBN63NFlAmJpH7WP9C0rYIcEzj82vbGfp2vAXET0JFt+sYiBxTNJMVMoq5CFAr+8DQo22OtXIJvADA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T17:57:29.175127Z"},"content_sha256":"e0e30cf6503e7dd0be71e5fed0cd23d85f527c83233ab56339342c7f690af308","schema_version":"1.0","event_id":"sha256:e0e30cf6503e7dd0be71e5fed0cd23d85f527c83233ab56339342c7f690af308"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EPDHVNUD2JSUOFZXEB7IRTS7EX/bundle.json","state_url":"https://pith.science/pith/EPDHVNUD2JSUOFZXEB7IRTS7EX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EPDHVNUD2JSUOFZXEB7IRTS7EX/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":"2026-07-06T17:57:29Z","links":{"resolver":"https://pith.science/pith/EPDHVNUD2JSUOFZXEB7IRTS7EX","bundle":"https://pith.science/pith/EPDHVNUD2JSUOFZXEB7IRTS7EX/bundle.json","state":"https://pith.science/pith/EPDHVNUD2JSUOFZXEB7IRTS7EX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EPDHVNUD2JSUOFZXEB7IRTS7EX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:EPDHVNUD2JSUOFZXEB7IRTS7EX","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":"f3fdfbd51ca46a6b4fd289a69c7053994f0734943b5c398535cb3d8f0015a4d4","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-05-07T02:49:59Z","title_canon_sha256":"29b43978aa5e6c9a79780744bb666c31498bc6ded8cfb55909ce0b7a53942047"},"schema_version":"1.0","source":{"id":"2405.03963","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.03963","created_at":"2026-07-05T09:36:11Z"},{"alias_kind":"arxiv_version","alias_value":"2405.03963v4","created_at":"2026-07-05T09:36:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.03963","created_at":"2026-07-05T09:36:11Z"},{"alias_kind":"pith_short_12","alias_value":"EPDHVNUD2JSU","created_at":"2026-07-05T09:36:11Z"},{"alias_kind":"pith_short_16","alias_value":"EPDHVNUD2JSUOFZX","created_at":"2026-07-05T09:36:11Z"},{"alias_kind":"pith_short_8","alias_value":"EPDHVNUD","created_at":"2026-07-05T09:36:11Z"}],"graph_snapshots":[{"event_id":"sha256:e0e30cf6503e7dd0be71e5fed0cd23d85f527c83233ab56339342c7f690af308","target":"graph","created_at":"2026-07-05T09:36:11Z","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/2405.03963/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) with retrieval augmented-generation (RAG) have been the optimal choice for scalable generative AI solutions in the recent past. Although RAG implemented with AI agents (agentic-RAG) has been recently popularized, its suffers from unstable cost and unreliable performances for Enterprise-level data-practices. Most existing use-cases that incorporate RAG with LLMs have been either generic or extremely domain specific, thereby questioning the scalability and generalizability of RAG-LLM approaches. In this work, we propose a unique LLM-based system where multiple LLMs c","authors_text":"Anvar Mahammad, Arijit Mukherjee, Brian Moore, Marko Krema, Punit Prakashchandra, Sohini Roychowdhury","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-05-07T02:49:59Z","title":"ERATTA: Extreme RAG for Table To Answers with Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.03963","kind":"arxiv","version":4},"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:de65db1219da74c859c571da1794ef15c04f2f37ab24fc5a43796adfdd84c99f","target":"record","created_at":"2026-07-05T09:36:11Z","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":"f3fdfbd51ca46a6b4fd289a69c7053994f0734943b5c398535cb3d8f0015a4d4","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-05-07T02:49:59Z","title_canon_sha256":"29b43978aa5e6c9a79780744bb666c31498bc6ded8cfb55909ce0b7a53942047"},"schema_version":"1.0","source":{"id":"2405.03963","kind":"arxiv","version":4}},"canonical_sha256":"23c67ab683d265471737207e88ce5f25d5392be46d97662de2e7b785020950e4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"23c67ab683d265471737207e88ce5f25d5392be46d97662de2e7b785020950e4","first_computed_at":"2026-07-05T09:36:11.354635Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:36:11.354635Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"niIabIyD1SUH1Zx+l2tcsPfC71k8sMb2uHLfgT1sXCeCWcJlTcaNL0ggMUFDurUtMA4RW4f4BQ9YV/65gkHrDw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:36:11.355132Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.03963","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:de65db1219da74c859c571da1794ef15c04f2f37ab24fc5a43796adfdd84c99f","sha256:e0e30cf6503e7dd0be71e5fed0cd23d85f527c83233ab56339342c7f690af308"],"state_sha256":"3e3efce53eb37a58312617615bf77a91b025cefa523652d264fc794afb7744c6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2JXGkmRLfbKRN6nAF0eEsZGw9XzXEzp40HnmDXwjB8nZM4onsLHuh0W6yiEXJ9w3qDvZEcPAALnXXpYhb1tHCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T17:57:29.177132Z","bundle_sha256":"5107730047715152eaed3267c654c90b98842f4347ea64a1804b457dff083106"}}