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Paper Citation Record · LEDGER

Using LLM to select the right SQL Query from candidates

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2401.02115.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2401.02115 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:13:50.115582Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-22T12:41:33.456655Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d148382c-1d54-40ea-a187-a6f4d78989a5 · inbound

Needle: A Generative AI-Powered Multi-modal Database for Answering Complex Natural Language Queries cites this paper.

Needle: A Generative AI-Powered Multi-modal Database for Answering Complex Natural Language Queries Using LLM to select the right SQL Query from candidates

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-12T05:13:50.115582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T05:13:50.115582Z digest=sha256:87eaefd597eb4be401b641119d54fb90e6216cf5481bfa8378d78ee1981cdd7a

Observation e63fac82-c76c-488b-8b28-780e46ac0cc5 · inbound

Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities cites this paper.

Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities Using LLM to select the right SQL Query from candidates

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-07T13:12:38.877153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:12:38.877153Z digest=sha256:9e1f514c3cc78d60016b25c8653d8b9071e29ae5052b5657080fdcd6065040eb

Observation 862c3349-58d6-4dc8-a0f8-6049481873be · inbound

ScaleDoc: Scaling LLM-based Predicates over Large Document Collections cites this paper.

ScaleDoc: Scaling LLM-based Predicates over Large Document Collections Using LLM to select the right SQL Query from candidates

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-22T12:41:33.461537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-22T12:39:39.823436Z digest=sha256:7f8f948d655e07dc438a814cf5661c31501036160de9beaa9fc49a9ebae6b684

Observation a63a1541-30a7-411f-a72f-34f725c4a4f1 · inbound

Free Energy-Driven Reinforcement Learning with Adaptive Advantage Shaping for Unsupervised Reasoning in LLMs cites this paper.

Free Energy-Driven Reinforcement Learning with Adaptive Advantage Shaping for Unsupervised Reasoning in LLMs Using LLM to select the right SQL Query from candidates

Reference 214

Resolution
verified exact
arxiv_id, observed 2026-05-11T07:45:59.467796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-05-10T16:58:10.013475Z digest=sha256:b754faab7bfdefc23e6c1565bdeee94e01cbc2afdfd6c7ff5cf9a499a9a83be8

Observation 5f124b19-da56-404e-916e-0e1a0aadc8f6 · inbound

Adapt to Thrive! Adaptive Power-Mean Policy Optimization for Improved LLM Reasoning cites this paper.

Adapt to Thrive! Adaptive Power-Mean Policy Optimization for Improved LLM Reasoning Using LLM to select the right SQL Query from candidates

Reference 199

Resolution
verified exact
arxiv_id, observed 2026-05-11T08:01:00.644346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-05-10T16:51:19.555272Z digest=sha256:ae0cb2d81fa2c14bc759f7b7316e361eed49ffaa6cfcbf57f298a43f5759c11d