{"as_of":"2026-08-09T18:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:dd2de995664dde8e6dc6ae73ba976dc43172ca2582a5698c1150fe4dfd565e6c","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:55:59.983378Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-19T06:52:59.970490Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.18406","last_updated":"2024-10-24T03:42:43Z","snapshot_observed_at":"2026-07-06T19:38:52.603055Z","submitted_at":"2024-10-24T03:42:43Z","title":"MoMQ: Mixture-of-Experts Enhances Multi-Dialect Query Generation across Relational and Non-Relational Databases","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.18406","snapshot_observed_at":"2026-08-07T14:55:59.983378Z","title":"Momq: Mixture-of-experts enhances multi-dialect query generation across relational and non-relational databases","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.17231","last_updated":"2025-05-22T19:13:34Z","snapshot_observed_at":"2026-08-09T02:15:37.607050Z","submitted_at":"2025-05-22T19:13:34Z","title":"ExeSQL: Self-Taught Text-to-SQL Models with Execution-Driven Bootstrapping for SQL Dialects","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-07T14:55:59.983378Z"},"links":{"cited_paper":"/paper/2410.18406","citing_paper":"/paper/2505.17231"},"observation_digest":"sha256:e27620bb0dd27d47c786727ba0456b3396da01a56c9500f31ef950603a1a521b","observation_id":"a006dae2-ec72-46e6-9ef8-14864aa4c542","resolution":{"observed_at":"2026-08-07T14:55:59.983378Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.18406","last_updated":"2024-10-24T03:42:43Z","snapshot_observed_at":"2026-07-06T19:38:52.603055Z","submitted_at":"2024-10-24T03:42:43Z","title":"MoMQ: Mixture-of-Experts Enhances Multi-Dialect Query Generation across Relational and Non-Relational Databases","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.18406","snapshot_observed_at":"2026-08-07T13:12:39.077590Z","title":"Momq: Mixture-of-experts enhances multi-dialect query generation across relational and non-relational databases","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23838","last_updated":"2025-05-28T13:23:38Z","snapshot_observed_at":"2026-08-09T04:39:49.673472Z","submitted_at":"2025-05-28T13:23:38Z","title":"Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:39.077590Z"},"links":{"cited_paper":"/paper/2410.18406","citing_paper":"/paper/2505.23838"},"observation_digest":"sha256:908267bcf49c1223ae725a37881449d40226e73d90dd1648c47d02bc72080410","observation_id":"9e601d1d-31ec-4c4e-85f7-65e2c0742a92","resolution":{"observed_at":"2026-08-07T13:12:39.077590Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.18406","last_updated":"2024-10-24T03:42:43Z","snapshot_observed_at":"2026-07-06T19:38:52.603055Z","submitted_at":"2024-10-24T03:42:43Z","title":"MoMQ: Mixture-of-Experts Enhances Multi-Dialect Query Generation across Relational and Non-Relational Databases","version":1},"cited_work":{"arxiv_id":"2410.18406","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.18406","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Momq: Mixture- of-experts enhances multi-dialect query generation across relational and non-relational databases","venue":null,"work_id":"3bae353b-630e-4445-b4ae-e9a9e9c142c4","year":2024},"citing_paper":{"arxiv_id":"2507.04701","last_updated":"2026-04-06T11:21:35Z","snapshot_observed_at":"2026-08-03T23:10:23.802142Z","submitted_at":"2025-07-07T06:50:46Z","title":"XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-19T06:52:57.718359Z"},"links":{"cited_paper":"/paper/2410.18406","citing_paper":"/paper/2507.04701"},"observation_digest":"sha256:fdd4f6462e95c3fc1dbcc97706a5556d619fa43486f4141a77fe1d6ad85b2cf9","observation_id":"c8600ca3-979f-4317-8fbd-e38270a03159","resolution":{"observed_at":"2026-05-19T06:52:59.973167Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.18406","last_updated":"2024-10-24T03:42:43Z","snapshot_observed_at":"2026-07-06T19:38:52.603055Z","submitted_at":"2024-10-24T03:42:43Z","title":"MoMQ: Mixture-of-Experts Enhances Multi-Dialect Query Generation across Relational and Non-Relational Databases","version":1},"cited_work":{"arxiv_id":"2410.18406","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.18406","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Momq: Mixture- of-experts enhances multi-dialect query generation across relational and non-relational databases","venue":null,"work_id":"3bae353b-630e-4445-b4ae-e9a9e9c142c4","year":2024},"citing_paper":{"arxiv_id":"2605.04065","last_updated":"2026-05-07T04:49:30Z","snapshot_observed_at":"2026-07-06T23:16:54.673178Z","submitted_at":"2026-04-11T07:26:04Z","title":"Free Energy-Driven Reinforcement Learning with Adaptive Advantage Shaping for Unsupervised Reasoning in LLMs","version":2},"reference_index":232,"source":"arxiv_source","source_observed_at":"2026-05-10T16:58:10.013475Z"},"links":{"cited_paper":"/paper/2410.18406","citing_paper":"/paper/2605.04065"},"observation_digest":"sha256:8abcf0b6591fddf40719a4bf8188a36373963f4ff6bede62c436ef4aba29c08c","observation_id":"b66eafde-c040-4b22-95d2-28e7b015a873","resolution":{"observed_at":"2026-05-11T07:46:00.018344Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.18406","last_updated":"2024-10-24T03:42:43Z","snapshot_observed_at":"2026-07-06T19:38:52.603055Z","submitted_at":"2024-10-24T03:42:43Z","title":"MoMQ: Mixture-of-Experts Enhances Multi-Dialect Query Generation across Relational and Non-Relational Databases","version":1},"cited_work":{"arxiv_id":"2410.18406","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.18406","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Momq: Mixture- of-experts enhances multi-dialect query generation across relational and non-relational databases","venue":null,"work_id":"3bae353b-630e-4445-b4ae-e9a9e9c142c4","year":2024},"citing_paper":{"arxiv_id":"2605.04066","last_updated":"2026-05-07T04:57:40Z","snapshot_observed_at":"2026-08-02T15:49:26.057284Z","submitted_at":"2026-04-11T07:34:59Z","title":"Adapt to Thrive! Adaptive Power-Mean Policy Optimization for Improved LLM Reasoning","version":2},"reference_index":217,"source":"arxiv_source","source_observed_at":"2026-05-10T16:51:19.555272Z"},"links":{"cited_paper":"/paper/2410.18406","citing_paper":"/paper/2605.04066"},"observation_digest":"sha256:9f7b80e276d3383a0470353bc68e6e4dabf3ee9a8f0dfa287e1c686a9521030a","observation_id":"d0b72e87-feb6-47db-83f6-5cdbbc30db58","resolution":{"observed_at":"2026-05-11T08:01:00.590046Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2410.18406/citation-record","integrity":"/paper/2410.18406/integrity","json":"/paper/2410.18406/citation-record.json","paper":"/paper/2410.18406"},"outbound":[],"paper":{"arxiv_id":"2410.18406","last_updated":"2024-10-24T03:42:43Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T19:38:52.603055Z","submitted_at":"2024-10-24T03:42:43Z","title":"MoMQ: Mixture-of-Experts Enhances Multi-Dialect Query Generation across Relational and Non-Relational Databases"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2410.18406."}